Kimon Keramidas
email: kimon.keramidas@univ-grenoble-alpes.fr

Silvana Mima

Adrien Bidaud

Opportunities and roadblocks in the decarbonisation of the global steel sector: a demand and production modelling approach

The steel sector represents a growing share of global carbon dioxide (CO2) emissions and is perceived as a hard-toabate sector in the drive towards economy-wide decarbonisation. We present a model detailing steel demand and multiple steel production pathways within a larger global multi-regional energy system simulation model, projecting material, energy and emissions flows to 2100. We examine decarbonisation levels and options under different assumptions on climate policy, technologies and steel demand patterns, and study low-carbon options in the production of hydrogen as a steel decarbonisation vector. Global steel demand increases at a decelerated pace compared to the past two decades (+65% in 2050 compared to 2020), driven by substantial increases in the underlying socio-economic conditions. Climate policies lead to a limited positive feedback effect on steel demand (+21% in 2050) due a faster equipment turnover and higher electrification, which could be overcompensated by energy saving and material efficiency measures. Increased recycling and strong electrification (up to 63% of production in 2050) are projected as key levers towards decreasing emissions, made possible thanks to the increasing availability of steel scrap. Strong climate policies would be needed to push the steel sector to decarbonize fully, with electrification, carbon capture, biomass and hydrogen all contributing. Carbon capture would be necessary to reach net-zero emissions in the second half of the century.

INTRODUCTION

With nearly all states having signed the United Nations international climate treaty of the Paris Agreement since 2016, collective efforts are needed to "holding the increase in the global average temperature to well below 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels" [START_REF] Bidaud | Adoption of the Paris Agreement[END_REF]. According to literature synthesized by the Intergovernmental Panel for Climate Change's (IPCC) Special Report on 1.5°C [START_REF]Global Warming of 1.5°C[END_REF], there is high agreement among the scientific community that global anthropogenic emissions of carbon dioxide (CO2) need to be reduced to net-zero by around mid-century in order to be consistent with the more ambitious Paris Agreement target. Accordingly, significant efforts to reduce emissions have to be pursued by all world countries and across all sectors of the economy. However, countries and sectors would differentiate their contribution in the overall reduction depending on cost-efficiency and other considerations. Thus, a net-zero global target does not necessarily coincide with a similar target for all sectors. In particular, heavy industry has been identified as a challenging sector to decarbonize, along with aviation, shipping and agriculture [START_REF] Luderer | Residual fossil CO2 emissions in 1.5-2 °C pathways[END_REF]; this is due to a combination of sector-specific factors: high projected activity growth, comparatively costlier emissions abatement options and slow capital turnover.

The iron and steel industry is responsible for a sizeable share of the world's CO2 emissions: around 7.4% of energy and industry emissions in 2021 with 2.70 GtCO2; the volume and share of these emissions grew rapidly over 2000-2015 (from 1.33 and 5.5% to 2.76 GtCO2 and 8.0%, i.e. +5.0%/year) and remained relatively stable since [START_REF]Iron and Steel[END_REF]. This is mainly due to an increase in demand for steel and steel products, from 860 to 1960 Mt over 2000-2021, mainly coming from China and India, while the emissions intensity has decreased slowly (-0.6%/year over .

The main goal of this study is to chart a way forward for the iron and steel industry in a context of energy transition as required by the regional and global effort of decarbonisation to limit climate change. We propose to tackle these issues of steel demand and supply in an integrated way within an energy system model. The resulting projections of steel flows, energy needs and greenhouse gas emissions are more self-consistent.

We first provide information on the methodology to project steel demand and production; we then describe the scenarios that we assessed (section 2); we present and discuss results for steel demand and trade, for the steel production mix, emissions and investments, and for overall mitigation options (section 3); finally, we conclude on main findings and open issues (section 4).

METHODS: MODELLING WORLD STEEL DEMAND AND SUPPLY

2.1. The energy system model POLES Prospective Outlook for Long-term Energy Systems (POLES) is a global multi-regional energy system model that has been used extensively in global and regional climate policy studies [START_REF] Vandyck | A global stocktake of the Paris pledges: Implications for energy systems and economy[END_REF]- [START_REF] Bertram | Energy system developments and investments in the decisive decade for the Paris Agreement goals[END_REF]. POLES is a 66-region multi-sectoral partial equilibrium simulation model with an annual time step, with endogenously calculated energy prices (including electricity and hydrogen prices with multiple production technologies) and lagged adjustments of energy demand. Demand for energy services is derived from existing equipment and macroeconomic indicators; following equipment depreciation, choice across fuels or technologies is made with a logit distribution function using total costs and preference factors. All greenhouse gas emissions are represented.

Steel module overview

We modified the POLES model by integrating different aspects for decarbonizing steel demand and production. This is a significant evolution of a previous POLES steel module [START_REF] Hidalgo | Technological prospects and CO emission trading analyses in the iron and steel industry: A global model[END_REF], which had not been used since 2003.

The overall scheme of the materials demand and production module enriched and developed in this work is given in Figure 1. A general description is provided below, with more information in the following sections and more detail in supplementary information (SI). See SI section 7 for a comparison of key results with the previous model version. Top left: for each region, annual demand for steel is determined based on material intensity per end-use , which are associated to stocks and flows of equipment, ultimately relying on macro-economic development and living standards. For each end-use, steel scrap is released at the end of the lifetime of the associated equipment; a scrap collection rate determines quantities of scrap available for recycling.

Top right: demand is met by production at the global level, with allocation based on production and transport costs and self-sufficiency constraints. Annual production is distributed across several production processes based on their costs and existing capacities; planning for new capacities is based on anticipation of future production needs and on costs. Cost-based competition within each process determines final energy consumption by fuel; energy-and process-related emissions are then calculated.

Bottom: energy and climate policy acts on the prices for energy fuels, including secondary fuels (electricity, hydrogen). Demand for steel can change as a result of the adaptation of the energy services and of the technologies mix to the policy; regional production allocation and the processes mix can change as a result of the policy directly impacting production costs.

Steel demand

Total apparent steel consumption by region was reconstructed from domestic production [START_REF] Usgs | USGS Minerals Yearbook -Iron and Steel Statistics and Information[END_REF] and net trade [START_REF]UN Comtrade Database[END_REF].

Time series for steel demand by end-use are difficult to obtain. News reports from the industry provide the decomposition of steel demand by end-use for select regions [START_REF] Arcelormittal | Premium Review Conference[END_REF]- [START_REF] Angloamerican | The South African iron and steel value chain[END_REF]: construction ranged from 35% (EU) to 68% (China) and automotive from 8% (China) to 26% (USA). However, the sectoral decomposition differs across sources and only select years are available; therefore, a top-down decomposition of total consumption would necessitate many assumptions.

For this work, demand was estimated for certain end-uses in a bottom-up manner (specific material content per sector times an activity indicator), leaving a residual category when compared with annual apparent consumption time series. Three main demand sectors were singled out: buildings construction, road transport and power. See SI sections 4 and 5 for detail.

Buildings construction

Buildings construction (residential and commercial) is the single largest consuming sector for steel, estimated in this work at 32% of total global steel demand in 2015. Although "construction" is estimated to be the largest end-use by industry reports, it encompasses not only buildings but also other infrastructure; in this modelling, demand for buildings construction is separated from demand for other infrastructure, which is in the residual category.

A specific steel demand per floor area surface is associated with projections of new constructions of floor surfaces. Steel demand per surface were adapted from [START_REF] Marinova | Global construction materials database and stock analysis of residential buildings between 1970-2050[END_REF]. No strong correlations were found with macroeconomic indicators (GDP/capita, urbanization rate, population density); differences appear to be related to regional construction practices. They were kept constant in the projections.

Road transport

The automotive sector is one of the main consuming sectors, estimated in this work at 12% of total global steel demand in 2015, and growing strongly at 10%/year. It encompasses the production of light (private cars, vans) and heavy (trucks, busses) duty vehicles for road transport.

A specific steel demand per vehicle type from [START_REF]Steel in automotive[END_REF], [START_REF]US Department of Energy Alternative Fuels Data Center -Vehicle Weight Classes & Categories[END_REF] is associated with projections of vehicle fleets.

Power sector

This work estimates that the power sector absorbed 4% of total global steel demand in 2015. Steel demand for power generation and for the power grid were distinguished. Specific material consumption per power generation technology (kg/kW) was obtained from [START_REF] Ashby | Materials and the Environment: Eco-informed material choice[END_REF]- [37]. New annual installations per technology are produced by the power sector module of the POLES model endogenously, on the basis of equipment lifetime, total costs and bottom-up electricity demand projections. Power generation capacities lifetimes differ depending on the technology; they range from 20 (wind) to 50 (hydro) years. Specific steel consumption for the power transport and distribution grid (kg/GWh) was derived from [START_REF] Harrison | Life cycle assessment of the transmission network in Great Britain[END_REF]. New needs for the grid arise from the projections of power generation and a grid equipment lifetime. Grid equipment is considered to have a 40 year lifetime.

Infrastructure and other

The remaining demand pertains to the construction of public works and other infrastructure (roads, rail network, oil and gas and mining extractive industries, manufacturing industries), to machinery and equipment (mechanical engineering, maritime vessels and other transport vehicles, containers and packaging, tubes, domestic appliances, and other uses). This work estimates that in 2015, it corresponded to 52% of global steel demand. Given the diffuse nature of this demand category and the lack of specific drivers to associate it with in the energy system model for a bottom-up estimation, this remaining demand was projected as a constant percentage of total demand. In projections, the aggregate changes in the three bottom-up end-uses above drive the evolution of the residual demand.

Steel production

Currently, three main processes dominate world steel production. The blast furnace followed by a basic oxygen furnace (BF-BOF, 72% of world production in 2020) makes use of coking coal as a reduction agent for the iron oxide in the ore; steel plants can also include facilities to convert mineral coal into coke. The electric arc furnace (EAF, 22% of production) is a form of recycling, it melts scrap metal into new steel items. The direct reduction with EAF process (DRI, 6%) does away with coking coal and uses a mix of hydrogen and carbon monoxide (as syngas derived from methane or coal) as reduction agents, producing sponge iron, which is fed into an EAF to produce steel. Market shares have been mostly stable over the past 30 years, with direct reduction gaining importance at the expense of electric arc.

Production processes considered for the modelling were limited to those with a technology readiness level today that have a reasonable chance to represent a sizable share of production capacities in the time horizon of the study. The Technology Readiness Level (TRL) of production processes were taken from [START_REF] Keys | Decarbonisation options for the Dutch steel industry[END_REF]- [START_REF] Bashmakov | Chapter 11: Industry[END_REF]; technologies with a TRL above 5, i.e. from demonstration level to mature, were selected to be modelled. They were: BF-BOF; EAF; DRI using fossil gas followed by EAF (DRI-NG); DRI using hydrogen directly (DRI-H2) followed by EAF; smelting reduction followed by BOF (Smelt); low-temperature electrolysis (electrowinning, E-winn); as well as 3 carbon capture options (BF-BOF+CCS, DRI-NG+CCS, Smelt+CCS). Technologies still at a research level, such as hightemperature electrolysis, were excluded.

Process efficiency and investment costs for new installations are set exogenously (from [START_REF]Making Net-Zero Steel Possible[END_REF], [START_REF] Keys | Decarbonisation options for the Dutch steel industry[END_REF], [START_REF]Best available techniques (BAT) reference document for iron and steel production: industrial emissions Directive 2010/75/EU : integrated pollution prevention and control[END_REF]- [START_REF] West | Hydrogen direct reduction steelmaking (HYBRIT) with external hydrogen production[END_REF], the assumptions are presented in SI section 6). Historical statistics on energy [START_REF] Enerdata | Global Energy & CO2 Data[END_REF] and capacities [START_REF]World Steel in Figures 2022[END_REF], [START_REF] Midrex | World Direct Reduction Statistics[END_REF] are used to set up process efficiency and fuel mix for existing equipment. Coke ovens were not accounted in the iron and steel sector but in the energy transformation sector.

Investment in new production capacities is calculated based on the growth of production in past years. The choice among capacities is made using a logit distribution with total costs and preference factors. Total costs consist of capital expenditure (CAPEX) and a number of operational expenditures (OPEX):

 CAPEX: overnight investment costs annualized over the lifetime of the equipment with a discount rate;  raw material costs: iron ore (driven by international maritime transport cost), steel scrap (availability influences the steel scrap price), coking coal (endogenously calculated), oxygen (constant);

 energy input costs (endogenously calculated, with upstream production costs and trade for fossil fuels and multiple production technologies for electricity and hydrogen, which are purchased by the steel sector);  CO2 price (exogenously defined);  CCS transport and storage cost (constant) and carbon credit from biomass-CCS (determined by the carbon content of biomass and can cover, at most, the energy input costs and CO2 price);  revenue from by-products (slag produced in basic oxygen furnaces is sold at a constant price).

CCS technologies include a fixed CO2 capture rate (BF-BOF: 65%; DRI: 55%; Smelt: 80%, from [START_REF] Keys | Decarbonisation options for the Dutch steel industry[END_REF], [START_REF] West | Carbon capture retrofit to REF Basic oxygen furnace steelmaking on coke ovens, hot stoves, lime kilns and steam generation units (60% avoided emissions)[END_REF], [START_REF] West | HIsarna with CCS[END_REF]).

Preference factors represent the factors that influence the choice that are unrelated to pure economics, reflecting country specificities observed on the historical mix such as inertia in choosing existing technologies (the weight of past capacities, its effect is phased out by 2035), or technological availability for new technologies (CCS availability and electrowinning maturity grows over time; a shortage in global scrap curtails investments in new electric arc capacities). In addition, there is the possibility to retrofit certain processes into others (in particular for retrofitting CCS).

The choice among fuels within each process is made using a logit distribution with user costs, including efficiency and carbon pricing and any process-specific constraints (such as a specific reduction agent). Biochar being a less energy dense fuel than coal and traditionally used in smaller furnaces, the use of biochar as a coke substitute was capped at 50% of the energy consumption of furnaces.

Regional allocation of global production is done on the basis of the region's average production cost and a transport cost (cost of exporting towards net importers, weighted average over importers' market share in global trade) under the constraint of available capacities per region.

Scenarios

In order to take into consideration several uncertainties in how the energy system as a whole, as well as steel demand and steel production technologies more specifically, might develop, we considered multiple scenarios that were quantified with the POLES model.

The scenarios are driven by macroeconomic projections (GDP, population) that are defined exogenously as model inputs; energy prices, technological development and emissions are calculated endogenously under scenario-specific constraints and parametrization. Scenario rely on historical statistics for energy demand and steel production, which extend to the year 2020.

We assessed the following contrasted scenarios:

A Reference (Ref) scenario: existing policies related to energy supply and demand policies and targets, as well as legislated emissions policies and targets that are backed by supporting energy-sector policies, are enacted. No additional policies are considered compared to what had been legislated as of June 2022 (see Annexes of [START_REF]Global energy and climate outlook 2022[END_REF] for the list of policies considered). Accumulated net CO2 emissions over 2020-2100 amount to 3070 Gt, resulting in a global mean temperature increase at the end of the century of 3.0°C (median probability), as calculated by the online MAGICC tool [START_REF]LiveMAGICC[END_REF].

Several scenarios compatible with the Paris Agreement goal of limiting climate change to 1.5°C above pre-industrial levels: they differ in several dimensions related to technological availability and maturity, and in the socio-economic implementation of the climate policies necessary to reach the temperature objective. They consist in:

(i) 15C_CCS with an earlier availability of carbon capture across the entire economy, to study a steel sector that might continue its reliance on technologies that involve combustion;

(ii) 15C_Elec with more ambitious technological learning for new direct and indirect electrification technologies, to study a steel sector that might move more towards a new paradigm;

(iii) 15C_LD with lower energy service demand and lower material demand, resulting in fewer emissions to decarbonize on the steel production side, to study a steel sector where sobriety, efficiency and circularity are more important;

(iv) and 15C_Opt which combines elements of all of the above, to study a best-case scenario for the degree of economic effort to obtain a decarbonized steel sector.

The specific levers that were used to parameterize these scenarios are presented in Table 1 and are detailed below. Climate policy: For each 1.5°C scenario, a carbon price is applied to all regions and all sectors of the economy. The carbon price follows a sigmoid curve starting from 2022, with an inflection in 2040 and a saturation level in 2100; it is adapted for each 1.5°C scenario for a peak-temperature budget (i.e., at the year of global net-zero CO2 emissions) of 650 GtCO2 from 2020, which is compatible with a 1.5°C global mean temperature increase compared to preindustrial levels at the end of the century. 2100 prices are 0, 880, 1130, 795 and 750 $/tCO2 for the scenarios listed above.

CCS availability: availability for installing CCS options and CCS infrastructure is determined by a multiplying factor to the technology's cost, uniformly across all regions, with a duration of 50 years from zero (first-of-a-kind) to one (pure cost-competition); first-of-a-kind from 2020 (default) or from 2050 (delayed).

Bioenergy availability: lignocellulosic biomass availability is determined by supply costs; higher costs result in total global bioenergy demand not exceeding 170 EJ/year (conservative) and lower costs result in 200 EJ/year (optimistic). A cap for bioenergy penetration in steelmaking processes was set at 50% (conservative) and 75% (optimistic). As a convention, solid biomass energy use is considered carbon-neutral (compensated by carbon absorbed in the growth of new biomass in a sustainably managed cycle); and biomass with carbon capture and sequestration (BECCS) is considered as carbon net-negative.

New electricity costs: investment costs for the electrowinning furnace for steel differ, with optimistic costs a third lower than pessimistic costs. Hydrogen electrolyser costs decrease, reaching 480 $/kW, as per [START_REF] Iea | The Future of Hydrogen[END_REF] (pessimistic), and to 98 $/kW in 2050, as per [START_REF] Bnef | Hydrogen: The Economics of Production from Renewables: Costs to plummet[END_REF] (optimistic); see SI section 7 for a complete view of hydrogen production pathways and costs. In addition, in the pessimistic case the steel sector is provided by the average hydrogen considering all production pathways plus a transport and distribution duty; the optimistic case sees green hydrogen production taking place close to the steel plant, resulting in a price driven only by wind and solar electrolysis technologies, without a transport and distribution duty.

Energy conservation measures: building surfaces and vehicle stocks are related to demography, economic growth and occupancy rates. Residential surfaces are capped to a maximum value of 50 m 2 /cap (default: no cap; the region with the highest level, the USA, reaches 108 m 2 /cap in 2100). The car ownership ratio is capped to a maximum of 0.4 car/cap (default: values are capped by 0.6 car/cap); in addition, we assume a modal shift from private transport to public transport and soft modes, maintaining the total passenger mobility compared to the default case.

Material efficiency measures: specific material demand per floor surface area for buildings decreases over time (to 67% of its initial value by 2050) and specific material demand per vehicle for road transport decreases over time (to 50% of its initial value by 2050), uniformly across all regions, with 2050 values following the ambitious scenario in [START_REF] Iea | Material efficiency in clean energy transitions[END_REF]. Steel scrap recycling increases to a maximum recovery rate (from an estimated 80% of end-of-lifetime scrap collection rate in 2020, to 90% in 2030, accounting for 10% considered unrecoverable).

Steel plant lifetime: the default value (35 years) for renewing the stock of steel production capacities was reduced in the 1.5°C scenarios (23 years) to reflect increased pressure to renew the stock towards more low-carbon processes. See SI sections 6 and 8 for more details.

RESULTS AND DISCUSSION

Scenarios were executed to 2100; results are presented for the world and key regional aggregates; more granular results are provided in SI spreadsheets.

Growth of world steel demand to decelerate

Global steel demand is projected to continue to increase in the coming decades, but at a decreasing rate of growth with a stabilization in the second half of the century (Figure 2). Demand is projected to reach 2.98 Gt in 2050 (65% above the 2020 level) peak in 2070 and reach 3.62 Gt in 2100 (15C_CCS). Annual demand growth decreases from 4.4%/year over 2000-2020 to 1.7%/year over 2020-2050 and 0.4%/year over 2050-2100. This is driven by a strong demand increase in India and the Rest of the world, while demand in China stabilizes to 2050 and increases again thereafter.

Total steel demand evolution is due to an increase of demand over all uses. In terms of volume, most of the increase to 2050 comes from infrastructure, followed by road vehicles, power and, finally, buildings construction. A second wave of buildings construction in China in the 2070-2090 period, roughly 70 years after the first wave over 2000-2020, results in global steel demand for buildings increasing again, while demand in most other regions increases or stabilizes. Demand for the other uses increase moderately throughout the end of the century.

These increases are driven by the underlying socio-economic conditions. Demand for buildings (+29% over 2020-2050) is driven by an increase in global floor surface area (+66%). Demand for transport (+214% over 2020-2050) is driven by an increase in global passenger (+46%) and goods (+65%) traffic volumes, with corresponding increases in vehicles stocks. Demand for power (+302% over 2020-2050) is driven by an increase in global power production (+180%) (15C_CCS). The implementation of climate policies leads to a positive feedback effect on steel demand. This amounts to 19-21% higher steel demand in 2050 and an additional 16-17% of accumulated demand over 2020-2050 (15C_CCS and 15C_Elec compared to Ref). This is brought about by an increase in demand for transport and for power, and to a lesser degree for buildings. This feedback would result in an increase in emissions in order to produce this extra steel; however, in all cases, this increase is smaller than the emissions savings brought about by the new low-carbon equipment and infrastructure built with this extra steel.

In buildings, climate policies accelerate the renovation rate in order to achieve higher buildings shell insulation levels, resulting in more steel needs. Over the 2020-2050 period, accumulated renovated surfaces are 29-32% higher (15C_CCS and 15C_Elec vs Ref), and accumulated steel demand is 4-5% higher compared to Ref. The productionside increase in emissions induced by this higher steel demand (about 0.9-1.0 GtCO2, accumulated) are more than compensated by the emissions savings brought about by more energy-efficient buildings and the accompanying switch to low-carbon fuels in buildings energy use (which were at 3.0 GtCO2/year in 2020).

In road transport, similarly, climate policies induce a faster stock turnover in order to achieve decarbonisation faster; in addition, climate policies induce a certain reduction of overall passenger mobility as well as a certain modal shift from private cars to busses. These effects combine to result in 2020-2050 accumulated steel demand 20-23% higher (15C_CCS and 15C_Elec) compared to Ref. This considerable increase would result in more emissions from steel production (about 2.3-2.7 GtCO2, accumulated); however, they would amount to the equivalent of less than the year 2020's total road transport energy use emissions.

In power, climate policies push towards higher electrification and a shift towards lower-carbon power sources. As the power sector is progressively decarbonised, electricity becomes a lower-carbon energy vector and power demand becomes higher. While low-carbon technologies require less steel than fossil fuel technologies per unit of installed capacity on average, some of them, such as wind and solar, have a low load factor and more units would need to be built to replace the equivalent fossil fuel unit with the same nameplate capacity. Over the 2020-2050 period, the average steel intensity of newly installed capacities is 7% lower in 15C_CCS compared to Ref (106 kg/kW vs 115 kg/kW) while accumulated installed capacities are 65% higher, resulting in an accumulated steel demand that is 53% higher (55% for 15C_Elec). Again, this feedback would result in additional emissions in steel production compared to Ref (approximately some accumulated additional 2.4 GtCO2); however, these would be much smaller than the emissions saving brought about by the decarbonisation of the power sector (from about 12 GtCO2/year in 2020 to net-zero in 2050).

In infrastructure and other uses, resulting from the combined behaviour of the above end-uses, steel demand is also higher than in the Ref (17% accumulated).

As a direct result of the energy and material savings and efficiency measures, the steel demand in 15C_LD is lower.

The climate policies feedback on steel demand could be contained and even overcompensated, resulting in total steel demand that is slightly lower than in Ref (-4% accumulated for 15C_LD, -5% for 15C_Opt). The effect is larger in the specific sectors where measures were implemented, buildings (-15%) and road transport (-18-20%). Demand in power is little different to the other 1.5°C scenarios (+44-48%), with differences mostly due to the feedback of lower steel production on electricity demand itself. The difference becomes more pronounced after 2050, when the measures have been fully phased in; total steel demand peaks in 2070 but at a much lower level (2.54 Gt in 15C_LD compared to 3.80 Gt in 15C_CCS), a level that is exceeded already in the 2020s in the other 1.5°C scenarios. Thus, these savings and efficiency measures would provide a longer time for the steel production industry to respond to an increasing demand while simultaneously meeting the decarbonisation challenge. Indeed, it could be realistically expected that the higher costs of producing low-carbon steel would be passed down to the end-users, which would result in a demand level responding accordingly; while a steel price elasticity on demand was not included in this modelling, its effects are here approximated by the adoption of such savings and efficiency measures.

Steel production mix to diversify

Steel production mix

Recent industrial-scale innovation and investment projects are focusing on DRI-H2 and DRI-NG-CCS solutions [START_REF]Iron and Steel Technology Roadmap: Towards more sustainable steelmaking[END_REF], which might prove to be decisive for the medium term. We offer here a longer-term view that takes into account a wider range of options that might be available to the sector. The production mix in the projection evolves from the current situation. Figure 3 presents the world production mix, including the substitution process that takes place over part of the stock once older equipment is retired and the effects of retrofitting CCS onto existing capacities.

Without strong climate policies (Ref), there is a strong increase of electrification (EAF) and a progressive penetration of direct reduction and, to a lesser degree, of smelting reduction, at the expense of BF-BOF. EAF covers 57% of production by 2050, up from 22% in 2020. Indeed, the scrap available for recycling (recovered from equipment reaching the end if its lifetime as well as new scrap produced in steel plants) more than doubles over 2020-2050 (+116%), allowing this considerable expansion of secondary steel production. Primary steel makes up 43% of total production, which is sufficient to provide for specialty steel products for which secondary steel is not of high enough quality.

With strong climate policies (15C_CCS), the mix is much more diversified: CCS technologies emerge (taking up 32% of the market by 2050) and direct reduction decreases (however, direct reduction using hydrogen remains approximately the same at 2-3%). These effects are more noticeable with more optimistic assumptions on electricity (15C_Elec), where CCS is substituted by more EAF (63%), more hydrogen and more unabated technologies, resulting in higher emissions.

DRI-H2 production over all scenarios increases over time in the first half of the century and reaches 2% (15C_LD) to 15% (15C_Elec) share of global steel production of in 2050, the latter amounting to 40 MtH2/year. In the second half of the century, the comparatively cheaper CCS options result in that share decreasing. This low share for DRI-H2 is consistent with other studies where this technology was included in economic competition with other production routes [START_REF]Making Net-Zero Steel Possible[END_REF], [START_REF] Van Sluisveld | A race to zero -Assessing the position of heavy industry in a global net-zero CO2 emissions context[END_REF]. As the CO2 price increases, the direct reduction process using hydrogen gains advantage over using natural gas and partly capturing the CO2 emissions with CCS.

The OPEX of EAF is driven upwards partly due to the impacts of the decarbonisation effort on the electricity price, but also due to the higher price for scrap, due to a higher demand for scrap globally.

The extended use of BF-BOF, Smelt and DRI technologies with CCS at the expense of EAF results in an increase of energy intensity, either after 2050 in 15C_CCS or after 2070 with the delayed implementation of CCS in 15C_Elec.

Steel sector emissions

The resulting CO2 emissions of the steel sector are presented in Figure 4. Steel sector emissions in 2020 are estimated at 2.4 GtCO2. Even without strong climate policies (Ref), emissions peak in the 2020s decade, decrease significantly and stabilize after 2040 to around 1.5 GtCO2, with the CO2 content of steel decreasing from 1.3 tCO2/t in 2020 to 0.5 tCO2/t in 2050.

With strong climate policies, the steel sector emissions decrease dramatically to around 0.3 GtCO2 in 2050; the CO2 content of steel reaches 0.1 tCO2/t in 2050 for all 1.5°C scenarios, under an average carbon price in 2050 of 800 $/tCO2. Net-zero emissions are reached in the second half of the century only thanks to the contribution of CCS technologies and biomass (during the 2070s for 15C_CCS and 15C_LD, not reached by the end of the century for 15C_Elec).

As evidenced by the 1.5°C scenarios, the carbon price, CCS availability and plant lifetime are the most important drivers for emission reductions (see also the sensitivity analysis in SI section 8). The shortening of lifetimes in the 1.5°C scenarios to allow a faster turnover is a significant contributing factor to the production mix change and fall in emissions. However, this presents the risk of considerable sunk costs in processes with unabated emissions. Overall, following the logarithmic mean Divisia index (LMDI) method [START_REF] Ang | The LMDI approach to decomposition analysis: a practical guide[END_REF], the main decarbonisation options expressed as a decomposition of emission differences between 2020 and 2050, averaged over the four 1.5°C scenarios, are (Figure 5): electricity (70%), biomass (12%), CCS (10%, most of which with fossil fuels), fossil fuel switch from coal to gas (9%), process efficiency (6%), hydrogen (5%). The increase in tons produced counts as an additional constraining factor for decarbonisation as it would otherwise lead to additional production-side emissions (activity, -17%). Source: POLES model. "Mitig vs 2020" refers to total emissions reductions in 2050 compared to 2020, decomposed in options below. Sector efficiency has been distributed across the "Efficiency" and "Electricity" options, given the large difference in energy use of the EAF process compared to all others. Hydrogen is sourced outside of the steel facility (green hydrogen is not accounted in electricity).

Steel sector investments and production costs

Investments to support the supply of steel (overnight investment costs of production capacities), both to replace ageing equipment and for greenfield plants, are considerable. They amount to an average of 11 G$/year over the G$/year as a result of demand growing much slower. Investments are higher in 15C_CCS (22 G$/year, +94% compared to Ref), given the costlier technologies chosen, the retrofitting towards CCS, but also the higher capacities installed (+24%) due to an even higher demand. This is mitigated by demand-side savings and material efficiency measures (15C_LD and 15C_Opt), where average investments are lower (14 G$/year, +19% compared to Ref).

As a result, taking into account the total costs (investment, raw materials, fuels cost, carbon price), the average ton of steel produced in the world becomes costlier. In Ref, the increase is small (+7% over 2020-2050); in 15C_CCS, steel is more than half as much (+67%) more expensive to produce in 2050 compared to 2020, from about 650 to 1080 $/t. If these costs were entirely passed down to the final consumer, this would make an average car (using 900 kg of steel) some 390 $ more expensive (or 1.4% more expensive considering 28,000 $/car); this is higher than in [START_REF] Rootzén | Paying the full price of steel -Perspectives on the cost of reducing carbon dioxide emissions from the steel industry[END_REF] (<1%) but consistent with the more ambitious climate target of 1.5°C in this study. This anticipated lowcarbon premium can be a limiting factor in the expansion of clean steel production for first movers, and innovative solutions will be needed to decrease the risk for investors (such as rules to access public finance, purchase contracts for low-carbon products, group investments around industrial clusters) [START_REF]Scaling Up Europe. Bringing Low-CO2 Materials from Demonstration to Industrial Scale[END_REF].

Whether costs would be passed down, especially the costs related to the carbon price, would be a policy-making question related to consumer protection and purchasing power beyond the scope of this study, as well as a matter of climate policy. With rising steel prices, it could be expected that the demand would adjust accordingly: this is indeed reflected in 15C_LD, which sees a steel demand that is 19% lower than Ref and a steel cost that is rises by 61% by 2050 (i.e., 50% higher than Ref in 2050). By comparing Ref and 15C_LD in 2050, the implicit price elasticity of steel demand is -0.54, which is within the range of values observed for long-term elasticities for energy products [START_REF] Labandeira | A meta-analysis on the price elasticity of energy demand[END_REF]. The steel saving measures could, however, be implemented by behaviour change and policy intervention with tools other than just pricing, such as building codes or equipment standards.

Of note, most of the production costs of steel are operating costs rather than fixed costs: the share of operating costs increases from 79% in 2020 to 87% and 90% in 2050 in Ref and 15C_CCS, respectively.

Hydrogen supply for steel

It then becomes an issue on how to supply the steel sector with sufficient quantities of low-carbon energies in a timely manner. This would be particularly challenging for new fuels that do not yet have a production, transport and distribution infrastructure in place, such as hydrogen.

Hydrogen production would have to move away from the current processes dominated by unabated fossil fuels (steam methane reforming, coal gasification) towards lower-carbon solutions, at a considerable cost (see SI section 7).

Our results show that hydrogen demand for steelmaking is projected to make up a relatively small share of overall hydrogen demand (as much as 6% of total hydrogen demand, in 15C_Elec in 2040), taking into account non-energy uses such as hydrogen as an intermediate product in nitrogen-based fertilizers, in oil refineries and other chemical processes. Other emerging uses, such as hydrogen fuel cells in transport and the use of hydrogen as an input to the production of synthetic hydrocarbons, consume larger amounts of hydrogen; in decarbonisation scenarios, they are projected to be the dominant uses of hydrogen worldwide [START_REF]Global energy and climate outlook 2022[END_REF], [START_REF] Iea | Global Hydrogen Review 2021[END_REF]. Thus, the steel sector is likely to not be a driving force in shaping the hydrogen production industry and its market price, and it is likely to be a price taker of hydrogen produced for multiple purposes. For the current most advanced pilot DRI-H2 projects, the same entity will be producing the hydrogen on-site using low-carbon grid electricity specifically, relying on other actors to expand grid capacity [START_REF] Lkab | HYBRIT: A unique, underground, fossil-free hydrogen gas storage facility is being inaugurated in Luleå[END_REF], [START_REF] H2 Green | On course for large-scale production from 2025[END_REF]. However, DRI-H2 projects' electrolyser capacity is a small share of the overall hydrogen projects underway in Europe [START_REF]Scaling Up Europe. Bringing Low-CO2 Materials from Demonstration to Industrial Scale[END_REF]. This raises questions as to whether self-production of hydrogen would be the most likely configuration for the steel sector, or whether the business model that would dominate would be hydrogen produced in a variety of ways and transported to the steelmaking site where it would be consumed.

Steel trade patterns to reorganize

The regional distribution of steel production, dependent on production capacities, production costs and transport costs, remains concentrated around China in the projections (Figure 6). The production over-capacity in certain regions resulting from changing demand patters redraws the map of potential importers/exporters (Figure 7). In the medium term, with standing capacities and a decreasing domestic demand, China could become a major net exporter. Certain regions change their trade status, from net importers to net exporters (USA, South Africa) at the expense of other exporters that reduce their exports (Russia, Brazil, Australia). The EU moves towards relying massively on imports, while the Rest of the world moves from relying significantly on imports toward selfsufficiency. In the longer term, the increase in transport costs becomes an important component of traded steel cost: on world average, from 6% to 11% and 15% in 2050 in Ref and 15C_Opt, respectively, resulting in more self-sufficient supply on average.

The implementation of climate policies usher small changes in the market shares of regions in the world steel production mix compared to Ref: in 15C_CCS, China +4% market share at the expense of EU, India and the Rest of the world; in 15C_Elec, China -6% at the benefit of the USA and the Rest of the world (15C_LD and 15C_Opt similar to 15C_CCS). Broadly, countries with a prevalent production mix based on EAF (e.g., USA) are better equipped to reduce emissions if CCS is unavailable and gain market share in 15C_Elec, while those with a predominantly BF/BOF-based mix (e.g., China) lose market share.

These relatively small changes in market shares are due to the uniform implementation of the carbon price in the 1.5°C scenarios in all regions; a fragmented climate policy with differentiated levels of decarbonisation effort across regions would result in a greater cost diversity and adjusted trade patterns. As global patterns of demand shift and production capacities adjust, steel trade (global net imports with the model's regional disaggregation) experiences changes in the coming decades, with less trade in the lower steel demand scenarios and overall volumes similar to the recent past (Figure 7). While traded volumes increased over the past decades and reached an all-time high in 2016, exports as a share of total production have actually been decreasing since 2000 (from 39% to 23% in 2020 [START_REF]World Steel in Figures 2022[END_REF]), which is extended and accelerated in the projections (19% and 23% in 2050 in Ref and 15C_Opt, respectively). 15C_Elec shows a notable increase in trade by mid-century as the delay of CCS increases residual emissions in BF/BOF-rich countries and changes the regions' ranking of production costs.

These results have to be put in a wider context of country-level industrial policy. Additional non-cost deciding factors might contribute to the decision to relocate production or not. Given the importance of the steel sector in direct and indirect employment, EU policy has regulation to mitigate relocation risk and prevent carbon leakage (EU emissions trading scheme [START_REF] Branger | Carbon Leakage and Competitiveness of Cement and Steel Industries Under the EU ETS: Much Ado About Nothing[END_REF], carbon border adjustment mechanism [START_REF] Sun | The carbon border adjustment mechanism is inefficient in addressing carbon leakage and results in unfair welfare losses[END_REF]). Other factors include innovation and strategic concerns, as per the EU's proposed Net-Zero Industry Act to maintain or increase domestic production.

Conversely, low-carbon steel producers might position themselves as exporters towards leading consumers that value low-carbon products (e.g., South Africa to EU [START_REF] Trollip | How green primary iron production in South Africa could help global decarbonisation[END_REF], Emirates to Japan [START_REF] Steel | Emirates Steel Arkan teams up with Japanese companies to produce low-carbon steel[END_REF]).

Moreover, in addition to new steel trade, the increase in electrification would also be enabled by recycling and large amounts of scrap trade worldwide.

Wider impacts

The supply of energy fuels, including hydrogen, to support the needs of the steel industry come with wider impacts on the energy system and on other sustainability indicators such as land use and water use.

In terms of land use (Figure 8), the low-carbon alternatives come at a significant premium, compared to a 1.6 km 2 /Mt production capacity for a typical BF-BOF steel plant today [START_REF] Gupta | Challenges to the growth of Indian Steel industry[END_REF]. A plant supplied entirely by hydrogen produced by electrolysis using solar power would require 20 times more surface (considering a 80 MW/km 2 land use for utility-scale PV [START_REF] Irena | IRENA Global Atlas Spatial planning techniques Session 2a: Solar power spatial planning techniques[END_REF]); importing the hydrogen by ship would require some 50 deliveries (considering an ammonia tanker carrying 1300 TJ/trip of NH3 similar to a long range oil tanker of 110,000 deadweight tons [START_REF]Oil tanker sizes range from general purpose to ultra-large crude carriers on AFRA scale[END_REF] and an 74% H2-NH3-H2 round-trip efficiency [START_REF] Zhang | Techno-economic comparison of green ammonia production processes[END_REF], [START_REF] Giddey | Ammonia as a Renewable Energy Transportation Media[END_REF]) to 105 deliveries (considering a liquefied hydrogen tanker carrying 500 TJ/trip similar to an 125,000 m 3 LNG tanker [START_REF] Bai | LNG Carrier[END_REF] and 4% of losses [START_REF] Irena | Global hydrogen trade to meet the 1.5°C climate goal: Part II -Technology review of hydrogen carriers[END_REF]). In addition, a BF-BOF plant that would switch entirely to biochar and biomass would require 460 times more surface (considering 200 GJ/ha of bioenergy crops yield [START_REF] Frank | Land-based climate change mitigation potentials within the agenda for sustainable development[END_REF]), while a switch to EAF with zero-emissions electricity would require the power grid to install for instance wind capacities occupying the equivalent of 25 times the area of the BF-BOF plant (considering 8 MW/km 2 for onshore wind [START_REF] Eea | Europe's onshore and offshore wind energy potential: An assessment of environmental and economic constraints[END_REF]). Positioning the energy production on a remote site would increase issues of supply logistics and transport losses. Given that steel plants are often located on shorelines to ease the import and export of feedstocks and products, and that steel plants are often in industrial areas where land already has competitive uses, there are significant challenges in low-carbon energy supply to enable low-carbon steel. In terms of renewables deployment rate, the expansion of low-carbon hydrogen would further burden the power system to install sufficient capacities of renewables to supply both electricity end-uses and hydrogen electrolysers. The decarbonisation of electricity poses significant administrative, investment and supply chain challenges already; wind and solar installation rates globally would need to increase by a factor of 11 in the 2040s compared to the 2015-2020 period (from 160 GW/year to 1,800 GW/year). Adding capacities for green hydrogen on top of that, as much as 35% additional by 2050, would raise that factor to around 16 (2,670 GW/year, 15C_Elec).

In terms of water consumption, if the above maximum of 15% of steel tons produced with the DRI-H2 process in 2050 are supplied by hydrogen produced with electrolysis, this would require 0.36 Gm 3 /year of water (considering 9 L/kgH2 stoichiometrically; 2.4 Gm 3 /year for all steel production, 3.8 Gm 3 /year for all hydrogen production). However, these volumes are small when considering the larger consumers of water such as agriculture (2,900 Gm 3 /year in 2019 [START_REF]AQUASTAT[END_REF]) or withdrawals for energy production mainly for power plant cooling (338 Gm 3 /year in 2016 [START_REF]World Energy Outlook 2018[END_REF]). While volumes are small, regional heterogeneity on water resources could raise local concerns of water scarcity. Furthermore, water could be recuperated from the DRI furnace shaft and recycled to produce hydrogen [START_REF] Bataille | Global facility level net-zero steel pathways: Technical report on the first scenarios of the Net-zero Steel Project[END_REF].

In terms of CO2 flows, the carbon captured in steelmaking and other sectors (hydrogen production, power generation, other) would need to be transported with dedicated infrastructure to the point of sequestration. While the costs are estimated to be low on a per ton basis (around 10 $/tCO2 [START_REF] Iea | Is carbon capture too expensive?[END_REF]), the infrastructure remains to be built entirely, or use repurposed fossil fuel infrastructure, with few actual projects for country-or continental-scale transport materializing as of 2023 [START_REF]Zero Emissions Platform[END_REF]. The world average steel plant of 2.3 Mt/year [START_REF]Global Steel Plant Tracker[END_REF] would produce some 4.2 MtCO2/year in the case of a BF-BOF plant equipped with carbon capture. This is comparable to the mass transiting through international gas pipelines (e.g., the Algeria-Tunisia-Italy TransMed pipeline capacity of 30.2 Gm 3 of natural gas over two lines, amounting to 11.6 Mt/year [START_REF]Trans-Mediterranean Natural Gas Pipeline[END_REF]), which underlines the scale of the effort required to put this infrastructure in place.

CONCLUSION

This paper presents work that gives new quantitative insights in the complex decarbonisation challenge of a sector perceived as "hard-to-abate" [START_REF] Bashmakov | Chapter 11: Industry[END_REF]. This work enriches the representation of industry sub-sectors in energy system models and consists in the state of the art in the way these issues are captured in energy system models. It provides an explicit picture of steel demand and its drivers, of steel production investment needs and of the effects of policy levers that are of interest to both policymakers and investors.

In this paper, we presented a new model detailing steel demand and multiple steel production pathways within a larger global multi-regional energy system simulation model. In order to assess the decarbonisation potential of the steel sector in interaction with the rest of the economy, we built several scenarios of the future energy system with different sets of assumptions on policies, technologies and steel demand patterns. We examined to what extent and at what pace the iron and steel sector can reduce its emissions and the contribution of each decarbonisation option. Finally, we broadened the scope to some additional challenges in the provision of low-carbon energy to fuel the cleaner production of steel.

This study adds an original set of projections for steel demand to the literature. Without additional climate policies, we project global steel demand to increase at a decelerated pace compared to the past two decades, reaching a plateau from 2070 at nearly double the level of 2020. This is calculated in a bottom-up manner due to substantial increases in buildings floor surfaces, road transport fleet, power production system and infrastructure, all of which are ultimately driven by the underlying socio-economic conditions, if material used per unit of activity remain the same.

This study differentiates drivers for steel demand increase and savings, with a decomposition of effects that is new to the literature. The implementation of climate policies leads to a positive feedback effect on steel demand due to a faster equipment turnover and higher electrification; however, this increase would result in new low-carbon equipment and infrastructure that would yield large emissions savings. Introducing energy saving and material efficiency measures, which could be expected given the projected increase in steel production costs, would overcompensate this increase. Our demand projections in 2050 range from to 2.0 to 3.0 Gt. Overall, these projections are comparable to those of other studies: e.g., for 2050, 2.0-2.6 Gt in [START_REF]Iron and Steel Technology Roadmap: Towards more sustainable steelmaking[END_REF], 2.0-3.4 Gt in [START_REF] Kermeli | Improving material projections in Integrated Assessment Models: The use of a stock-based versus a flow-based approach for the iron and steel industry[END_REF], 1.5-2.5 Gt in [START_REF]Making Net-Zero Steel Possible[END_REF] and 1.7-5.2 Gt in [START_REF] Watari | Major metals demand, supply, and environmental impacts to 2100: A critical review[END_REF].

We provide "what-if" projections of decarbonizing steel supply based on technology availability and production costs, as opposed to normative scenarios of technology substitution [START_REF] Pye | Regional uptake of direct reduction iron production using hydrogen under climate policy[END_REF] or potentials assessments [START_REF] Fan | Low-carbon production of iron and steel: Technology options, economic assessment, and policy[END_REF]. The CO2 emissions of the steel production sector are projected to decrease even without strong climate policies: with increased recycling thanks to the higher availability of steel scrap, electrification increases at the expense of the currently dominant blast furnace process and thereby yields important energy efficiency gains. Strong climate policies would be needed to push decarbonisation further. In the context of a full decarbonisation of the economy compatible with a 1.5°C climate change target, steel sector emissions would decrease by nearly 90% in 2050 compared to 2020. This achieved with the use of hydrogen in direct reduction but especially thanks to CCS technologies, increased recycling and electrification and use of biomass. The steel sector could reach net zero as early as the 2070s; however, a delay in the availability of CCS technologies would make this unfeasible.

The costs of material efficiency measures are difficult to quantify; however, the economic assessment hints at their attractiveness as emission mitigation measures. In all decarbonisation scenarios, the steel production cost is projected to increase by half over 2020-2050, mainly driven by the price of energy inputs (including carbon pricing), thereby providing a strong impetus for the implementation of the savings and efficiency measures as an economically efficient option for decarbonisation.

The geographical distribution of steel production as well as the importer/exporter status of world regions are projected to experience in-depth changes. However, as a result of increasing transport costs, global steel trade volumes are projected to stabilize over time.

Although the hydrogen route has gathered significant investments for electrolysis and steel production in recent years [START_REF] H2 Green | On course for large-scale production from 2025[END_REF], [START_REF]COM/2020/301 final: A hydrogen strategy for a climate-neutral Europe[END_REF], [START_REF]Overview of Basic Hydrogen Strategy[END_REF], we project it to make up only a small part of the solution for steel due to its high supply cost. Direct reduction using hydrogen is projected to produce, at most, 15% of global steel throughout the century and across all scenarios, with a wide diversity across regions. The above share is reached in an optimistic configuration (green hydrogen produced in proximity to the steel plant, ambitious electrolyser cost decreases). Hydrogen demand for steelmaking is projected to make up a minority share of total hydrogen demand. This would put the steel sector in a position of price taker and raises questions as to which business model is likely to materialize, one of steelmakers purchasing hydrogen on the market or one where hydrogen and steel production is co-located. Furthermore, the provision of large enough quantities of low-emissions energy to the steel sector would pose significant challenges in many fields. The indirect land use would increase severalfold to provide renewable electricity, green hydrogen or biomass. The pace of installation of renewable electricity capacities would need to increase by as much as 16 times compared to the recent past. The carbon transport and sequestration infrastructure remains to be built. This study confirms the considerable multi-sectoral challenges related to the decarbonisation of the steel sector. Although policymaking and private sector interest in this issue has increased in recent years, more needs to be done in multiple fronts: adapt climate policy to align investments in clean technologies, mitigate bottlenecks in clean energy supply and CO2 transport infrastructure and anticipate potential adverse effects in land use, water use and employment.

Figure 1 :

 1 Figure 1: Schematic view of the steel demand and production modules of POLES for this work

Figure 2 :

 2 Figure 2: Global steel demand (a) across scenarios; (b) by end-use sector in 2050, all scenarios (c) by region, 15C_CCS scenario; (d) by end-use sector, 15C_CCS scenario

Figure 3 :

 3 Figure 3: Global steel production mix

Figure 4 :

 4 Figure 4: Global steelmaking emissions (left), emissions intensity per ton of output (middle) and energy intensity per ton of output (right)

Figure 5 :

 5 Figure 5: Emissions reductions in 2050 compared to 2020 and distribution of decarbonisation options from 2020 to 2050

  period in Ref, which are considerably lower (-54%) than the estimated investments over 2001-2020 of 25

Figure 6 :

 6 Figure 6: World steel production by region, Ref (left), 15C_Opt (middle) and 2050 for all scenarios (right)

Figure 7 :

 7 Figure 7: Global steel trade (left) and regional independence rate (right) in the 15C_Opt scenario

Figure 8 :

 8 Figure 8: Indirect land requirements for different steel processes options

Table 1 :

 1 Parameter matrix for the scenarios in this study

	Climate	CCS	Bioenergy	New	Energy	Steel	Steel plant
	policy	availability	availability	electricity	demand	demand	lifetime
				costs	adaptation	adaptation
	Ref Current	Default	Conservative Pessimistic	Default	Default	Default
	adopted
	policies
	15C_CCS Global	Default	Optimistic	Pessimistic	Default	Default	Reduced
	carbon price
	15C_Elec Global	Delayed	Conservative Optimistic	Default	Default	Reduced
	carbon price
	15C_LD Global	Default	Conservative Pessimistic	Energy	Material	Reduced
	carbon price				conservation	efficiency
					measures	measures
	15C_Opt Global	Default	Optimistic	Optimistic	Energy	Material	Reduced
	carbon price				conservation	efficiency
					measures	measures


domain: chim.cris chim phys

text:
Mehdi Bouchouit

Hadjer Belahlou

Mounia Guergouri

Rafik Bensegueni

Sofiane Bouacida

El-Eulmi Bendeif

Karim Bouchouit

Abdelmalek Bouraiou
email: bouraiou.abdelmalek@umc.edu.dz

Synthesis, characterization and structural study of new Nickel(II) and Mercury (II) complexes with imidazole oxime ligand

Keywords: Imidazole oxime, Complex, Single crystal, Cyclic voltammetry, DFT analysis

In this work, we reported the experimental study of two new complexes named bis[chloro(1methyl-1H-imidazole-2-carbaldehyde oxime) Nickel(II) {Ni(L) 2 Cl 2 } (I) and Di-μ-chlorobis[chloro(1-methyl-1H-imidazole-2-carbaldehyde oxime) mercury(II) {Hg 2 (L) 2 Cl 4 }, DMF (II) based on (1-methylimidazole-2-aldoxime, L). The synthesis, crystallization, and characterization were detailed. Complex I display a hexa-coordinated compound with a distorted octahedral geometry. The Ni II ion is coordinated to two ligands in bidentate mode through nitrogen atoms.

Complex II has one unique Hg II atom and forms a dimeric complex that contains two Hg II atoms related by a crystallographic inversion centre. In this complex, the oxime ligand is ligated to the metal ion in a monodentate fashion through the nitrogen atoms. On the basis of bond distances, this complex exhibits a distorted tetrahedral geometry around each mercury centre metal. The complexes are analyzed using single-crystal diffraction, FT-IR, NMR, and UV-visible. Cyclic voltammetry was also used to understand the electrochemical behaviour of the ligand and both complexes. Furthermore, we have performed DFT calculations to get more about the spectral electronic transitions' nature, reactivity, and electronic structure of the ligand and complexes.

Introduction

Transition metal ions are fundamental components of coordination complexes. Using ligands with several donor atoms is very beneficial since their metal complexes often lead to materials with beneficial physical properties and biological significance and can be exploited in diverse domains [START_REF] Kaim | Bioinorganic Chemistry: Inorganic Elements in the chemistry of Life, an introduction and guide, Inorganic Chemistry: A Textbook series[END_REF][START_REF] Bailar | The Chemistry of the Coordination Compounds[END_REF].

Oxime entities are widely recognized as versatile ligands, and their complexes with various transition metals have been studied since the beginning of the 20th century. The interest in oxime-containing coordination compounds is increasing due to their biological implications.

The realization of their function and the metabolism mechanism in biological systems are connected with chelation by metal ions. Furthermore, oxime species play great importance in analytical chemistry and have been used extensively for detecting and separating transition metals [START_REF] Mehrotra | [END_REF][4].

In general, the reaction of oxime function with metal salts led to four coordination modes which confirm that the chemistry of metal-bonded oximes should be rich (Figure 1). The oxime ligand is potentially ambidentate and can coordinate with metal ions through nitrogen and/or oxygen atom(s), leading to mononuclear, dinuclear and trinuclear metal complexes [5]. Several recent papers have presented various topics, including synthetic pathways leading to oxime complexes and structural and different coordination modes (Figure 2). The binding mode of the oxime function depends not only on the coordinative ability of the oxime group but also on the presence of atom donors in the same molecule [6][7][8][9]. For example, imidazole oximes present several donor atoms in their structures. They are also very appealing ligands for synthesizing coordination compounds. The chelating behaviour of imidazole oxime and the different options for coordination are well established [9]. Fig. 2. Some previously reported oxime complexes [6][7][8][9].

In continuation of interest in the synthesis and characterization of coordination compounds, we report in the present work, the synthesis and structural characterization of nickel (II) and mercury (II) complexes containing 1-methyl-1H-imidazole-2-carbaldehyde oxime as a ligand.

The electronic structure of the ligand and complexes are further studied using the DFT method, and several computational parameters such as E HOMO , E LUMO , ΔE, χ, η, σ and ΔN are calculated. The ligand and the two studied complexes were also characterized by cyclic voltammetry.

Experimental section

Materials and instrumentation

All the reagents and solvents were purchased from Aldrich, Acros Organics, and Merck and used without further purification. The melting point was determined using a Büchi M-565 Automatic Melting Point Apparatus. The NMR spectra of Ligand and complex II were recorded on a Bruker Avance 250 MHz mixture of CDCl 3 and DMSO-d 6 . The UV spectra were recorded on an Optizen 1220 UV/VIS Spectrophotometer and FT-IR spectra were recorded on a Thermo Scientific Nicolet iS50 FTIR spectrophotometer. Single crystals of complexes I and II were grown by crystallizing the crude products in DMF. Crystallographic data for both structures were collected on a Bruker APEX II diffractometer equipped with an Apex II CCD detector using Mo-K radiation (microfocus sealed tube with a graphite monochromator). The crystal was coated with Paratone oil and mounted on loops for data collection. The reported structure was solved by direct methods with SIR2002 [10] to locate all the non-H atoms, which were refined anisotropically with SHELXL97 [11] using fullmatrix least-squares on the F2 procedure from within the WinGX [12] suite of software used to prepare material for publication. All absorption corrections were performed with the SADABS program [START_REF] Sheldrick | SADABS[END_REF]. All non-hydrogen atoms were refined anisotropically. Hydrogen atoms were placed in idealized geometrical positions and refined with Uiso tied to the parent atom with the riding model. A Voltalab PGZ 301 was used in our experiments. The electrochemical study was carried out by cyclic voltammetry (CV) and performed using a three-electrode cell containing a glassy carbon disk (GCE) as the working electrode, a platinum wire as an auxiliary electrode, and a KCl-saturated calomel electrode (SCE). A 0.1 M solution of tetrabutylammonium tetrafluoroborate (Bu 4 NBF 4 ) in dimethylsulfoxide was used as a supporting electrolyte. Before electrochemical measurements, all solutions were deaerated by argon bubbles for about 10 minutes.

Preparation of ligand L, complex I and complex II

Synthesis of -1-methyl-1H-imidazole-2-carbaldehyde oxime (L)

1-methyl-1H-imidazole-2-carbaldehyde oxime (L) has been synthesized following established methods. Spectroscopic results and physical properties agree with literature reports [START_REF] Bebout | [END_REF]. UV-Vis (DMF, λ max /nm): 275; IR (cm -1 ): 3120, 2990, 2920, 1670, 1630, 1520, 1470, 1420, 1280, 1150, 1080, 974, 936, 889, 837, 727, 687; 1 H NMR (250.13 MHz) δ (ppm): 11.46 (s L , 1H), 8.08 (s, 1H), 6.96 (s, 2H), 3.81 (s, 3H); 13 C NMR (62.9 MHz) δ (ppm): 141.1, 140.2, 128.0, 123.7, 34.9.

Synthesis of complexes 2.3.1. Preparation of {Ni(L) 2 Cl 2 } (I)

A mixture of a mixture of 1-methyl-1H-imidazole-2-carbaldehyde oxime (L) (2.0 mmol; 250.2 mg) and NiCl 2 (1.0 mmol; 129.6 mg) was dissolved in 10 ml of EtOH. The mixture was stirred for 18 hours. Solid were collected by filtration and dried in vacuo. Green powder; Yield 60 %; m.p. 229-230°C; UV-Vis (DMF, λ max /nm): 278, 497, 634; IR (cm -1 ): 2983, 1655, 1531, 1477, 1234, 1089, 926, 885, 628. Green crystals of I were obtained from dimethylformamide solution.

Preparation of {Hg 2 (L) 2 Cl 4 }, DMF (II)

Complex II was obtained by a similar method as described for I. A mixture of 1-methyl-1H-was dissolved in 10 ml of EtOH. The mixture was stirred for 18 hours. The obtained solid were collected by filtration and dried in vacuo. White powder; Yield 65 %; m.p.

Results and discussion

Chemistry and spectroscopic characterization

Following the established methods, we have prepared the oxime ligand, and the spectroscopic results and physical properties are in good agreement with the literature reports [START_REF] Bebout | [END_REF]. Complex I was obtained with a 60% yield from the reaction of one equivalent of NiCl 2 and two equivalents of the ligand L in ethanol. The reaction of equimolar amounts of HgCl 2 and the ligand L gave the corresponding complexes II with a 65% of yield (Scheme 1). The ligand and their metal complexes are stable at room temperature, are non-hygroscopic, soluble partially in methanol and ethanol and easily soluble in dimethylformamide. On the other hand, several studies have been made on the infrared spectra of oximes, and most of them have dealt with OH and C=N stretching vibration [15,16]. The simple Ligand (L) spectra exhibit an OH band at 3120 cm -1 , a C=N band at about 1670 cm -1 , and an N-O band at 974 cm -l . The asymmetric and symmetric C-H stretching vibration of the CH 3 group was observed at 2920-2990 cm -1 . Infrared spectra of metal complexes have already been studied intensively, and a metal ion's effects on the vibration frequencies in the ligand have been investigated [17,18]. In the spectra of complex I, the C=N and N-O bands appear at 1655 cm -1 and 926 cm -1 , respectively. However, in the spectra of complex II, the C=N and N-O bands appear at 1655 cm -1 and 968 cm -1 . Compared with the infrared spectra of the free ligand, the IR frequency of both C=N and N-O bonds in complexes I and II were shifted to lower frequencies, and it was shown the existence of coordinate bond formation with metals through the nitrogen atoms of the azomethine groups.

Crystallographic analysis

Crystal structure data for complexes I and II are listed in Table 1. The molecular structures of the two complexes are shown in Figs. 3 and4. Complex I crystallizes in the triclinic space group Pī, and the structure consists of a discrete complex with the formula {Ni(L) 2 Cl 2 } (I). 3a and3b) [19,20]. The two imidazole rings are quasiplanar, with a maximum deviation of -0.0783 (23)Å at the C(5a) atom and -0.0271 (23)Å at the C(5b) atom, and form a dihedral angle between them of 87.25(4)°. The crystal packing can be described as alternating double layers along the b-axis (at b = 1/4 and b = 3/4) parallel to the (011) plane (Fig. 2b). The unit cell interacts with each other through the CH…O hydrogen interaction bond between one hydrogen of imidazole cycle and the oxime oxygen atom with distances between 2.29-2.75 Å and angles between 120 and 156°. These interaction bonds form an infinite, one-dimensional network in one line. Additional intramolecular hydrogen bonds are created through the interaction of the oxime hydrogen atoms with the onecoordinate chlorine. Other bonding parameters are listed in Table 2. 2). These layers then stack along a direction, packed as two layers per unit cell (Fig. 4b). Distances (Å) and angles (°) for Donor-Acceptor in crystals of {Ni(L)2Cl2} (I) and {Hg2(L)2Cl4}, DMF (II).

D-H…A d(D-H) d(H…A) d(D-A) D-H-

Table 3a

Calculated and experimental geometrical parameters (Å, º) of complex {Ni(L)2Cl2} (I)

Table 3b

Calculated and experimental geometrical parameters (Å, º) of complex {Hg2(L)2Cl4}, DMF (II)

Bond Exp. (X-ray) Calc. (DFT) Angles Exp. (X-ray) Calc. (DFT)

The program Crystal Explorer 3.1 [START_REF] Wolff | Crystal Explorer 3.1[END_REF] was used to generate the Hirshfeld surface analysis [START_REF] Jelsch | [END_REF], which has been undertaken better to understand the intermolecular interactions in the crystal structure. The d norm (normalized contact distance) representation mode was used; red spots on the surface indicate the presence of atoms in very close proximity to the outside of the Hirshfeld surface, and the white surface means that the atoms are in medium proximity.

The blue surface shows little proximity of outside atoms. The Hirshfeld surface (Fig. 5 and7)

shows red spots corresponding to H…Cl/Cl…H and H…O/O…H close contacts, which are due to the O-H…Cl, C-H…O and O-H…O hydrogen bonds for both compounds.

The corresponding two-dimensional fingerprint plots [25] were used for decoding and quantifying the intermolecular interactions in the crystal lattice are shown in Figures 6 and8.

The relative contributions of the various intermolecular contacts are illustrated in Fig. 6b and Fig. 8b. In the complex {Ni(L) 2 Cl 2 } (I), the H…H contacts dominate. Its relative contribution to the total surface is 41%. The H…Cl/Cl…H contacts also contribute significantly to the total surface with 26%. Furthermore, the O…H/H…O contacts display a notable proportion of 12%. In complex {Hg 2 (L) 2 Cl 4 }, DMF (II), the H…H and H…Cl/Cl…H contacts dominate;

their relative contributions to the total surface are 35% and 36%, respectively. The H…O/O…H contacts also contribute significantly to the total surface with 13%.

Electrochemical study

The CV measurements were employed to investigate the redox behaviour of the compounds and to estimate the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels of L from the onset of the oxidation and onset of the reduction potentials [26]. CV of L and its complexes were recorded at 10 -3 M in a deoxygenated dimethylsulfoxide (DMSO) solution mixed with 0.1 M of Bu 4 NBF 4 as supporting electrolyte, with a scan rate of 100 mV/s. L, complex {Ni(L) 2 Cl 2 } and complex {Hg 2 (L) 2 Cl 4 } were investigated voltammetrically within the potential window from -1.2 V to +1.2 V, from -0.75 V to +1.5 V and from -2 V to +2 V, respectively. The cyclic voltammogram of L displayed one irreversible anodic peak at 1.06 V and one irreversible cathodic peak at -0.94 V. The anodic peak was assigned to the oxidation of the oxime group, while the cathodic peak was assigned to its reduction (Fig. 9) [27]. The cyclic voltammetry of complex {Ni(L) 2 Cl 2 } showed a decrease in the current intensity of the anodic peak assigned to the oxidation of the oxime group, which confirms its participation in the coordination. In addition, a new redox couple appears with a reduction peak recorded at -0.18V and an associated oxidation peak recorded at 0.563V (Fig. 10). This redox peak was assigned to the Ni +2 /Ni 0 redox couple [28]. We also note the disappearance of the reduction peak of the oxime group, which is probably due to the participation of its nitrogen atom in the coordination. The voltammogram of complex {Hg 2 (L) 2 Cl 4 } shows two redox systems. The cathodic peaks are recorded at -0.86 and -0.53 V, while the corresponding anodic peaks at 0.08 and 0.5 V (Fig. 11), respectively. These systems are associated to the electrochemical response of the couples Hg +2 / Hg +1 and Hg +1 / Hg 0 [29]. Moreover, the oxidation peak of the oxime group is still present, but has lost its intensity (Fig. 11). This behavior confirms that the oxime group do not participate in the complex {Hg 2 (L) 2 Cl 4 } coordination, unlike complex {Ni(L) 2 Cl 2 }.

The presence of the reduction peak of the oxime cannot be confirmed because it overlaps with that of the metal ion. Figure 12 depicts the UV-vis spectra of L and its complexes in the DMSO. The absorption maximum (λ max ) of L, centered at 281 nm, is due to a π-π* transition of the imidazole ring [30]. This absorption shifts to 278 nm for the two complexes, probably because of the ligand energy change after coordination with the metal or for the overlap of a π-π* transition by a charge transfer band resulting from coordination [START_REF] Miessler | Inorganic chemistry[END_REF]. Indeed, in transition metal complexes, the electron distribution exchanges between the metal and the ligand lead to some intense absorption bands called charge transfer (CT) [START_REF] Housecroft | Outer Sphere Mechanism[END_REF] that can appear in the ultraviolet and/or visible portions of the spectrum [START_REF] Miessler | Inorganic chemistry[END_REF]. Moreover, a hyperchromic effect was observed for this band of complex {Ni(L) 2 Cl 2 }. This effect should be related to a metal-ligand charge transfer (MLCT) from Ni II to C=N-O (ligand) [START_REF] Dos Reisa | Orbital: Electron[END_REF]. In the imidazole unit present in the octahedral d 6 Ni-complex which is generally considered to be a moderate s-donor ligand [34,35], electrons can be excited not only from t2g to eg, but also from the ligands s orbitals to eg [START_REF] Miessler | Inorganic chemistry[END_REF]. The s → eg excitation results in a charge transfer transition from the ligand to metal (LMCT) recorded at 427 nm, while the t2g → eg leads to a weak band d-d transition recorded at 641 nm. For complex {Hg 2 (L) 2 Cl 4 }, no d-d transitions were observed in the spectrum as expected for the d 10 Hg II complex.

Theoretical study

The ligand, complex {Ni(L) 2 Cl 2 } and complex Hg 2 (L) 2 Cl 4 } without DMF molecule were modelled using ACD Chemsketch software [START_REF]ACD/ChemSketch[END_REF] and optimized at the DFT/B3LYP/(6-31G + No imaginary frequencies were observed for the three compounds. All DFT calculations were conducted via Gaussian 09W software [START_REF] Gaussian ; Revision | [END_REF]. Visualization and result analysis were performed with the GaussView 5 program [START_REF] Dennington | GaussView, Version 5.0.9[END_REF].

A comparative analysis of the essential bond lengths and angles was realized for both complexes (Tables 3a,3b). It shows a good harmony between calculated and experimental data globally. Frontier orbitals, LUMO and HOMO, are very helpful for the molecular reactivity and stability prediction of one given compound. These orbitals were plotted, and their energies were calculated for the three studied molecules (Figure 13). Frontier orbital analysis showed that they are homogeneously deployed for the ligand. However, HOMO of the complex {Ni(L) 2 Cl 2 } was localized on the chlorine and nickel atoms, while LUMO was located on one half of its structure. For the complex {Hg 2 (L) 2 Cl 4 }, both HOMO and LUMO were mainly situated on the imidazole oxime ligand.

The value of the gap (the energy difference between the frontier orbitals) for the ligand and its two complexes is comparable, showing similar stability. However, the smallest value corresponds to complex {Ni(L) 2 Cl 2 }, thus reflecting a slightly higher reactivity for the latter.

Frontier orbitals' energies are used to calculate the global reactivity descriptors for the studied compounds (Table 6). Time-dependent density functional theory (TD-DFT) computation was also carried out at the same level of the theory in DMSO using the IEF-PCM salvation model, to predict the UV-Vis electronic absorption spectra of our complexes. The major transition and the oscillator strength (f), as well as the energy of the main bonds, were also explored (Table 5).

Conclusion

In summary, we have synthesized two new mononuclear compounds from 1-methyl-1H-

Fig. 1 .

 1 Fig. 1. The crystallographically established coordination modes of oxime and oximato groups.

Scheme 1 .

 1 Scheme 1. Synthesis of complexes I and II. Both complexes were characterized by spectroscopic methods, and their structures were confirmed by X-ray crystal structure analysis. The 1 H NMR spectra of the ligand (L) shows two signals at δ = 11.46 and δ = 8.08 ppm, which are assigned to NOH and HC=N protons, respectively (Scheme 1). The signal at δ = 6.96 ppm is assigned to two imidazole protons ring. Also, the signal at 3.81 ppm corresponds to protons of the methyl group. Complex II has similar spectra. Signals observed around 11.95 and 8.33 ppm are assigned to the NOH and HC=N protons. A signal at 7.15 ppm corresponds to imidazole protons. The signal at 3.82 ppm is due to protons of the methyl group. The 13 C NMR spectrum of the free ligand (L) shows a signal at 141.1 ppm, which may be assigned to azomethine carbon. While the resonance signals observed in the region 140.2-123.7 ppm were assigned to carbon atoms of the imidazole ring. In complex II, two aromatic signals have undergone a downfield shift and

  The NiII cation occupies an inversion center and is coordinated by two chlorine ligands and two bidentate imidazole oximes through four N atoms. It creates a coordination sphere that possesses a distorted octahedral geometry. The Ni-N(1a), Ni-N(3a), Ni-N(1b), and Ni-N(3b) bond distances are 2.0830(18), 2.0635(18), 2.0836(17), and 2.0546(7) Å, respectively. The Cl(2)-Ni-N(1b), Cl(1)-Ni-N(3b), N(1a)-Ni-N(3a), Cl(1)-Ni-N(3a), and N(1a)-Ni-N(3b) bond angle values of 164.02(5), 98.92(5), 77.81(7), 86.98(5), and 96.20(7)° support the distorted geometry of the molecular structure. The bond distances and angle values are in the normal ranges and are comparable to the corresponding bonds found in similar Nickel oxime Complexes (Table

Fig. 3 .

 3 Fig.3. Mercury plots of the molecular structure, hydrogen bonding interactions and crystal packing of I. (a) The molecular structure of I showing the atom numbering scheme. (b) Projection along the b-axis of the atomic arrangement of I showing CH…O intermolecular hydrogen bonds in dashed red lines.

Fig. 4 .

 4 Fig.4. Mercury plots of the molecular structure, hydrogen bonding interactions and crystal packing of II. (a) The molecular structure of II showing the atom numbering scheme. (b) Projection along a axis of the atomic arrangement of II. Dotted lines represent the hydrogen bond.

Fig. 5 .

 5 Fig.5. View of the 3D Hirshfeld surface mapped over dnorm of complex {Ni(L)2Cl2} (I).

Fig. 6 .

 6 Fig. 6. (a) The 2D fingerprint plot. (b) Relative contributions to the Hirshfeld surface area for the various close intermolecular contacts in complex {Ni(L)2Cl2} (I).

Fig. 7 .

 7 Fig. 7. View of the 3D Hirshfeld surface mapped over dnorm of complex {Hg2(L)2Cl4}, DMF (II).

Fig. 8 .

 8 Fig. 8. (a) The 2D fingerprint plot. (b) Relative contributions to the Hirshfeld surface area for the various close intermolecular contacts in complex {Hg2(L)2Cl4}, DMF (II).

Fig. 9 .

 9 Fig.9. Cyclic voltammogramm recorded with 5.0×10 -3 M of Ligand in Bu4NBF4 (0.1M)/DMSO solution at a scan rate of 100 mV/s.

Fig. 10 .

 10 Fig.10. Cyclic voltammogramm recorded with 5.0×10 -3 M of complex {Ni(L)2Cl2} in Bu4NBF4 (0.1M)/DMSO solution at a scan rate of 100 mV/s.

Fig. 11 .

 11 Fig.11. Cyclic voltammogramm recorded with 5.0×10 -3 M of complex {Hg2(L)2Cl4} in Bu4NBF4 (0.1M)/DMSO solution at a scan rate of 100 mV/s.

Fig. 12 .

 12 Fig.12. UV-vis absorption spectra of L (black), complex {Ni(L)2Cl2} (I) (red) and complex{Hg2(L)2Cl4} (blue).

  (d) for C, H, O, N, Cl atoms and the Stuttgart/Dresden effective core potentials basis set (SDD) for Ni and Hg atoms) level in the gas phase. The Stuttgart-Dresden effective core potential ECPs (SDD) were used for metal atoms. The same level of theory was used for vibrational frequency calculations to confirm that the optimized geometries were reel minima.

Fig. 13 .

 13 Fig.13. Frontier orbitals' contour surfaces and energies for the ligand, complex {Ni(L)2Cl2} and complex {Hg2(L)2Cl4}.

imidazole- 2 -

 2 carbaldehyde oxime ligand with MII cations, namely {Ni(L)2(Cl)2}, (I) and {Hg2(L)2Cl4}, DMF (II). Spectroscopic methods characterized both complexes and their structures were confirmed by X-ray crystal structure analysis. The d8 configuration of the NiII cation and the d10 configuration of the HgII allow the self-assembly of MII with the organic ligand in a distorted octahedral geometry for complex (I) and distorted tetrahedral geometry for complex (II). Hydrogen-bonding interactions help to consolidate both crystal structures. The Hirshfeld surface shows the domination of H…Cl/Cl…H and H…O/O…H contacts which are due to the O-H…Cl, C-H…O and O-H…O hydrogen bonds for both compounds. Cyclic voltammetry was used to investigate the chemical reactivity of the studied compounds, confirming the participation of the oxime group in the coordination in the case of the complex {Ni(L)2Cl2}. DFT calculations exhibited comparable experimental and theoretical findings. Exploration of the natural spectral electronic transitions showed a HOMO → LUMO transition for the ligand, while other orbitals are involved for complex {Ni(L)2Cl2}and complex{Hg2(L)2Cl4}.

Table 1

 1 Crystal

structure data for complexes (I) and (II).

{Ni(L)

2 Cl 2 } (I) {Hg 2 (L) 2 Cl 4 }, DMF (II)

	Formula	C10H14Cl2N6NiO2	C16H28Cl4N8O4Hg2
	Formula weight	379.88	939.44
	Crystal habit, color	Prism, green	Prism, Colorless
	Crystal system	Triclinic	Monoclinic
	Space group	Pī	P 2/c
	a (Å)	7.1357(2)	9.0323(5)
	b (Å)	8.5591(2)	13.0416(7)
	c (Å)	12.7830(3)	14.0145(7)
	α (º)	86.948(1)	90
	β (º)	78.548 (1)	120.555(3)
	γ (º)	84.218(1)	90
	Volume (Å 3 )	760.85(3)	1421.61(14)
	Z, Z'	2, 2	2, 4
	Density (calculated, g cm -3 )	1.658	2.195
	Absorption coefficient (mm -1 )	1.64	11.20
	F(000)	388	880
	Crystal size (mm)	0.12×0.10×0.08	0.16×0.08×0.05
	θ range for data collection (º)	2.4-27.8	2.3-26.1
	Reflections collected	12093	30724
	Independent reflections	3407	4730
	Rint	0.0242	0.04
	Reflections with I ≥ 2σ(I)	2653	2999
	Number of parameters	194	157
	Goodness-of-fit on F 2	1.03	1.05
	Final R indices [I ≥ 2σ(I)]	0.028	0.0333
	R indices [all data]	R1=0.03, wR2=0.068	R1=0.037, wR2=0.116
	Largest difference peak and hole (Å -3 )	0.29, -0.28	1.46, -0.91
	CCDC deposition no.	CCDC 1478614	CCDC 1478614

Table 2

 2

Table 4

 4 Electrochemical HOMO, LUMO and band-gap values of Ligand. Energy of the band-gap calculated from the difference between the energy of the HOMO and the LUMO band.

		E ox	onset (V) a E red onset (V) b	E HO MO (eV) c	E LUMO (eV) d	E g el (eV) e
	Ligand	0.78	-0.73	-5.18	-3.67	1.51
	a The onset potential of oxidation . b The onset potential of reduction. c E HOMO (eV) = -[E ox	onset (vs. SCE) + 4.4]. d E LUMO (eV) = -[E red	onset
	(vs. SCE) + 4.4]. e

Table 5

 5 Major transition, energy and oscillator strength (f) of the main bonds of the three compounds' UV-Vis spectra,

calculated in DMSO (IEF-PCM) at the DFT/B3LYP/(6-31G + (d) for C, H, O, N, Cl atoms and SDD for Ni and Hg atoms) level. λ exp. (nm) λ theo. (nm) E(

eV) f Main transition Contribution

	Ligand	281	271.51	4.5664 0.5118	HOMO → LUMO	98.25
	Complex {Ni(L)2Cl2}	279	293.20	4.2287 0.3265 HOMO → LUMO+3	39.49%
	Complex {Hg2(L)2Cl4}	278	287.78	4.3083 0.2099 HOMO → LUMO+2	64.77%

Table 6

 6 Global reactivity descriptors calculated in vacuum (DFT/B3LYP/Genecp) for the ligand, complex {Ni(L)2Cl2} (I) and complex {Hg2(L)2Cl4} (I

)I. Parameters (eV) Ligand Complex {Ni(L) 2 Cl 2 } Complex {Hg 2 (L) 2 Cl 4 }

	EHOMO	-5.8181	-6.1759	-6.5969
	ELUMO	-1.0531	-2.4588	-1.9467
	Energy band gap |ELUMO -EHOMO|	4.7650	3.7171	4.6502
	Ionization potential (I = -EHOMO)	5.8181	6.1759	6.5969
	Electron affinity (A = -ELUMO)	1.0531	2.4588	1.9467
	Chemical hardness (η = (I-A)/2)	2.3825	1.8585	2.3251
	Chemical softness (ζ = ½ η)	1.1912	0.2690	0.2150
	Electronegativity (χ = (I+A)/2)	3.4356	4.3174	4.2718
	Chemical potential (μ = -χ)	-3.4356	-4.3174	-4.2718
	Electrophilicity (ω = μ²/2η)	2.4771	5.0146	3.9242
	Maximum charge transfer index (ΔNmax. = -μ/η) 1.4420	2.3230	1.8373

Acknowledgements:

We thank the Ministère de l'Enseignement Supérieur et de la Recherche Scientifique -Algérie (MESRS) for financial support. We thank the HPC Center of Constantine 1 University for providing the computing infrastructure used in our calculations.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement


Noury Bouraqadi

Eric Duviella

Luc Fabresse

Guillaume Lozenguez

Lala Rajaoarisoa

Mobile Robots for Qualitative and Quantitative Management of Water Resources: Opportunities and Challenges

Keywords: Water Resources, Mobile Robots, Large-Scale Systems

This paper aims at presenting a reflection on possible contributions of robots to answer human's needs in matter of qualitative and quantitative management of water resources. We describe challenges and advocate the use of mobile robots. We list requirements for a robot-based water resource management. These requirements are the foundation of a framework we have introduced to build solutions addressing the targeted problem.

INTRODUCTION

Context and Motivation

The water resource is known for its scarcity and for its precious value. It is impacted by various sources of pollution, often linked to human activity. In addition, extreme climatic events such as floods worsen these situations by discharging contaminated water into the natural environment, or by causing damages to human's economic activity and property.

In this context, it becomes crucial to preserve water resource by improving and designing management strategies both from a qualitative and a quantitative point of view. To contribute to these objectives, water resource management requires a better knowledge of the dynamics of water systems. Besides, it needs a better and quicker estimation of their quantitative and qualitative states. Mitigation solutions based on management strategies, are also necessary, particularly in crisis situations. Previous objectives require new solutions that address the 3 facets of water resources management:

Surveillance Detect abnormal situations (e.g. pollution, extreme water level), and diagnose what is going wrong (e.g. pollution source, faulty mode of a pump or other hydraulic device). Supervision Observe the evolution of some phenomena, and possibly deduce a prognostic of the evolution, e.g. how pollution will diffuse. Remediation Control Strategy to address a undesirable situation, e.g. clean up an oil spill, collect floating garbage, open gates of an expansion area, run auxiliary pumps.

Challenges

Achieving surveillance, supervision and remediation of water resources, first requires sensors to collect data. Collected data will then help taking appropriate actions such as activating some actuators (e.g. pumps) and give feedback on the impact of these actions. However, collecting data and controlling actuators is tedious and slow if done by human operators. To be effective, a solution should provide regular and precise measurements (high resolution). Hence, automation is compelling but it is also challenging because of the scale of hydrographical networks or water bodies and their limited accessibility. A trivial solution would be to set up a dense network of sensors or actuators but this is not realistic. Realistic solutions are a tradeoff between the economical costs, the ecological impact and the system's performance such as the resolution of measurements.

Robot-based Management of Water Resources

In this paper, we advocate that mobile robots allow for a flexible, economically and ecologically viable solution for managing water resources. Indeed, a fleet of robots can operate as a sensor network. But contrary to a fixed network, robots are mobile and can organize themselves to maximize performance given the conditions (e.g. topology, weather). Indeed, each robot can be equipped with different sensors and actuators to collect data and perform some tasks at desired positions. They can also collectively build maps with data of interest, by moving around until covering a whole area. The resolution of these maps can be adjusted by simply adjusting the distance between robots. For example, robots might move in a row while searching for the source of a pollution. Then, when they find it, they can collectively limit the diffusion of the toxic substance by organizing themself in circle around it.

Mobile robots provide support for designing cheaper, more flexible and ecologically respectful water management systems. However, they should be part of a larger framework that target all kind of problems related to water resources management, including, but not limited to localizing pollution sources (?) and collecting waste and pollutants (?).

REQUIREMENTS FOR A ROBOT-BASED SYSTEM FOR MANAGING WATER RESOURCES

Mission

Designing a system to manage water resources requires to define what is its mission and how to achieve it. According to Section 1.1, a mission is related to one or more facets namely: Surveillance, Supervision or Remediation.

The target of a mission (what part) is the physical phenomenon that is observed or act upon. Examples are: water level, PH, floating waste, phytoplankton, and conductivity.

The mission strategy (how part) can be to keep the physical phenomenon close to an objective value, or patrolling over an extended period of time. The following section 2.2 discusses how a mission strategy decomposes into tactics addressing different aspects of the solution.

Tactics to Implement a Mission Strategy

To implement a mission strategy, the first step is to choose the appropriate kind of hydraulic devices and robots. This means choosing a hardware, with sensors and actuators that best fit the mission. Although in theory, one can choose hydraulic devices for a given mission, in practice, this is often not an option, mainly because these are big and expensive infrastructural projects, requiring political decisions. This is why, choosing hardware boils down to picking the appropriate robots.

For instance, consider surveillance or supervision missions of inland waterways. In this example, surface aquatic robots are more appropriate to accurately measure the amount of deposited sediments, or to collect floating waste. Other kinds of hardware are more suitable for other missions, such as underwater robots for measuring water pressure or PH, at different depths. A review on aquatic unmanned vehicles is proposed in (?), and in (?) for marine applications.

The number of robots is also a parameter that defines a tactic to address a mission. A solution can rely on a single robot, or a fleet of two or more robots. For example, multiple robotic fish are used in (?), to collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. Surface multirobots have been designed in (?) to measure the density of the plankton in water. Yet another example is a fleet of autonomous floating robots tracking the fate and the trajectory of oil spill (?), or collaborate to recover oil with the help of oil skimmers (?).

Robots involved in a water resource management mission can be either tele-operated by a remote human operator or autonomous. That is they can performed assigned tasks, without human intervention. Hence, the level of robots autonomy is also a question to consider when implementing a mission strategy.

To achieve a mission, robots need to go to different locations to collect data, or perform some action (e.g. gather waste). This is why mission managers need to choose a navigation algorithm that plans 2D or 3D paths that fit the mission. This algorithm should ensure obstacle avoidance in case of robots that are fully or partially autonomous. In (??), the designed robotic fish can follow a given track and change its direction to avoid collision with obstacles.

Another point concerns communications at two levels. On the one hand, autonomous robots operating in a fleet, need to communicate to coordinate their operations. On the other hand, robots might need to send collected data to a base station. These data will inform a decision system (automated or with humans in the loop). Communication can also be directed from the base station to robots. The base station will then issue orders and assign tasks to robots (e.g. where to go next).

Water System Simulator Water Resource Management Strategy

Water System

FRAMEWORK FOR WATER RESOURCE MANAGEMENT

We propose a framework for the robot-based management of water resources (cf. Figure 1). The water resource management strategy block (in green) defines strategies for surveillance, supervision and remediation to mitigate the impact of undesirable situations. Strategies require data provided by both hydraulic devices (arrow 3 in Figure 1) and robots (arrow 6 ). A simulator of the water system can be used to optimize the strategy 1 . This simulator allows reproducing accurately the dynamics of the water system and predict its behaviour 2 . Data collected in real-time by hydraulic devices 4 and robots 5 can feed the simulator for better results. The strategy will inform the robotic system about data to collect, where and how to perform measurements. Eventually, the strategy will issue commands to hydraulic devices 7 and robots 8 to mitigate undesirable situations.

A review of the literature, shows some work related to this proposed framework. Bayat et al. (?) did a survey gathering multirobot solutions and algorithms for monitoring water pollution. This is a very interesting source for tactics to implement the robotic part of a system based on our framework. However, this paper does not deal with remediation solutions. It is also restricted to water pollution and does not study other phenomena such as floods or drought.

Another source of solutions to implement the robotic part of our framework is the application agnostic survey proposed by (?). It deals with task decomposition, coalition formation, task allocation, perception, and multi-agent planning and control.

Fernandez et al. (?) presents an overview of the work carried out by his group of robotic about cooperation between mobile robots and sensor networks, for different environmental applications. They propose an integrated platform for integrating mobile robots and sensors with a pre-existing infrastructure coping with heterogeneous and static devices, offering adaptive and optimal management strategies. Here again, it is a very interesting proposal for the deployment, operation and maintenance of large-scale sensor networks. However, this paper does not deal with remediation solutions, and does not consider directly management of water resources.

CONCLUSION

Designing better management systems for water resources is crucial nowadays. In this context, this paper presents some requirements for both qualitative and quantitative management of water resources. We advocate that using mobile robots can improve management strategies on these two levels. This article also introduces a framework to build solutions targeting various applications in this area. We pave the way for our effort to explore new solutions to water resource management.

Figure 1 .

 1 Figure 1. Framework for water resource management.