Gabriel Bekö

Nicola Carslaw

Patrik Fauser

Violeta Kauneliene

Sascha Nehr

Gavin Phillips

Dikaia Saraga

Coralie Schoemaecker

Aneta Wierzbicka

Xavier Querol

The past, present, and future of indoor air chemistry

Keywords: Indoor air chemistry, Building materials, Occupants, Microbial activity, Modeling

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Particles

"In developed countries, we spend 80-90% of our time indoors" is the opening sentence for most grant applications and publications in our field. But has this well-worn truism lost its impact? We know that the majority of our exposure to air pollution occurs indoors, so why has indoor air quality not received the attention it deserves and how do we as a community better communicate this message? Recently, there has been increasing interest from experts from a wide range of backgrounds, including outdoor air quality scientists. They have enhanced our community, sparking a rapid evolution in the measurement technology used indoors, and the number, diversity and novelty of findings.

The INDoor AIR POLLution NETwork (INDAIRPOLLNET) was recently supported by the European Cooperation in Science and Technology (COST), following submission of a proposal that began with the sentence highlighted above. It consists of ~200 participants from 38 countries, comprising both scientists and practitioners in chemistry, biology, aerosol characterization, toxicology, exposure, emissions and chemical risk assessments, material design, building physics, civil engineering and standardization. Over a four-year period, INDAIRPOLLNET will address the current state of indoor air pollution, with emphasis on indoor air chemistry (IAC), including the associated research needs, challenges, and ways to address them. A liaison with the International Organization for Standardization (ISO) will facilitate the transfer of this scientific knowledge to practice.

Increasing climate change awareness is driving rigorous energy efficiency measures with buildings becoming more airtight, though adverse health effects can be associated with lower ventilation rates. [START_REF] Sundell | Ventilation rates and health: multidisciplinary review of the scientific literature[END_REF] Air pollutant concentrations are often higher indoors than outdoors, particularly following activities such as cleaning, cooking and smoking. [START_REF] Nazaroff | Indoor chemistry: research opportunities and challenges[END_REF] More than two million healthy life years are lost across Europe because of indoor air pollution, including indoor exposure to outdoor pollutants, indoor combustion sources, moisture, and emissions from building materials and consumer products. [START_REF] De Oliveira Fernandes | Coordination action on indoor air quality and health effects[END_REF] There are vast differences in building types and uses, occupant behavior, geographical locations, ventilation systems and indoor and outdoor sources. Chemical processes indoors and their relation to those occurring outdoors must be well understood, in order to extrapolate results to a wider range of buildings and locations than in the relatively few, in which measurements have been made.

In its first year, INDAIRPOLLNET used seven subgroups to mine recent literature to summarize what existing measurement and model studies reveal about IAC. The research priorities from the subgroups are now presented.

Chemical transformations

Indoor chemical processes have been identified by reviewing laboratory, field and modelling studies both indoors and, where relevant, outdoors. The relative importance of chemical processes indoors differs to outdoors because of higher surface-to-volume ratios, and differences in pollutant sources and dispersion, light characteristics and temperature and humidity profiles. [START_REF] Nehr | Emerging developments in the standardized chemical characterization of indoor air quality[END_REF][START_REF] Weschler | Indoor Chemistry[END_REF] The following priorities were identified:

-Assess the potential for indoor chemistry in the gas phase, with emphasis on: o Role of photolysis (via attenuated solar and artificial light sources), o comparison of the relative importance of OH, Cl, NO 3 , O 3 as oxidants, the conditions where each dominates and routes to formation, o contribution of different reaction pathways to the formation of secondary products, o competition between chemical reactions and ventilation rate.

-Advance knowledge on the abundance of organic trace constituents in indoor air and the identification of new constituents through total OH reactivity measurements compared with measurable individual trace gas measurements. -Investigate the impact of solid and liquid phase processes indoors (e.g. reactivity in the liquid phase indoors, including acid-base chemistry). -Parameterize heterogeneous processes (production yields of secondary species such as aldehydes and HONO) for representative indoor surfaces and in real environments.

Building materials, household products and occupant behavior

Building and household products (e.g. wood based building materials, paints, varnishes, cleaning and personal care products, air fresheners, combustion appliances, electronic appliances, furniture, carpets, toys) as well as occupant activities (e.g. cooking, smoking, cleaning) can contribute significantly to indoor air pollution. A comparison of 13 labelling schemes for construction products worldwide has identified 15 lists of target compounds, with 611 individual chemicals occurring on at least one of the lists. [START_REF] Brown | Assessing and controlling risks from the emission of organic chemicals from construction products into indoor environments[END_REF] Indoor surfaces may act as both sources and sinks of gasphase air pollutants; there is increasing interest in secondary pollutant emissions following surface interactions indoors. [START_REF] Weschler | Indoor Chemistry[END_REF][START_REF] Kruza | Impact of surface ozone interactions on indoor air chemistry: A modeling study[END_REF] The following are the identified research priorities:

-Improve the characterization of pollutant emissions, deposition and chemical transformations on various indoor surfaces for the identification and development of building and furnishing materials for better indoor air quality. -Design field studies with a particular focus on the role of semivolatile organic compounds (SVOCs) and their reaction products in surface chemistry and related health effects.

-Disentangle the role played by humidity (potential impact on aqueous chemistry) versus reactive species (e.g. chlorine) in surface reactivity. -Study the impact of building construction, location and operation on IAC.

Occupants

Humans emit a range of organic compounds from sweat and sebaceous secretions from skin, breath, and intestinal gases. The compounds associated with the presence of humans contribute 40-57% of the volatile organic compound (VOC) concentrations indoors in daytime. [START_REF] Liu | Contribution of human-related sources to indoor volatile organic compounds in a university classroom[END_REF][START_REF] Tang | Volatile organic compound emissions from humans indoors[END_REF] Studies on occupantrelated chemical transformations have mainly focused on ozonolysis of squalene, a major skin oil constituent. [START_REF] Weschler | Roles of the human occupant in indoor chemistry[END_REF] The following research priorities were identified:

-Investigate the personal and environmental factors that influence human emissions (e.g. diet, stress level, personal hygiene, age, sex, health condition, activity level, personal care products, clothing and its laundering).

-Study the inter-and intrapersonal variability and the influence of prior exposure on human dermal and oral emissions and their reactive capacity. -Perform real-time measurements of OH, NO 3 , Criegee intermediates and other short-lived, highly reactive species in occupied and unoccupied indoor environments. -Investigate the influence of occupancy on indoor surfaces as well as on the composition of airborne particles.

Microbial activity

Microbial life is ubiquitous within buildings [START_REF] Kelley | Studying the microbiology of the indoor environment[END_REF] and is a frequent cause of indoor air quality problems and health effects. [START_REF] Claeson | Effects on perceived air quality and symptoms of exposure to microbially produced metabolites and compounds emitted from damp building materials[END_REF] Microbial colonies such as molds produce a wide variety of VOCs through their metabolisms, which encompass a range of functional moieties. [START_REF] Misztal | Emission factors of microbial volatile organic compounds from environmental bacteria and fungi[END_REF] The behavior of analogous microbes in the ambient atmosphere suggests that their indoor counterparts are chemically and photochemically active and are likely to affect chemistries both in the gas phase and on surfaces, including on aerosols. Future research priorities include:

-Determine the relative VOC load from microbial activity and the impact on IAC in an "ordinary dwelling" compared to a "problem dwelling" with similar occupancy. -Determine the influence of species variety indoors and of the balance between the building biome and the occupant biome on indoor air chemistry. -Investigate the levels of toxic volatile emissions resulting from microbial processing of widely-used building materials. -Coordinate a network of well-characterized test facilities with a minimum set of controlled variables in conjunction with a coupled indoor chemistry/dynamics model framework, in order to address the potential variability due to geographic differences in species, indoor environments and emission factors. -Individually study microbial activity by the use of standardized coupons whereby known quantities of microbes are introduced into well-characterized realistic environments and the marginal effects of the microbial activity is measured.

Particles

Airborne particles form an integral part of IAC, as dynamic changes between gas and particle phase take place continuously. [START_REF] Lucattini | A review of semivolatile organic compounds (SVOCs) in the indoor environment: occurrence in consumer products, indoor air and dust[END_REF] Particles in indoor air are influenced by both physical and chemical processes, which change their physical characteristics, chemical composition and concentrations. [START_REF] Morawska | Indoor Aerosols: From Personal Exposure to Risk Assessment -Review[END_REF] Recent studies based on real time aerosol mass spectrometry have brought novel understanding of chemical transformations taking place indoors. Future studies are recommended in the following domains:

-Study the factors influencing SVOC uptake on particles (e.g. chemical composition, number size distribution, surface area, environmental parameters). -Determine size resolved particle-and gas phase chemical emission factors and mass spectra/signatures for specific indoor sources under controlled laboratory settings with parallel use of proton-transfer-reaction mass spectrometry and aerosol mass spectrometry. -Assess the oxidative reactivity of particles from specific sources and mixtures under laboratory conditions and compare with measurements in occupied real indoor environments.

-Study the change of physicochemical characteristics of particles upon infiltration and the transformations that occur when outdoor and indoor air pollutants in the gas-and particlephase interact.

Source apportionment

Receptor models apportion the measured mass of an atmospheric pollutant at a given site (receptor), to its emission sources by using multivariate analysis to solve a mass balance equation. [START_REF] Belis | European guide on air pollution source apportionment with receptor models[END_REF] Positive Matrix Factorization (PMF), Chemical Mass Balance (CMB) and Principal Component Analysis (PCA) are among the receptor models most frequently used. While the major sources of indoor air pollution have been identified, few studies have attempted to estimate the contribution of specific sources using such techniques, mainly because the presence of both indoor and outdoor sources, building-related mechanisms (e.g. ventilation, infiltration), as well as outdoor meteorology and long-range transport of pollutants makes source apportionment challenging. Key research areas that warrant attention are:

-The influence of IAC on source apportionment model applications, including the validity of assumptions and relevant constraints (e.g. unstable source profiles over time). -The dependence of receptor modeling applications on available decay rates, air exchange rates and penetration factors. -Improved estimates of the outdoor contribution in source apportionment of indoor pollutants. -Perform source apportionment studies based on datasets obtained with real time measurements with very short time resolution (e.g. aerosol mass spectrometer, PTR-MS). -Define basic guidelines for source apportionment in indoor environments and address reliability issues of online source apportionments based on low-cost sensor networks.

Modelling

Indoor air pollutant measurement techniques are still unable to measure many pollutants at sufficient temporal frequency and with the required specificity in a wide enough range of buildings, to provide a broad and representative understanding of chemistry indoors. The development and use of indoor air models is, therefore, a substantial requirement for understanding IAC. [START_REF] Morrison | A Modelling enterprise for chemistry of indoor environments (CIE)[END_REF] Indoor air chemistry models need to include the important sources and sinks of pollutants within a building envelope, such as chemical reactions, material emissions and surface interactions, human activity, exchange of pollutants with outdoors, or transport of pollutants within/between different zones of a building. A number of challenges for modelling studies have been identified:

-Field experiments in real buildings, especially those with real-time measurements and real/simulated activities, are necessary to validate and improve models.

-Models often assume well-mixed air in buildings, when spatial variation within zones should be considered. -Indoor air chemistry models typically use chemical mechanisms originally constructed for modelling outdoor chemistry and may lack appropriate degradation schemes. -Current models typically include estimated photolysis rates indoors (although some measurements have become available recently [START_REF] Kowal | Wavelength-resolved photon fluxes of indoor light sources: Implications for HOx production[END_REF][START_REF] Blocquet | Impact of the spectral and spatial properties of natural light on indoor gas-phase chemistry: Experimental and modeling study[END_REF] ) and often fail to consider the propagation of light from the windows throughout the indoor space. -Modelers and experimentalists must work together; models help design experiments and the experimental results can be used to improve models.

The field of indoor air chemistry is moving forward rapidly, accelerated to a great extent by the Alfred P. Sloan Foundation's Chemistry of Indoor Environment program (whose website's homepage vividly flashes the "90%-sentence"). From the lab to the field, from test houses to climate chambers, from extraordinary campaigns such as HOMEChem [START_REF] Farmer | Overview of HOMEChem: House observations of microbial and environmental chemistry[END_REF] to modeling efforts like the international MOCCIE consortium, invaluable data about indoor air chemistry and physics is being swiftly generated. Indoor air chemistry occupies an increasing share in the programs of the Indoor Air conference series and at meetings such as the recent joint conference of The International Societies of Exposure Science (ISES) and Indoor Air Quality and Climate (ISIAQ) in Kaunas, Lithuania. But as it often is the case in science, new answers generate new questions and we seem to have lots of them. Such new questions are to be welcomed. As Albert Einstein noted, "To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advance in science". As we continue to address the unknowns of indoor air chemistry and allow our scientific curiosity to generate further insights, we should remember that we ultimately strive not only for understanding, but especially for healthier indoor environments.

Acknowledgments

We thank those participants of INDAIRPOLLNET's Working Group 1, who actively contributed to the work, which resulted in this editorial. These participants are Elena Gomez Alvarez, Noel J. Aquilina, Steigvile Bycenkiene, Nuno Canha, Regina Duarte, Emer Duffy, Sebastien Dusanter, Renata Kovacevic, Mila Ródenas García, Pawel Misztal, Aleksandar Petrovski, Ana Maria Scutaru, Milena Jovasevic-Stojanovic, Kristina Plauškaitė -Šukienė, Teresa Vera, Lenka Wimmerová. The authors gratefully acknowledge the support of the European Cooperation in Science and Technology (COST).


Paul Hazemann

Dominique Decottignies

Sylvie Maury

Séverine Humbert

Adrien Berliet

Cécile Daniel

Yves Schuurman
email: yves.schuurman@ircelyon.univ-lyon1.fr

Kinetic data acquisition in high-throughput Fischer-Tropsch experimentation

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Introduction

Fischer-Tropsch synthesis (FTS) is a promising route for clean fuels 1 and sustainable chemicals production via the XtL process, in which biomass (BtL), coal (CtL) or natural gas (GtL) can be used as raw materials. Efficient Fischer-Tropsch catalysts are crucial to make the process economically viable and competitive with conventional petroleum-based routes. However, the evaluation of new catalyst performances can be time consuming and costly 2 . To overcome this issue, two strategies can be employed: the first one is the use of fast characterization techniques, for which the analysed properties are directly linked to catalysts performances. For instance, Boldrin et al. 3 proposed an efficient way to accelerate Fischer-Tropsch catalysts screening using High-Throughput TGA and XRD. The second one, chosen by more and more research groups [4][START_REF] Botes | Catalysis and synthetic fuels: state of the art and outlook[END_REF][START_REF] Sartipi | [END_REF][7][8][9] , is the use of parallelized and miniaturized reactor setups, more commonly known as high-throughput experimentation (HTE). Van der Waal et al. [START_REF] Van Der Waal | Catalysis and synthetic fuels: state of the art and outlook[END_REF] demonstrated the potential of such a technology for FTS by characterizing the coherence of the data generated with 64 fixed-bed reactors operated in parallel. A high repeatability of the catalysts performances between reactors was obtained (95%), and the selectivity measured by on-line gas chromatography was in good agreement with the off-line liquid phase analysis. A strong repeatability of reactor to reactor data was also obtained by Cao et al. [START_REF] Cao | Catalysis and synthetic fuels: state of the art and outlook[END_REF] on a multi-channel microtubular reactor setup, with a similar standard deviation. Because of its high accuracy, the HTE technology is recognized as a powerful tool to acquire a large amount of data in a short period of time, making it suitable for catalysts screening, kinetic modelling and mechanistic understanding 2,[START_REF] Van Der Waal | Catalysis and synthetic fuels: state of the art and outlook[END_REF][START_REF] Moulijn | [END_REF]13 . Several issues are, however, important to consider when operating such experimental setups, in order not to compare incoherent data and make biased conclusions. For instance, Van der Waal 10 and Cao [START_REF] Cao | Catalysis and synthetic fuels: state of the art and outlook[END_REF] both underlined the necessity to use sufficiently small reactor diameter to avoid heat and mass transfer limitations (< 2 mm 10 ), which are particularly important when data is acquired for kinetic studies. Small reactor diameter have also been recommended by Chambrey et al. 14 to better control temperature during the a. IFP Energies nouvelles, Rond-Point de l'Echangeur de Solaize 69360, France b. Univ Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON, F-69626, Villeurbanne, France reactor start up. Because of the high exothermicity 15,[START_REF] Mark | Handbook of Heterogeneous Catalysis[END_REF] of the Fischer-Tropsch reaction 9G* r = -167 kJ mol -1 for CH 2 monomer formation 15 ), the first exposition of the catalyst to syngas may indeed lead to an uncontrolled temperature increase and provoke undesired deactivation. This is a recurring issue in conventional fixed bed reactors, for which radial heat transfer is low. It can be avoided in HTE, by the use of milli-fixed bed reactors.

The accuracy of the data obtained by HTE might also depend on other factors, where the test methodology plays a major role. Xiao et al. [START_REF] Xiao | [END_REF] reported several phenomena that could impact significantly the Fischer-Tropsch product analysis. Particular attention should be paid to heavy product accumulation, which can induce severe deviations from the Anderson-Schulz-Flory distribution if the reactor is not operating in steady state. A certain period of time is required for the dense products to fill the downstream dead volumes and thus to be collected, whereas the light products (typically in gaseous state) rapidly reach the collection system because of their higher molar volume. Deactivation is also an important parameter to consider, in particular if performance evaluation is carried out for long Time On Stream (TOS). Chambrey et al. 14 showed that even if catalysts are less sensitive to deactivation in milli-fixed bed reactors, the activity loss can reach 1%•day -1 , which is significant when tests are carried out for hundreds of hours. To avoid this issue, Morra et al. 18 used a HTE setup which can feed one reactor with a fixed composition and the other ones with a different composition. This configuration allows the acquisition of kinetic data under different conditions on a selected reactor, while the others are running under nondeactivating conditions (inert feed). The data acquisition is then performed successively reactor after reactor, and the overall data set is thus representative of the initial catalyst state for every reactor. However, to reduce the costs and improve accuracy, most recent HTE setups embed specific systems such as mutualized feed distributions 10 , pressure regulators [START_REF] Van Der Waal | Catalysis and synthetic fuels: state of the art and outlook[END_REF][START_REF] Cao | Catalysis and synthetic fuels: state of the art and outlook[END_REF]13 or heating blocks [START_REF] Van Der Waal | Catalysis and synthetic fuels: state of the art and outlook[END_REF][START_REF] Cao | Catalysis and synthetic fuels: state of the art and outlook[END_REF]13 making it impossible to choose different operating conditions from one reactor to the other, such as flowrate, flow composition and pressure. To face this problem, alternative strategies have been used, for instance contact time variations by loading 2,4,[START_REF] Moulijn | [END_REF]13,18 different amount of the same catalyst in different reactors, which is particularly interesting for kinetic studies. This method may, however, introduce some differences between the different reactors since as a consequence, the catalysts are also activated at different hydrogen space velocities from one reactor to another, and their performances can be affected. A way to ensure a uniform degree of reduction between each reactor is the use of reduction promoters, but this method might not be suited for kinetic studies because of the promoters potential involvement in Fischer-Tropsch reactions [START_REF] Nabaho | Recent Advances in F-T Synthesis and Fuel Processing Catalysis[END_REF] .

The present study discusses the suitability of HTE for kinetic studies, dealing with several issues frequently encountered in Fischer-Tropsch synthesis: catalysts activation and deactivation, mass transfer limitations, and wax accumulation. The effect of hydrogen space velocity on non-promoted catalysts activation will first be studied, and the accuracy of the data obtained in HTE will then be evaluated on a 16-reactor setup. Effects of operating conditions will be studied and compared to the data from literature. A focus on contact time variation methods will be carried out in particular to assess their suitability for kinetic studies. Deactivation and steady state product analysis will finally be discussed.

Experimental

Catalysts synthesis

Two 15 wt.% Co catalysts were prepared by incipient wetness impregnation, using commercial Siralox5 (silica alumina from Sasol) as a support. An aqueous Co(NO 3 ) 2 •6H 2 O solution was employed as a source of cobalt, and two successive impregnations were performed to reach the desired cobalt loading. The impregnated catalysts were then dried and calcined with two different protocols in order to obtain two different cobalt particle sizes. The first catalyst (referred as Co-12) was dried in a static oven at 85°C for 12 hours before being calcined in air (2 NL•h -1 •g -1 ) with a fixed bed reactor for 2 hours, at 400°C using a heating ramp of 2.5°C•min -1 . The other catalyst (Co-18) was pre-dried at 85°C for 30 minutes in a static oven, before being transferred into a fluidized bed reactor where the main drying step occurred. This last step consisted of exposing the catalyst to an air flow with a gas velocity of 0.8 NL•h -1 •g -1 of support, at 100°C for 2 hours. The heating ramp from room temperature to 100°C was 1.5°C min -1 . The subsequent calcination was conducted directly in the same reactor, at 400°C for 2 h using an air flow of equivalent gas velocity. The heating ramp from 100 to 400°C was 5°C min -1 .

Catalysts characterization

The specific surface area and pore volume of the cobalt oxide catalysts were measured by nitrogen physisorption using an Asap® 2420 instrument from Micromeritics®. The specific surface area was estimated with the BET method and the pore volume was considered to be equivalent to the nitrogen volume adsorbed at maximum pressure.

Cobalt oxide (Co 3 O 4 ) average particle sizes were determined by X-Ray Diffraction (XRD). Diffractograms were registered on a PANalytical X'Pert Pro® apparatus equipped with a copper source 9P QR% = 1.5406 Å). The Co 3 O 4 average particle size was calculated using the Scherrer method on oxide catalysts and the metallic cobalt particle size was estimated to be equal to 0.80 the size of the Co 3 O 4 particle (molar volume ratio of Co over Co 3 O 4 ). The metallic cobalt particle size distribution was also estimated by Transmission Electron Microscopy (TEM). TEM images of reduced and passivated catalysts were taken in dark field mode for a better contrast, with a JEM 2100F microscope. Particle size distributions were based on the measurement of more than 200 particles, and the average particle size in volume was estimated by fitting the particle size distribution with a log normal law. The catalyst reduction before TEM analysis was performed in a 5 mm ID reactor at 400°C for 16h, using a hydrogen gas velocity of 2 NL h -1 g -1 and a heating ramp of 2°C min -1 . The samples were then passivated at room temperature with a mixture of 1% O 2 /N 2 for 3h before unloading the catalysts.

To quantify the catalysts active sites and to corroborate the particle size at a given degree of reduction with XRD and TEM results, static H 2 -chemisorption was performed using a BELSORP-max apparatus over catalysts that were reduced at 350°C under pure hydrogen for 2h. The degree of reduction (DOR) of the samples was controlled after the analysis by Temperature Programmed Reduction (TPR), using the procedure described below. Knowing the catalysts DOR, the average particle size was calculated from the number of surface cobalt atoms considering a spherical particle shape. Hydrogen was considered to adsorb dissociatively on metallic cobalt [START_REF] Bartholomew | [END_REF] , thus the number of active sites corresponds to twice the number of adsorbed H 2 molecules. TPR analysis was performed with an AutoChem II 2920 V4.03 apparatus from Micromeritics®. 0.5 g of catalyst was exposed to 58 NmL min -1 of 5% H 2 /Ar while heating from room temperature to 1000°C at 5°C min -1 . The technique was either used to qualitatively estimate the catalysts reducibility, but also performed to quantify the degree of reduction of reduced catalysts. In practice, the CoO area of the reduced passivated catalyst profile was subtracted from the CoO area of the catalyst precursor obtained after the calcination profile to determine the fraction of unreduced cobalt.

Magnetization measurements were used to determine the fraction of metallic cobalt of the catalyst. Metallic cobalt exhibits a high magnetic susceptibility compared to its oxide or carbide phases [21][22][23][24][START_REF] Mohn | Magnetism in the Solid State An Introduction[END_REF] . Knowing the theoretical magnetization of a sample corresponding to hundred percent metallic fraction, and by neglecting the susceptibility of the non-metallic cobalt phases the actual DOR is given by the relation:

DOR = M m (Eq. 1)
being the saturation magnetization, the magnetic M susceptibility of metallic cobalt, and the cobalt mass of the m sample. The saturation magnetization was estimated at a magnetic field of 2T.

High-throughput experiments

The HTE setup used is a Flowrence® unit developed by Avantium (Figure 1). Sixteen down-flow fixed bed reactors (2 mm ID) run in parallel with a mutual gas feed, equally distributed through the reactors by a patented passive microchip. The relative standard deviation of the flowrate measured on the different reactors without any catalyst is less than 2%. Pressure is regulated for each reactor independently around a common set point, and is based on the values measured at the entrance of the reactors. Four independent heating blocks are used to control the temperature of 4 reactors each. Temperatures are measured at three heights of the heating blocks to ensure the temperature homogeneity along the catalytic beds. The reactor isothermal zone (±0.7°C at 250°C) measures 20 cm starting from 8 cm of the bottom. The catalyst bed (100 mg -500 mg) is placed within this isothermal zone on top of a 10 cm layer of ceramic powder (Microblast® B-120). A second layer of ceramic powder is placed on top of the catalyst layer to reach a total bed length of 40 cm. The last 16 cm of the reactor are filled using a 1 mm glass sock, to minimize dead volumes and improve gas mixing. Effluents are collected downstream and heavy products are separated from the light ones by a cold trap at 80°C. Light products up to C 10 hydrocarbons are analysed on-line by gas chromatography and heavy products off-line by simulated distillation analysis. Based on thermodynamic calculations using PRO/II software (SRK model), C 10-products are recovered at 95% in the gaseous phase and C 17+ at 95% in the liquid phase. Helium is used as an internal standard for gas flow calculation and is analysed by a thermal conductivity detector (TCD), along with N 2 , CO, and H 2 , which are separated through a molecular sieve column. The lightest hydrocarbons cut (C 1 -C 8 ) is separated from the heavy

Q formula Q = Q V (Eq. 2)
is the inlet flowrate of helium, and is the Q V measured outlet volume fraction of helium. Specific flowrates of compounds are then calculated as

Q Q = Q • (Eq. 3)
is the concentration of compound at the outlet. CO conversion, CH 4 , C 2 , C 3 , C 4 and C 5+ selectivity are obtained by the following relations:

X = Q Q Q (Eq. 4) S 4 = Q 4 , Q Q (Eq. 5) S 2 = 2 Q 2 , Q Q (Eq. 6) S 3 = 3 Q 3 , Q Q (Eq. 7) S 4 = 4 Q 4 , Q Q (Eq. 8)
S 5 + = 1 (S 4 + S 2 + S 3 + S 4 )

(Eq. 9)

The catalysts activity was followed through the CO consumption rate divided by the catalyst mass, noted (in µmol•s -1 •g -1 ) and r defined as:

r = X Q & & '( ()* (Eq. 10)
with the CO molar volume under standard conditions of & temperature and pressure and the CO inlet flowrate (NL Q s -1 ).

During the tests, one of the 16 reactors is kept empty to verify that both the feed distribution and analysis of the products are correct. The measured CO conversion in this reactor should be 0%, any deviation would indicate a default of either the analytical system or of the feed section.

For each test, catalysts are reduced in-situ under 0.45 NL h - 1 of pure hydrogen at 400°C for 16 h, with a heating ramp of 2°C min -1 at atmospheric pressure. The temperature is then decrease to 180°C under hydrogen and the pressure is progressively raised to 20 barg. Syngas is finally injected and the temperature increased to 220°C with a ramp of 1°C min -1 . The syngas GHSV used in the tests were comprised between 2 and 13 NL h -1 g -1 , and the H 2 /CO molar ratios between 1.85 and 2.4.

Heat and mass transfer are considered not to be limiting factors according to the calculations presented in Table 1, for which the details can be found elsewere [START_REF]EUROKIN spreadsheet on requirements for measurement of intrinsic kinetics in the gas-solid fixed-bed reactor[END_REF] . All criteria to assume ideal intrinsic kinetic conditions are fulfilled.

Slurry reactor tests

Before Fischer-Tropsch slurry experiments, the catalyst activation was performed in a fixed bed reactor using the same protocol as for HTE, but with a H 2 flowrate of 2 NL h -1 g -1 . The catalyst was then unloaded under inert gas and coated with n-C 22 solvent to prevent re-oxidation. The n-C 22 catalyst sample was let to solidify before being transferred into the slurry reactor previously filled with n-C 22 . Then slurry experiments were carried out in a 50 cm 3 CSTR at 220°C, 20 barg, molar ratio H 2 /CO = 2.12. A known mass of 3 to 4 g of reduced embedded catalyst was loaded in the reactor, and the syngas space velocity was adjusted at the beginning of the test to reach 45% CO conversion. The conversion was then followed with time on stream like the HTE fixed bed experiments in which no gas velocity change is applied. Gas phase analysis was carried out on-line using a Molecular Sieve column with a TCD for permanent gases, and a PONA column with a FID for light hydrocarbons. The relative standard deviation of the GC analysis was estimated to 3%.

Wax composition analysis

The liquid product fraction (typically C 17+ hydrocarbons) was analysed off-line by simulated distillation. The hydrocarbon mix (typically 0.1 g) was dissolved in CS 2 before being separated through a MXT®-1 column, after which hydrocarbons were quantified by a FID. The chain growth probability R was calculated based on the C 17-42 hydrocarbons distribution, by fitting the Anderson-Schulz Flory (ASF) equation [START_REF] Anderson | [END_REF] .

Results

Catalysts properties

Oxide catalysts main textural properties are presented in Table 2. No major difference is observed between the two catalysts, which both exhibit slightly lower surface area and pore volume than the support (171 m 2 g -1 , 0.52 mL g -1 ), probably due to pore blockage by cobalt oxide particles 28 .

The average particle size of the two catalysts given by the different techniques mentioned in section 2.3 are also listed in Table 2. For each method, the Co-18 catalyst shows a larger particle size than the Co-12 one. For instance, a value of 18±2 nm is obtained by XRD for the Co-18 oxide catalyst while a value of only 12±1 nm is found for the Co-12 one. The average particle Please do not adjust margins Please do not adjust margins size of reduced catalysts can be deduced from these values, considering the molar volumes of Co 0 and Co 3 O 4 (v Co /v Co3O4 = 0.80). The average particles sizes of the reduced catalysts are estimated to be 14 nm and 10 nm for the Co-18 and Co-12, respectively. Particle sizes calculated from TEM images corroborated rather well these calculations since they were found as 11±1 nm for the Co-12 catalyst and 12±1 nm for the Co-18 one. In-situ magnetisation measurements showed that passivation reduces the DOR of both catalysts by 25%, which corresponds to a passivation layer of 0.6 nm considering a spherical particle shape. The reduced catalyst particle sizes are therefore estimated to 12 nm and 13 nm for Co-12 and Co-18, respectively. Although the difference between the two is less by TEM method than by XRD, a larger particle size is found for the Co-18 catalyst. The same observation is made for the H 2 chemisorption measurements, with particle sizes of 15 nm and 17 nm for the Co-18 and Co-12, respectively. Despite an overestimation, these values are in good agreement with the XRD results. Both TEM and H 2 chemisorption indicate less of a difference in the average particle size between the two catalysts after reduction. This could be due either to sintering of small particles during reduction, or to preferential reduction of big particles.

TPR profiles of the two catalysts are presented in Figure 2, which clearly show the particle size effect on the catalyst reducibility. The first peak of H 2 consumption (300°C) corresponds to the reduction of Co 3 O 4 into CoO 29,30 , whereas the second one correspond to the reduction of CoO into Co 0 . While no significant difference can be observed for the first peak, the second peak is slightly shifted to higher temperatures for the Co-12 catalyst. This might be due to the high metalsupport interactions of small particles that decrease the catalyst's reducibility 31,32 .

Effect of hydrogen space velocity on catalysts activation

The conversion in a plug flow fixed bed reactor depends on the ratio W cat /F CO , which is directly related to the mean residence or contact time. The generation of accurate data at different ratios of W cat /F CO is therefore essential in kinetic studies. To vary the contact time, one can either vary the mass of catalyst (W cat ) or the inlet flow rate (F CO ). To take full advantage of the parallelization in HTE, it is best to adapt the catalyst mass in the different reactors 2,4,[START_REF] Moulijn | [END_REF]13,18 and operate all reactors at the same flow rate. This strategy, however, implies that different hydrogen space velocities are applied to all catalyst samples during the in-situ catalyst reduction treatment. This parameter has a great influence on the degree of reduction of cobalt and nickel catalysts [START_REF] Tsakoumis | Chemical communications[END_REF][START_REF] Bartholomew | [END_REF] . Water is the main product of cobalt oxide reduction, and a change in its dilution level may modify the final extent of reduction 35 because of a kinetic inhibition 36 . The effect of the hydrogen space velocity on the catalyst reduction was studied by magnetisation measurements of samples reduced in the HTE setup. The hydrogen space velocity used for reduction was varied from 1.0 to 2.2 NL h -1 g -1 , by varying the amount of catalyst from 100 mg to 460 mg (hydrogen flowrate of 0.45 NL•h -1 ). Figure 3 shows the two catalyst's DOR's as a function of the GHSV (Gas Hourly Space Velocity) used for reduction, performed at 400°C for 16 h in the HTE setup. The DOR's were calculated based on magnetization measurement. After reduction in the HTE unit, the samples were unloaded under inert atmosphere (<0.5 ppm O 2 , <0.5 ppm H 2 O), to prevent any re-oxidation by air exposure. Two different trends were observed. The less reducible one, Co-12, exhibits a clear dependency of its DOR on the GHSV, with higher DOR's at high hydrogen gas velocity. On the contrary, the Co-18 catalyst is not sensitive to the gas velocity used for reduction, at least for a GHSV range between 1 and 3 NL h -1 g -1 . This phenomenon complicates the measurement of kinetics in HTE over catalysts that are difficult to reduce. This may be one of the reasons why trends have sometimes been reported at high conversion [START_REF] Marion | Studies in Surface Science and Catalysis Natural Gas Conversion VIII[END_REF][START_REF] Yates | [END_REF] , but Ma et al. 47 explained this by the rapid deactivation of the catalyst that occurs at high conversion, changing the degree of oxidation and therefore the selectivity. It can also be due to the high partial pressure of water produced at high conversion levels, which promote the water-gas-shift reaction and thus modify the H 2 /CO ratio.

The reactor-to-reactor repeatability of catalyst performances is quite similar to the one reported by van der Waal et al. [START_REF] Van Der Waal | Catalysis and synthetic fuels: state of the art and outlook[END_REF] on a 64-reactor setup, with a RSD value of maximum 5% on activity. However, the inter-test repeatability of catalysts performances is a bit lower than this reactor-toreactor repeatability, with a RSD value that reaches 10% on CO conversion (Table 3). This difference may be due to the error added by the systematic, but necessary recalibration of the analytical system performed at the beginning of each test. A lower accuracy of catalysts performances measurements should therefore be considered if data is acquired through several tests. Besides, the CO conversion RSD values are higher for the Co-12 catalyst than the Co-18 one. This might be due to the lower reducibility of Co-12, which makes its performance more sensitive to the reduction GHSV. Finally, no effect of the catalyst amount on the error margin is observed within the range operated.

Steady state

When catalysts are exposed to syngas at the early stage of reaction they generally undergo a transitory period. During this phase their performances are evolving and may not be representative of those at longer TOS. Steady state has to be reached if data is acquired during extended periods. Two phenomena may be at the origin of this behaviour: catalyst surface reconstruction [START_REF] Schulz | Fischer-Tropsch synthesis on the eve of the XXI Century[END_REF] , and hydrodynamic changes due to accumulation of waxes [START_REF] Xiao | [END_REF] in the catalyst bed and porosity. The period duration can be estimated by monitoring the catalyst activity or selectivity either by gas phase or liquid phase analysis. However, considering the low flowrate of the liquid product fraction, the transitory period for the determination of this fraction composition may be longer than for the gas phase [START_REF] Xiao | [END_REF] . The 24 h catalytic tests showed that the initial transitory period of surface reconstruction and pore filling is fast enough not to be detected by the gas phase analysis. After the first gas analysis registered at TOS 1h the CO conversion decreased continuously, which is likely related to catalyst deactivation. To monitor the evolution of the composition of the liquid fraction, a dedicated HTE test was carried out during an extended period while collecting samples every 12 h. Two different operating conditions were tested successively in order to measure also the transitory period after a change of reaction conditions (lower H 2 /CO value). Figure 5 shows the evolution of the liquid productivity and chain growth probability with TOS for the Co-18 catalyst (338 mg). During the first 24 h the liquid fraction productivity increased due to its accumulation inside the downstream dead volume, which represents 0.46 mL. Considering a liquid density of 0.95 g mL -1 and a liquid productivity of 100 mg h -1 g -1 , 12h are required to fill this downstream section with waxes, which corroborates well with our measurements. Chain growth probability is also increasing during this initial period, but in an extent that remains within the error margin. Still, this behaviour could be due to a more severe hold up of heavy hydrocarbons [START_REF] Schulz | Fischer-Tropsch synthesis on the eve of the XXI Century[END_REF][START_REF] Shi | [END_REF] . During the first stage of reaction where the catalyst pores are filled, the heavy hydrocarbons are accumulating in a greater extent than lighter ones [START_REF] Shi | [END_REF] leading to product segregation and thus an underestimation of the chain growth probability. The surface reconstruction could also be at the origin of this behaviour. Several authors have reported an increase in the heavy compound selectivity in the gas phase [START_REF] Xiao | [END_REF][START_REF] Schulz | Fischer-Tropsch synthesis on the eve of the XXI Century[END_REF] . A third explanation of this behaviour could be the sintering of the smallest particles. This phenomenon is known to occur mainly at the beginning of the reaction 50 , and it has been shown that larger particles (over 8 nm) exhibit a higher selectivity towards heavy compounds than small ones 37 . Starting from 36 h of TOS, both productivity and chain growth probability reach a stable value.

After the change of the reaction conditions, an increase of the alpha value is measured, whereas the liquid productivity remains almost constant. No particular transitory period is observed during this condition change for the liquid composition, the alpha value being relatively constant after the first measurement.

Whereas no significant delay is required for the gas phase analysis to obtain representative catalysts performances at a given condition, a transitory period should be respected for accurate liquid phase analysis especially during the reactor start-up. This period should at least exceed the time necessary to fill the dead volume after the reactor, which is correlated to the catalyst liquid productivity. Despite very slight variations of the liquid composition observed during the first 36 h of reaction, it is preferable to perform liquid collection after this period to ensure a representative composition. This constraint dramatically extends the test duration if liquid phase analysis is required and operating conditions are tested successively. In this case, catalyst deactivation becomes an important parameter to consider for coherent data acquisition. Please do not adjust margins Please do not adjust margins syngas composition have to be studied in a successive manner, which is not only time consuming but also detrimental to the accuracy, because of the catalyst deactivation (cf section 3.7). Thus, to minimize the test durations, the impact of these three parameters were studied through two separated runs. In the first one the influence of gas composition and pressure were studied, whereas in the second one, the effect of temperature and pressure were studied. Despite the possibility to perform simultaneous variations, temperature was varied successively to acquire the data on the same reactor (and thus avoid deviations due to changes of the conditions during activation or stabilisation). In practice, each of the 3 parameters studied was varied twice using a positive and a negative variation around a central value, while keeping the other parameters constant. The default values used for the Co-18 catalyst were 220°C, H 2 /CO = 2.12, 20 barg while the ones used for Co-12 catalyst were 216°C, H 2 /CO = 2.12, and 20 barg. The temperature was adjusted between the two catalysts to reach the same CO conversion level of 50%. The results are shown in Table 5. It can firstly be observed that the impact of syngas composition, pressure and temperature on performances do not differ from one catalyst to the other. Thus, a general description of the observed trends is presented in the following without any distinction between catalysts. Regarding catalyst activity, CO consumption rate per gram of catalyst systematically increases when either temperature, pressure, or H 2 /CO ratio increases. The positive effect of the H 2 /CO increase on the activity, indicates an inhibition of the reaction by CO (i.e. a negative reaction order) and a promotion by H 2 , which has already been reported by several authors [START_REF] Van Santen | Physical chemistry chemical physics PCCP[END_REF][START_REF] Agrawal | [END_REF][60][61][62] . Using Arrhenius law, an apparent activation energy of 110 kJ•mol -1 is obtained, which is comparable to the ones reported [60][61][62] .

The parameter effect on catalyst selectivity is more complex, but in line with the trends reported in the literature 39,61 . It can be observed that, as for the activity, the methane selectivity systematically increases when P, T or H 2 /CO increases. The C 5+ selectivity follows the opposite trend and always decreases when the CH 4 selectivity increases as observed by Lillebø et al. 38 . However, the chain growth probability R does not obey the same correlation. When the pressure increases, the R value increases while the C 5+ selectivity decreases, which is not expected if a perfect ASF distribution is considered. This behaviour has also been observed by Todic et al. 39 and may be explained by a deviation from the theoretical ASF distribution with two alpha values [63][64][65][66] , one attributed to light products and one to heavy products. The chain growth probability of light products is negatively affected by the pressure increase while the heavy products one is positively affected. This double chain growth probability may originate from the presence of two active sites 39 , the chain length dependency of olefins re-adsorption 67 , or even products accumulation [START_REF] Xiao | [END_REF]68 . The last explanation can be ruled out, since it has been verified that steady state was reached before collecting samples (cf next section). Concerning the CO 2 selectivity, a decrease is observed when the H 2 /CO ratio increases and an increase is measured when temperature increases. CO 2 being mainly produced through the water-gas-shift reaction in which CO is a reactant and H 2 a product, the increase of the H 2 /CO ratio logically induces a CO 2 selectivity decline. Finally, the olefin-to-paraffin ratio decreases both with the increase of the H 2 /CO ratio and temperature 69 , but increases when the pressure increases. The increase of H 2 partial pressure has been reported to accelerate the alpha olefins secondary hydrogenation reaction 39 and thus a decrease of the O/P ratio. The temperature effect observed on the products selectivity might originate from differences in the activation energy of the reactions leading to the different products respectively.

The present analysis shows that general trends of activity and selectivity as a function of operating parameters corresponds well to the ones reported in the literature. It can therefore be expected that the kinetic data acquired in HTE is well representative of the one acquired in slurry reactors.

Conclusions

High-throughput technology is a well-suited tool for kinetic studies due to the rapid data generation capacity. In order to improve the accuracy of such equipment and make sure to acquire coherent data sets, several points require to be investigated before carrying out data acquisition. To accelerate kinetic data acquisition, it is convenient to load different amounts of the same catalyst in different reactors operated in parallel to obtain data with different contact time. However, this method may have some drawbacks and there are some cases where successive flowrate variations applied on a selected reactor might be preferred. Variation of catalyst amounts over several reactors is suitable only in the case of easily reducible catalysts, for which similar extents of reduction are obtained whatever the amount of catalyst. On the other hand, we have shown that in the case of less reducible cobalt catalysts, varying the hydrogen space velocity during activation can modify the catalyst extent of reduction and thus conduct to incoherent kinetic data. In this method, the catalysts also have to be resistant to deactivation. We observed that deactivation is directly correlated to the conversion level. The catalyst stabilization under syngas may thus differ depending on the operated conversion level and the amount of catalyst. The variation of the syngas flowrate to vary the contact time should be therefore preferred for less reducible catalysts and catalysts sensitive to deactivation. In that case, however, the catalysts need to be relatively resistant to deselectivation (i.e. loss of selectivity) to avoid incoherent data due to the evolution of performances with time on stream. When data is successively acquired, the tests duration can rapidly increase. Even with the present miniaturized HTE setup, significant transitory periods need to be considered for liquid composition analysis after a change of reaction conditions. Deactivation is thus an important phenomenon to quantify, in particular when catalysts are working at high conversion and the tests are performed for extended periods of time. This issue can still be avoided by running successive short tests. It is however time consuming and implies to consider the inter-test repeatability, which may Please do not adjust margins Please do not adjust margins be lower than the reactor-to-reactor repeatability and thus decrease the overall accuracy of a data set. The test methodology is therefore crucial to measure with accuracy the catalysts performances, and obtain a coherent data set. Moreover, different catalysts types (particles size, support, promoter) will have different reducibility and different sensitivity to deactivation, which will make it necessary to adapt the methodology for kinetic studies.

Figure 1 :

 1 Figure 1: Schematic of the HTE 16-reactor Fischer-Tropsch unit

Table 1 :

 1 Evaluation of different criteria to assess the intrinsic kinetic conditions. For the calculations, the standard reaction conditions were used (see main text) and rCO,max = 0.012 mol•kgcat -1 •s -1 ). h: catalyst bed length, dp: catalyst particle diameter, DR: reactor diameter. Physical phenomenon Criteria plug flow (axial) h/d p = 1100 > 7 plug flow (radial) D R /d p = 17 > 10 external mass transfer (Carberry number) 1 10 -5 < 0.05 internal mass transfer (Wheeler-Weisz) 1 10 -3 < 0.08 external heat transfer (film) 0.01 K < 1.2 K external heat transfer (radial) 0.4 K < 1.2 K internal heat transfer 0.001 K < 1.2 K ARTICLE Journal Name 4 | J. Name., 2012, 00, 1-3 This journal is © The Royal Society of Chemistry 20xx

Figure 2 :Figure 3 :

 23 Figure 2: Temperature Programmed reduction profiles of the two catalysts

Figure 5 :

 5 Figure 5: Liquid fraction productivity and composition evolution, at initial reaction stage and during a condition change (CO conversion range: 30 -40%). Condition 1 : 220°C, 20 barg, H2/CO = 2.12, 3.4 NL•h -1 •g -1 . Condition 2 after 100h TOS : 220°C, 20 barg, H2/CO = 1.85, 3.2 NL•h -1 •g -1 .

12 |

 12 J. Name., 2012, 00, 1-3 This journal is © The Royal Society of Chemistry 20xx

  one (C 9 -C 14 ) by a first HP-INNOwax column. Light hydrocarbons are then separated through a GS-GasPro column, and heavy ones through a second HP-INNOwax column. Both hydrocarbons cuts are finally analysed with a flame ionisation detector (FID). CO 2 is converted into methane over a Ni catalyst before being analysed on the heavy products column. Water is not quantified, and since some heavy products are not fully condensed at 80°C, element balance cannot be calculated.

	The total outlet flowrate	is calculated according to the

Please do not adjust margins

Please do not adjust margins hydrocarbons

Table 2 :

 2 Catalysts textural properties and particles size

		S BET (m 2 /g) V p (ml/g)	d Co3O4 , XRD (nm)	d Co , XRD (nm) d TEM (nm)	d TEM corr. (nm)*	Ns chem (mmol/g)** d chem (nm)
	Co-12	139±7	0.37±0.01	12±1	10±1	11±1	12±1	0.10 (62)	15
	Co-18	135±7	0.36±0.01	18±2	14±1	12±1	13±1	0.11 (74)	17

*Corrected considering the passivation layer **Number of active sites estimated by H2 chemisorption analysis, with in parenthesis the percentage of reduction of the samples (%)

Published on 31 August 2020. Downloaded by Université Claude Bernard Lyon on 8/31/2020 4:07:31 PM.View Online

Published on 31 August 2020. Downloaded by Université Claude Bernard Lyon on 8/31/2020 4:07:31 PM. View Article Online DOI: 10.1039/D0CY00918K

Acknowledgements

The authors would like to thank IFP Energies nouvelles for financial and analytical support, and IRCELyon for experimental tools. Catalytic tests were conducted on the high-throughput heterogeneous catalytic platform located on Axel'One Campus facilities.

Conflicts of interest

There are no conflicts to declare.

Journal Name

ARTICLE

This journal is © The Royal Society of Chemistry 20xx J. Name., 2013, 00, 1-3 | 13


András Hatos

Borbála Hajdu-Soltész

Alexander Miguel Monzon

Nicolas Palopoli

Lucía Álvarez

Burcu Aykac-Fas

Claudio Bassot

Guillermo Ignacio Benítez

Martina Bevilacqua

Anastasia Chasapi

Lucia Chemes

Norman Davey

Radoslav Davidović

A Keith Dunker

Arne Elofsson

Julien Gobeill

Nicolás S González Foutel

Sudha Govindarajan

Mainak Guharoy

Tamas Horvath

Valentin Iglesias

Andrey V Kajava

Orsolya Panna Kovacs

John Lamb

Matteo Lambrughi

Tamas Lazar

Jeremy Y Leclercq

Emanuela Leonardi

Sandra Macedo-Ribeiro

Mauricio Macossay-Castillo

Emiliano Maiani

Jose A Manso

Cristina Marino-Buslje

Elizabeth Martínez-Pérez

Bálint Mészáros

Ivan Mičetić

Giovanni Minervini

Nikoletta Murvai

Marco Necci

Christos Ouzounis

Mátyás Pajkos

Lisanna Paladin

Rita Pancsa

Elena Papaleo

Gustavo Parisi

Emilie Pasche

Pedro José

Barbosa Pereira

Vasilis J Promponas

Jordi Pujols

Federica Quaglia

Patrick Ruch

Marco Salvatore

Eva Schad

Beata Szabo

Tamás Szaniszló

Stella Tamana

Agnes Tantos

Nevena Veljkovic

Salvador Ventura

Wim Vranken

Zsuzsanna Dosztányi

Peter Tompa

Silvio C E Tosatto
email: silvio.tosatto@unipd.it

Damiano Piovesan

DisProt: intrinsic protein disorder annotation in 2020

The Database of Protein Disorder (DisProt, URL: www.disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent Page 2 of 16 For Peer Review Nucleic Acids Research developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies.

The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the "dark" proteome.

INTRODUCTION

About 20 years ago, the concept of the intrinsic structural disorder of proteins came into being [START_REF] Romero | Thousands of proteins likely to have long disordered regions[END_REF][START_REF] Wright | Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm[END_REF]. Since then, the field has reached adulthood, with the concept of protein disorder gaining wide acceptance in the community. Intrinsically disordered proteins/regions (IDPs/IDRs) are now often being referred to without a citation, the term having become as common as the "globular" structure of a protein, or the "active site" of an enzyme. Yet, the field is still accelerating and has not reached its climax, as signaled by several recent breakthroughs and high-impact stories [START_REF] Van Der Lee | Classification of Intrinsically Disordered Regions and Proteins[END_REF][START_REF] Davey | The functional importance of structure in unstructured protein regions[END_REF].

For example, it was recently recognized by "omics" data analyses that about half of eukaryotic proteins are "dark", in the sense that we have no information on their 3D structure [START_REF] Perdigão | Unexpected features of the dark proteome[END_REF], which poses a serious bottleneck in their functional characterization and annotation. Similarly, only 45% of the residues of all human proteins are covered by multiple sequence alignment-based Pfam-A protein family annotations [START_REF] Mistry | The challenge of increasing Pfam coverage of the human proteome[END_REF]. These values suggest that only a vague notion about the structure and function of the majority of proteins in our databases. As a significant fraction of the dark proteome and non-Pfam annotated proteins and protein regions are intrinsically disordered (the concepts having become almost synonymous), our best approach for illuminating the dark proteome is to predict disorder from sequence, and experimentally characterize the underlying structural ensembles [START_REF] Bhowmick | Finding Our Way in the Dark Proteome[END_REF].

The prediction of protein disorder from sequence was on the menu of the Critical Assessment of Protein Structure Prediction (CASP), a community-wide experiment of predicting protein structures from sequence [START_REF] Monastyrskyy | Assessment of protein disorder region predictions in CASP10[END_REF], for many years. A new initiative, the Critical Assessment of Intrinsic protein Disorder (CAID), has now reached maturity and will be reintegrated into the CASP programme, with a clearer IDP perspective. New annotations in DisProt have already been used to provide a blind evaluation of disorder predictors [START_REF] Necci | A comprehensive assessment of long intrinsic protein disorder from the DisProt database[END_REF].

Several recent breakthroughs have also signaled the vitality of the field. An unsettled question with IDPs/IDRs is whether their structural disorder persits in the crowded interior of cells.

Whereas diverse indirect evidence indicates that this is the case [START_REF] Tompa | The interplay between structure and function in intrinsically unstructured proteins[END_REF], only in-cell NMR seems currently available to address this issue. For example, it was recently applied to study Parkinson's disease protein α-synuclein (DisProt DP00070), once suggested to have folded, oligomeric structure in cells [START_REF] Bartels | α-Synuclein occurs physiologically as a helically folded tetramer that resists aggregation[END_REF]. In-cell NMR has clearly shown that α-synuclein preserves its disordered, monomeric state in non-neuronal and neuronal cells alike [START_REF] Theillet | Structural disorder of monomeric α-synuclein persists in mammalian cells[END_REF].

Another aspect of the functionality of IDPs is that they often mediate protein-protein interactions, mostly by folding upon partner binding [START_REF] Yang | Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding[END_REF], but sometimes by preserving their structural disorder (fuzziness) in the bound state [START_REF] Pricer | From Fuzzy to Function: The New Frontier of Protein-Protein Interactions[END_REF]. This was recently shown to occur in the extremely tight (picomolar) interaction between two human IDPs, histone H1 (DisProt DP01156) and its nuclear chaperone, prothymosin-α (DisProt DP01677). These proteins associate while retaining their highly dynamic, fully disordered state [START_REF] Borgia | Extreme disorder in an ultrahigh-affinity protein complex[END_REF]. Functional regulation of another type may also arise from structural disorder, via the entropic force generated by the structural ensemble of an IDP/IDR. In the enzyme UDP-α-D-glucose-6dehydrogenase (UGDH, DisProt DP02338), the C-terminal disordered tail has such a role, finetuning the energy landscape of the protein and stabilizing a sub-state that has a high affinity for an allosteric inhibitor [START_REF] Keul | The entropic force generated by intrinsically disordered segments tunes protein function[END_REF][START_REF] Egger | Structure and Mechanism of Human UDP-glucose 6-Dehydrogenase[END_REF].

It is without doubt that we cannot afford to ignore this intrinsically disordered, yet functionally important part of the proteome. Not only does structural disorder play an exquisite role in cellular signaling and regulation [START_REF] Piovesan | MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins[END_REF], it is also often implicated in disease [START_REF] Mészáros | Systematic analysis of somatic mutations driving cancer: uncovering functional protein regions in disease development[END_REF][START_REF] Babu | The contribution of intrinsically disordered regions to protein function, cellular complexity, and human disease[END_REF].

Consequently, IDPs also represent important drug targets: a largely unexplored frontier in developing molecular medicine is the rational design of drugs against IDPs [START_REF] Ruan | Targeting intrinsically disordered proteins at the edge of chaos[END_REF].

Due to these challenges, it is important to update and upgrade DisProt, the primary database of protein disorder. Whereas predicted disorder features are available in MobiDB [START_REF] Piovesan | MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins[END_REF], which has recently been integrated in UniProtKB [START_REF]UniProt: a worldwide hub of protein knowledge[END_REF], the crux of understanding protein disorder is the availability of manually curated, experimentally verified disorder annotations. The previous release of the database, DisProt 7 [START_REF] Piovesan | DisProt 7.0: a major update of the database of disordered proteins[END_REF], held data of about 800 entries of IDPs/IDRs. Other databases, like IDEAL [START_REF]UniProt: a worldwide hub of protein knowledge[END_REF], ELM [START_REF] Gouw | The eukaryotic linear motif resource -2018 update[END_REF], DIBS [START_REF] Schad | DIBS: a repository of disordered binding sites mediating interactions with ordered proteins[END_REF] and MFIB [START_REF] Fichó | MFIB: a repository of protein complexes with mutual folding induced by binding[END_REF], also include curated disorder information but are somehow different capturing specific functional aspects, or protein classes, and the overlap with DisProt is minimal [START_REF] Necci | Where differences resemble: sequencefeature analysis in curated databases of intrinsically disordered proteins[END_REF]. To reflect on the above-noted breakthroughs and the recent explosion of the related liquid-liquid phase separation (LLPS) field [START_REF] Shin | Liquid phase condensation in cell physiology and disease[END_REF], we present a significant update and upgrade of the DisProt database, which is now at version 8. DisProt 8 holds almost two-times as many entries as DisProt 7, including the majority of those available in aforementioned databases.

DisProt has been completely redesigned with an extended and updated functional classification scheme that relies on functional/structural aspects of annotated regions and incorporates a novel functional class "biological condensation". Annotation concepts have been formalized in a new Disorder Ontology (DO), which is maintained by the entire DisProt community.

DisProt 8 also has many novel features that make it easier to search. The graphical interface has been redesigned and a new entry format provides greater flexibility, simplifies maintenance and allows the capture of more information from the literature.

Lastly, we made significant improvements on the new annotation interface used by DisProt curators to populate the database. It is now easier to use and leverages curators' work by exploiting text-mining technologies, integrating third-party information on-the-fly and implementing several validation checks.

In recent work, specific sequence features have been associated with different disorder "flavours" and mapped on a large scale [START_REF] Necci | Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe[END_REF]. This information has been used to improve protein function prediction from sequence [START_REF] Piovesan | 2.0: improving protein function prediction for the dark proteome[END_REF]. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the "dark" proteome.

PROGRESS AND NEW FEATURES

Database structure and implementation

The way disorder information is represented in the literature is inherently complex. Articles describe functional and structural aspects, where IDPs are strictly connected to dynamic behavior. DisProt tries to capture as much biological knowledge as possible while at the same time providing simple and clear annotations. The idea is to optimize user experience and improve data exchange with other major annotation resources.

Database Records

The major change compared to the previous release is the new annotation paradigm. In DisProt 7 experimental methods represented the annotation core of a DisProt region and function terms were used as attributes. Now the core of an annotation is the functional/structural aspect of a region and the experimental method is an attribute representing the quality of the annotation.

Both functional/structural aspects and the type of evidence are encoded in a controlled vocabulary, in line with other core data resources (e.g., UniProtKB).

In the new DisProt region format a "statement" field has been introduced to track the literature text supporting the evidence. When the text is too long or complicated, a curator statement is provided instead. All "statements" are available from the website and could be used to train text-mining algorithms and to highlight sentence-based annotations on abstracts and full text articles.

At present, functional terms can be associated to a subset of disordered residues, i.e. to a region shorter than the one for which disorder has been experimentally evaluated. For example, a paper describing a folding upon binding event can provide two DisProt records, one region spanning the folding residues and another showing the interacting ones. All regions have now a region identifier field which is unique and stable, i.e. it is never reused and becomes obsolete if the reference sequence changes. Functional and structural vocabulary terms along with experimental methods have been encoded in a new Disorder Ontology (DO).

Disorder Ontology

In order to describe the different functional aspects of IDPs and the experimental methods used to characterize them, an annotation scheme was introduced in DisProt 7. A more formalized version of the disorder ontology was implemented in DisProt 8, to move towards a descriptive, interoperable and collaborative ontology of IDPs. This is the first release of the Disorder Ontology in the specific Biomedical Ontology (OBO) or the Web Ontology Language (OWL) formats [START_REF] Smith | The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration[END_REF][START_REF] Smith | OWL Web Ontology Language Overview[END_REF]. Besides improving the ability to reuse and share the ontology, these formats allow definition of label attributes such as 'xterm' (cross-references to external databases or ontologies) and 'synonym EXACT' (alternative names). They also support assignment of relationships among terms (including for example 'disjoint_from' to mark terms that should not be linked together).

An identifier was assigned to each term in the ontology. It gives each label an 8-character accession code (e.g., "DO:00001"), with the string "DO:" to indicate the disorder ontology and five numeric characters to indicate the term unambiguously. Mirroring the Gene Ontology, accession numbers are assigned incrementally and there is no relationship between accession codes and the ontology topology.

We have reviewed the terms and organization of the whole ontology, paying particular attention to the "Function" category. We made some straightforward changes, for example, we split "Fatty acylation (myristoylation and palmitylation)" into a renamed parent class "Fatty acylation" and its new children terms "Myristoylation" and "Palmitoylation". A new functional term was also introduced to annotate different phenomena related to "Biological condensation" (DO:00040). It describes proteins that undergo phase separation from a solution, e.g., either to form a dynamic liquid droplet (DO:00041, "Liquid-liquid phase separation") or a hydrogel (DO:00042). It also includes cellular protein condensates (DO:00045 and DO:00046 describe "Granule" and "Cellular puncta", respectively), regardless of their existence in physiological or pathological states (as in "Amyloid", DO:00046). This class provides an initial scheme to annotate the relevant but still scarce information available about protein condensates, and we expect this subset of the hierarchy to be modified (possibly by conforming its own subontology) as the field matures.

The distinction between structural states and dynamic events, like disorder-to-order transitions, has been made clearer. Previously "Structural state" terms were part of the "Structural transition" category and "Disorder" was only used implicitly. Now, a new "Structural state" category has been created and it includes "Disorder", "Order", "Pre-molten globule" and "Molten globule" terms. In the future, structural states will be annotated in conjunction with the corresponding environmental conditions affecting the conformation (pH, Post-translational modifications (PTMs), temperature, etc.).

All experimental methods are now encoded under the "Detection method" branch. An overlap with other ontologies exists, but it is not complete or the definition of the same experiment is often slightly different. For example, in DisProt the term "Crystallography" includes "Missing electron density" as a child. In other ontologies "Crystallography" always indicates methods for structural determination. A new "Electron cryomicroscopy" (DO:00128) term has been also introduced in DisProt 8.

The Disorder Ontology (version 0.1.0) is maintained by the DisProt consortium and is available to be adopted by other databases for general use. In the future it will be made available also from third party dedicated repositories.

Curation process and updates

DisProt data is provided by a community effort and annotations are collected through a web interface, which has been improved drastically compared to the previous version in terms of field validation, autocompletion and Named Entity Recognition (NER). In particular, curators can use a dedicated service from the NextA 5 literature triage infrastructure [START_REF] Mottin | neXtA5: accelerating annotation of articles via automated approaches in neXtProt[END_REF] to rank relevant literature starting from a gene name. In complement, when curators start from an article, the DisProt interface exploits the SciLite software through the EuropePMC API [START_REF] Pmc | Europe PMC: a full-text literature database for the life sciences and platform for innovation[END_REF] to automatically retrieve biological entities and identifiers in the manuscript.

The annotation interface implements the concept of ownership and user privileges. DisProt distinguishes two types of users, curators and reviewers. Curators can edit only entries that they have created, while reviewers can modify all entries. Before release the reviewers check all annotations to ensure high quality of the data. Curators are experts in the field and trained to meet DisProt annotation standards.

Access to the annotation interface is restricted to registered curators and provided through Google Authentication (based on the OAuth 2.0 protocol) or the ELIXIR authentication and authorization infrastructure system [START_REF] Linden | Common ELIXIR Service for Researcher Authentication and Authorisation[END_REF]

Database content

Since the last release, both the number of proteins and regions has almost doubled. DisProt 8 contains 1,556 proteins and 3,511 sequence segments annotated as disordered, which cover 19.7% of the number of residues. These numbers become 1,390 proteins, 3,041 regions and 18.7% of disorder content when ambiguous evidence is not considered. Previous annotations have been fixed and updated. Regions shorter than ten residues are no longer allowed and existing short regions were marked as obsolete. Regions ending outside the sequence, regions with a start index of zero instead of one and entries for which the reference sequence in UniProtKB changed, were corrected and, when necessary, new records were created manually.

Figure 1 shows the distribution of regions based on their length and experimental detection method. Compared to the previous version, the distribution shape has not changed. Secondary methods, which include all "Detection methods" terms except "Missing electron density" (DO:00130) and "Nuclear magnetic resonance" (DO:00120) dominate experiments used to identify longer (>100 residues) regions.

The statistics on annotation data for the main branches of the disorder ontology are reported in Figure 2. Only terms one node away from the ontology root are considered and more specific annotations are propagated following the "true path rule", i.e. following the ontology hierarchy, so that parent terms account for children counts.

Different ontology aspects are shown with different colors. In red the "Structural state" terms show as the majority of region records in DisProt are annotated as disordered. Only 5 proteins are annotated with the "Order" term. In the future, curators will be encouraged to also track information about order, in particular when relevant for structural transitions. Transitions are mainly covering folding events ("Disorder to order"), 365 proteins and 36,200 residues, and not the contrary. The majority of interaction partner annotations refers protein and nucleic acid binding. Binding residues are, however, overestimated since in the previous DisProt version, due to hard constraints in the database schema, it was not possible to narrow region boundaries to real interacting positions. Binding positions will become more precise in the future. The new term introduced in DisProt 8, "Biological condensation" (DO:00040) has been assigned to a total of 20 proteins, 29 regions and 2,610 residues. The new "Electron cryomicroscopy" (DO:00128) term, which is a child of "Crystallography", covers 34 proteins, 67 regions and 4,726 residues. Darker segments in Figure 2 indicate the fraction of proteins (left plot) and residues (right plot) for which more than one experimental evidence is available. At the bottom in orange the distribution of "Detection methods" terms. "Proteins" and "Residues" distributions have a similar shape. "Crystallography", which is a parent of "Missing electron density", covers less residues compared to "Spectrometry" and "Optical analysis", indicating that regions identified with crystallographic techniques are shorter on average. Moreover, "Crystallography" has less residues covered by multiple experimental evidence compared to other techniques. In general, disorder annotation is well supported with 44.4% of disordered proteins and 43.2% of the disordered residues backed by two or more literature references.

DisProt website

The DisProt website has been completely redesigned, improving the user experience, visualization and functionalities. Additionally, a big effort was made to develop the DisProt Application Programming Interface (API) to enable users to retrieve a single entry or a region and to perform advanced searches via RESTful endpoints (URLs).

Entry page

The entry page is composed of three main sections. On the top, general information of the protein including name, DisProt ID, organism, sequence length, MobiDB and UniProtKB accession numbers are provided. On the top right, it is possible to select the DisProt version and hide/show ambiguous/obsolete evidence. A download dropdown button allows saving the whole entry data in JSON, TSV (tab-separated) or the corresponding sequence in FASTA format.

A new dynamic feature viewer allows to visualize DisProt evidence mapped onto sequence.

The feature viewer shows two tracks by default, DisProt consensus and domains, the latter including Pfam [START_REF] El-Gebali | The Pfam protein families database in 2019[END_REF] and Gene3D (37) annotation. DisProt consensus is generated by merging region annotation following the hierarchy of the ontology terms. In the last step, when merging the four main ontology branches, priority is given to "Interaction partner", "Structural transition", "Structural state" and "Disorder function", respectively.

The feature viewer can be expanded to see sub tracks and it is possible to zoom in and out specific regions, customize the view and download a high quality image. Region tooltips are activated on mouse over and provide detailed information about the corresponding annotation.

Region details are also provided on the bottom of the page, organized in a dynamic list of boxes. A search box, which supports regular expressions, allows to filter the list of regions.

The filter is also applied to the feature and sequence viewers (right) in real time, for example, by typing "nuclear magnetic resonance" it is possible to select only region evidence from NMR experiments.

Browsing and searching data

DisProt implements both a database and a BLAST search [START_REF] Altschul | Basic local alignment search tool[END_REF], both available from the "Browse" page. The database search allows to compose a query against several fields, which can be combined to meet multiple criteria. All search fields accept regular expressions, and "Free text" allows to search against the entire database content. For example, by searching "p53" in "Free text" and "homo | mus" in "Organism" will return all human and mouse proteins with the "p53" string somewhere in the corresponding database records (protein name, annotation reference title, etc.). Query results are displayed in the table below the search box. DisProt API DisProt provides programmatic access to perform a search through REpresentational State Transfer (or RESTful) Web Service API. A single entry or evidence can be retrieved by using DisProt or UniProtKB identifiers. Additionally, a text search against the entire database can be performed by specifying query fields (name, organism, etc.) directly as URL parameters in the HTTP request. JSON, TSV and FASTA formats are supported. or residues (right) for which more than one piece of evidence is available.

  . In the past the DisProt interface had been kept open for limited time slots. Now the new DisProt interface is always open and new releases will be delivered more frequently. DisProt versioning has been improved. A numeric identifier indicates the version of the database entry, e.g., version "8.0", and a "<year>_<month>" code indicates the version (timestamp) of annotated data, e.g., "2019_09".

FIGURESFigure 1 .

 1 FIGURES

Figure 2 .

 2 Figure 2. Distribution of disorder annotation terms. Terms belong to the Disorder Ontology and only those one node away from the ontology root are shown. Annotation counts for child terms are propagated to parents up to the root. The dark segments correspond to proteins (left)

Figure 1 .

 1 Figure 1. Distribution of region length. Regions shorter than 100 residues (left) are binned in groups of 10 residues. Regions longer than 100 (right) are binned in 100 residues. The tick labels indicate the lower bound which is included. Gray bars refer to the previous release (DisProt 7).

  Table columns are customizable and the result can be downloaded in JSON, TSV or FASTA format.

ACKNOWLEDGEMENTS

DisProt is a service of the Italian ELIXIR node. Part of this work was done in the context of an ELIXIR Implementation Study linked to the ELIXIR Data platform.

CONCLUSIONS AND FUTURE WORK

In the previous release, DisProt disorder annotations were polished and major errors were fixed but the number of newly annotated proteins was limited. In DisProt 8, disorder annotations doubled and a robust infrastructure has been put in place to leverage and accelerate the annotation process. The database format has been improved to be flexible enough to capture essential information from the literature but, at the same time, keeping disorder representation