Quantum Bio-Inorganic Chemistry (QBIC) Society Special Collection

Prof. Lubomír Rulíšek,* [a] Prof. Maja Gruden,* [b] Dr. Maylis Orio,* [c] Dr. Robert J. Deeth* [d] Metals and their ions are 'elements of life'. They are intimately involved in most fundamental biological processes. Almost all redox reactions occurring in Nature as well as 'difficult' chemical transformations (O2 bond cleavage, N2 and C-H bond activations), including formally spinforbidden processes, are catalyzed by metalloproteins. Of equal importance is the structure and function of RNA and DNA that critically depends on the presence of metals and it comes as no surprise that metal-containing complexes are to be found among anticancer drugs.

The enormous progress made in the field of bioinorganic chemistry over seven decades -since the first reports on the presence of metal ions in biological systems appeared -has been always supported by physico-chemical and spectroscopic methods. Since the 1990's, computational and theoretical chemistry has played an indispensable and integral role in bioinorganic research. At the same time, the need for accurate treatments of open-shell, strongly-correlated, spin-coupled, entangled, or 'relativistic' systems -just to mention few of the common characteristics of bioinorganic systems -has led to the development of advanced quantum chemical methods and also stimulated formulations of new DFT functionals. The field of quantum chemical modelling of bioinorganic systems has been given a name: quantum bioinorganic chemistry (QBIC). The QBIC community evolved around the series of QBIC conferences, the first one organized by Rob Deeth in Warwick, UK in 1999, the last ones taking place in 2018 (Bath, UK), 2019 (Marseille, France) and 2021 (on-line, hosted by University of Groningen, Netherlands). Proceedings of the QBIC conferences also provide historic evidence of how the field developed from its infancy to maturity (see Figure 1 for graphical visualization of the ). From early attempts to model reaction mechanisms of metalloproteins by employing small model systems to evaluate interactions of metal ions with biomolecules in the gas-phase, or to reproduce rough trends in spectroscopic characterization of inorganic or bioinorganic systems, quantum bioinorganic chemistry has progressed enormously, as we hope this issue will show.

Nowadays, it provides a robust and often quantitative framework that supports state-of-the-art experiments, and sometimes even directs experimental efforts. We believe that in near future, the theoretical expertise will be used for the rational design of new bioinorganic systems with desired properties and reactivities. This requires high cooperativity, transferring knowledge, and exchanging ideas which further motivated the foundation of the Quantum Bio•Inorganic Chemistry Society in 2018 to bring together scientists from academia and industry working in the broad fields of theoretical and computational inorganic, bioinorganic, and biological chemistry. Among the activities of the Society are the organization of regular international scientific meetings, the support of summer/winter schools oriented towards young scientists, and the coordination of networking actions. Lastly, the review paper of Jensen (https://doi.org/10.1002/cbic.202100554) deals with one of the most abundant 3d-elements in nature: zinc(II). Zn(II) is not only utilized in many biological processes, but also plays a major role in the immune system, as is pointed out and thoroughly analyzed in the review.

Concerning original research articles, it can be highlighted that state-of-the-art studies of one of the most intriguing metalloenzymes in Nature -nitrogenase -are reported independently by three research groups, employing various theoretical approaches and methods. Nitrogenase is the only enzyme in nature that can fix N2 from the air and contains a 'formidable' cofactor comprising seven irons and one molybdenum connected by sulfide bridges. Wei and Siegbahn (https://doi.org/10.1002/chem.202103745) address computationally the loss of a sulfide, and argue that the barrier for the loss is much lower than the barrier for protonating the central carbide which is then protonated in the E3 to E4 step. They also present a complicated transition state for H2 formation and correlate the computed data to experiments. Jiang and Ryde (https://doi.org/10.1002/chem.202103933) used quantum mechanical and molecular mechanical (QM/MM) calculations to present a contradicting hypothesis, assuming that none of the sulfide ligands dissociate. This leads to a formulation an alternate reaction mechanism. Finally, Thorhallsson and Bjornssonn (https://doi.org/10.1002/chem.202102730) present an exploration of the potential energy surface of the E2 state. They utilize both cluster-continuum and QM/MM calculations and consider various classes of E2 models: including terminal hydrides, bridging hydrides with a closed or open sulfide-bridge. A path back to the resting state, by H2 evolution via a hydride-proton reaction is found.

A similar topic as reviewed by Wojdyla and Borowski (vide supra), reaction selectivity (regioand chemoselectivity), is studied and discussed by de Visser and coworkers (https://doi.org/10.1002/chem.202100791) on an example of nonheme iron dioxygenase. They point out the role of charged residues and show that the selectivity is guided through electrostatic perturbations inside the protein from charged residues, external electric fields and electric dipole moments. They notice that the studied dioxygenase is a nice example of so-called negative (or non-Bell-Evans Polanyi) catalysis where the formation of a less thermodynamically stable product is promoted via the lower activation energy with respect to its more stable counterpart (product). Along similar lines, Bleher, Comba, and coworkers (https://doi.org/10.1002/chem.202103452) study the [(L 1 )(Cl)Fe IV =O] + complex/intermediate (where L 1 is a tetradentate bispidine), presumably the most reactive small-molecule halogenase model. DFT calculations show that the intermediate has, as observed experimentally, a lower energy barrier to the halogenation than the hydroxylation product; and thus, calculations nicely explain the origin of the observed chemoselectivity. Heme chemistry is represented by the contribution of Rozza, Harvey, Olah and coworkers (https://doi.org/10.1002/chem.202200930). They present a computational study of the mechanism of biochemical NO-sensing. On an example of the NO-receptor in humans -soluble guanylyl cyclase (sGC) -a histidine-ligated heme protein, they show how the NO binding activates a downstream signaling cascade.

Two of the most advanced, but still reasonably practical, methods are the density matrix renormalization group-self consistent field (DMRG-SCF) and domain-based local pair natural orbital-coupled cluster DLPNO-CCSD(T) approaches. They are considered, by many applicationoriented quantum chemists as the major developments in WFT in the last two decades. The application of DLPNO-CCSD(T) in the realm of bioinorganic chemistry is nicely demonstrated by Drousou and Pantazis (https://doi.org/10.1002/chem.202101567), in the case of the wateroxidizing complex of photosystem II. Their results enable the evaluation of conflicting models for the S3 state of the oxygen-evolving complex (OEC) and thus provide deeper theoretical insight into its reaction mechanism.

Csizi, Reiher, and coworkers (https://doi.org/10.1002/chem.202103937) apply DLPNO-CCSD(T) to evaluate the spin-statedependent reaction energies and structural effects at the active site of naphthalene (Rieske) dioxygenase. Their results strongly indicate an allosteric control of the substrate on Rieske dioxygenase reactivity to avoid unproductive O2 activation. Freitag, González, and coworkers employed DMRG to calculate the low-lying excited states of CpMo(CO)2NO (https://doi.org/10.1002/cphc.202100549) demonstrating a novel, relatively easy-to-use, procedure, employing the maximum single-orbital entropy scheme, applied to several states, to select an appropriate active space. This has often been the bottleneck of previous multi-reference methods that this new approach may overcome. Three insightful and conceptual papers deal with classical concepts in bioinorganic chemistry that are shown to be still very useful, despite the availability of more and more accurate and (at the same time) computationally demanding methods. Deeth (https://doi.org/10.1002/ejic.202100936) discusses the failure of crystal-field theory (CFT) for simple linear complexes, such as CuCl2. He proposes a new cellular ligand field (CLF) analysis, including the equatorial toroidal void and presents the 'distributed donation' model that correctly describes the bonding in simple terms. Buchkorn, Deeth, and Krewald (https://doi.org/10.1002/chem.202103775) present a model case of tetrahedral halido metalates ([M II X4] 2-, M=Mn-Cu) an inspirational usage of ab initio ligand-field theory (LFT), coupled with a generalised, symmetry-independent and automated fitting procedure for angular overlap model (AOM) parameters that is even applicable to formally underdetermined or experimentally inaccessible systems.

Li, Nam and coworkers (https://doi.org/10.1002/chem.202103295) revive the multi-state reactivity concept and utilize it for high-valent iron-imido complexes. These can perform C-H activations and sulfimidations, but are far less studied than the more ubiquitous iron-oxo species. It is concluded that their electronic structures may change to perform efficient oxidations, thus emulating the multi-spin state reactivity in Fe IV O systems.

With respect to current societal needs, a very important reaction to be understood and ultimately utilized in industry is H2 production. It is addressed in four papers in this Special Virtual Issue. Barozo and Orio (https://doi.org/10.1002/cphc.202200056) present a study aiming to develop and optimize catalysts for H2 production. Trying to fully understand the reaction mechanism of the Coand Ni-thiosemicarbazone-based complexes, they notice the differences in catalysis when considering ligand-and metal-centered reactivity. This might be important to find the most optimal mechanisms for hydrogen evolution reaction. Natarajan, Stein, Kaur-Ghumaan and coworkers (https://doi.org/10.1002/open.202100238) report a synthesis, crystallization, spectroscopic and computational characterization of a series of new diiron hydrogenase mimic complexes with one or two terminal tris(4-methoxyphenyl)phosphine and different μ-bridging dithiolate ligands that have catalytic activity towards electrochemical proton reduction in the presence of weak and strong acids. They show that the nature of the μ-bridging dithiolate is critical for controlling the reaction and the catalyst performance in the H2 evolving reaction. Arrigoni and Zampella (https://doi.org/10.1002/ejic.202200153) report the design of dinuclear iron systems, originating from the electron rich [Fe2(pdt)(RNC)6] (pdt=CH2(CH2S -)2) complex. The purpose of computationally-aided design was to find a stable inverted (or rotated) disposition of the two FeL3 pyramidal moieties in the dimetallic core which is a key feature of the [FeFe]-hydrogenase cofactor. Finally, a reverse reaction, the H2-oxidation by enzymes, employed for example in the reaction cycle of the molybdenum and cobalt-containing carbon monoxide dehydrogenase The results obtained are discussed from the perspective of the recent findings in the Aβ redox chemistry in AD pathology. Both of these copper-related studies may ultimately lead to important applications in medicine.

Last, but not least, two studies from the realm of nucleic acids are presented. As mentioned above, Zaccaria, Fonseca Guerra and coworkers present a study (https://doi.org/10.1002/cphc.202100529) of how the internal guanine quadruplex channel site interacts with alkaline earth metal (Mg 2+ , Ca 2+ , Sr 2+ , and Ba 2+ ), and (post-)transition metal (Zn 2+ , Cd 2+ , Hg 2+ , and Pb 2+ ) cations, using density functional theory computations. It is found that divalent cations generally bind to the GQ cavity with a higher affinity than conventional monovalent cations (e. g. K + ). Their findings may contribute to a deeper understanding of how pollution metals could induce genomic instability. Nieuwland, Bickelhaupt and coworkers (https://doi.org/10.1002/open.202100231) study quantum chemically the influence of nucleotide composition and sequence on the stability of double-stranded B-DNA triplets in aqueous solution. Their calculations reveal enhanced stabilities for duplexes possessing a higher GC base pair content.

In summary, we believe that the QBIC Special Virtual Issue is a timely showcase of the contemporary achievements in the field, covering diverse topics, demonstrating an insight obtained by correlating experiment and theory, suggesting new design principles, while not forgetting the older but still useful theories, such as crystal-field/ligand-field theories, or multi-state reactivity. We thank all the authors for contributing to the issue and wish them success in their future work.

Figure 1 :

 1 Figure 1: The typical size of the bioinorganic systems studied (A) in late 1990's; (B) in early 2020's. Both are taken from the work of Ulf Ryde. A is the optimized structure of Cu(imidazole)2(SCH3)(S(CH3)2) + in Cs symmetry as the model of the active site of plastocyanine (https://doi.org/10.1021/ja962381f); B is the active site model of nitrogenase from this collection (https://doi.org/10.1002/chem.202103933).

  //doi.org/10.1002/cphc.202200053) employing QM/MM methods. They test the hypothesis whether a water molecule present in the active site might act as a nucleophile upon formation of the new C-O bond.Another bioinorganic 'classic' is presented by Han Du and Noodleman (https://doi.org/10.1002/cphc.202100831). Very meticulous and state-of-the-art computations of Mössbauer isomer shift and quadrupole splitting properties for the resting oxidized "as-isolated" O[Fea3 3+ -H2O-CuB 2+ ] state of the Fe/Cu dinuclear center clusters of cytochrome c oxidase (CcO) are presented, employing the OLYP-D3(BJ) density functional method. It is nicely demonstrated how tiny structural changes are imprinted in the Mössbauer parameters; the computations thus provide an important link in structure/spectroscopy correlations. The same adjective (classic) can be used for the study of Stanczak, Straka and coworkers (https://doi.org/10.1002/cphc.202200076) that examines the "enigmatic" [Cu2O2] peroro-vs. bis-µ-oxo equilibrium including various physical contributions, such as relativity and solvent effects, plus a calibration of various wavefunction and DFT methods. The presented results provide insight into understanding of the reactivity of the coupled binuclear copper proteins and their biomimetic models.A very interesting contribution comes from Auth, Stein, and coworkers (https://doi.org/10.1002/chem.202103130). In recent years, a lot of insight into the origin of bioinorganic reactivity has been obtained from gas-phase (fragmentation) experiments. Herein, the authors report a study of the high-valent tetraalkylcuprates(III) and -argentates(III) which are key intermediates of copper-and silver-mediated C-C coupling reactions. Correlating collisioninduced dissociation experiments with advanced quantum-chemical calculations and kinetic computations, they obtained detailed mechanistic insights for these important and catalytically relevant gas-phase reactions. The heavier counterpart of the Cu(III) and Ag(III), namely gold(III), is investigated by Engbers, Klein, and coworkers (https://doi.org/10.1002/chem.202200599). Specifically, the C(sp 3 )-H and O-H bond breaking steps in the oxidation of 1,4-cyclohexadiene and phenol by a Au(III)-OH complex are studied computationally. For both types of bonds the initial X-H cleavage step proceeds via concerted proton coupled electron transfer (cPCET) which seems to be quite distinct from the chemistry of analogous Cu(III)-OH species complexes studied by the Tolman group. Hence, the authors issue a warning that care should be taken when transferring concepts between Cu-OH and Au-OH species. Similarly, in concerted experimental and theoretical efforts, Heinrich, Kulak, and coworkers (https://doi.org/10.1002/chem.202102601) demonstrate that an exchange of the α-amino acid Gly with the β-amino acid β-Ala in the [Cu(II)ATCUN] complex (ACTUN = GGH tripeptide) reinstates ROS production ( • OH and H2O2), i.e., it 'wakes up' the originally redox silent species. This replacement is also accompanied by higher cytotoxic activity against several human cancer cell lines where β-Ala2 peptide complexes have lower IC50 values compared to Gly2. Of a similar importance is the study of Arrigoni, Bertini, and coworkers (https://doi.org/10.1002/ejic.202200245) dealing with the oxidative stress and copper dyshomeostasis which are well established factors in the course of Alzheimer's disease (AD). The computations yield detailed energy profiles for the final two steps of O2 reduction to superoxide and hydroperoxide radicals by ascorbate, on different Cu(II) ⋅ Aβ coordination models.

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D R Quiroga-Martinez

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Enhanced mismatch negativity in harmonic compared to inharmonic sounds

Many natural sounds have frequency spectra composed of integer multiples of a fundamental frequency. This property, known as harmonicity, plays an important role in auditory information processing. However, the extent to which harmonicity influences the processing of sound features beyond pitch is still unclear. This is interesting because harmonic sounds have lower information entropy than inharmonic sounds. According to predictive processing accounts of perception, this property could produce more salient neural responses due to the brain's weighting of sensory signals according to their uncertainty. In the present study, we used electroencephalography to investigate brain responses to harmonic and inharmonic sounds commonly occurring in music: piano tones and hi-hat cymbal sounds. In a multi-feature oddball paradigm, we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants in listeners with and without congenital amusia-an impairment of pitch processing. As hypothesized, we observed larger amplitudes and earlier latencies (for both MMN and P3a) in harmonic compared to inharmonic sounds. These harmonicity effects were modulated by sound feature. Moreover, the difference in P3a latency between harmonic and inharmonic sounds was larger for controls than amusics. We propose an explanation of these results based on predictive coding and discuss the relationship between harmonicity, information entropy, and precision weighting of prediction errors.

Introduction

Many naturally occurring sounds are comprised of frequencies that are integer multiples of a single, fundamental frequency (F0). These are termed harmonic sounds and include human and animal voices as well as many musical instrument sounds. Harmonic sounds have an easily detectable pitch, a subjective perceptual quality that enables them to be ordered from low to high [START_REF] Mcdermott | Audition[END_REF][START_REF] Oxenham | Pitch Perception[END_REF] . Pitch information is used for solving many cognitive tasks. In tonal languages, pitch contours determine the meaning of words, while in non-tonal languages they are used for prosody. In music, pitch is critical for the processing of melody and harmony [START_REF] Huron | Voice Leading: The Science behind a Musical Art[END_REF] . Pitch information is also used in auditory scene analysis, where simultaneously perceived sounds can be segregated using pitch discrepancies [START_REF] Bregman | Auditory Scene Analysis: The Perceptual Organization of Sound[END_REF][START_REF] Wightman | The Perception of Pitch: The pitch of a sound wave is closely related to its frequency or periodicity-but the exact nature of that relation remains a mystery[END_REF] . Inharmonic sounds possess frequency spectra that do not follow the harmonic series. These are often described as metallic, crackling, sizzling or noisy. Inharmonic sounds are sparingly used in Western tonal music (mainly in percussion instruments) as they do not produce a clear pitch sensation that may be used to convey melody.

Historically, research in auditory neuroscience focused on simple stimuli, mostly limited to sine waves and harmonic complex tones (McPherson & McDermott, 2020a) . Not much is known about the neural mechanisms of perception of inharmonic sounds or how inharmonicity influences the perception of auditory features other than pitch. Recent studies have shown harmonicity to be an important factor in several auditory tasks not directly related to pitch.

Violations of harmonicity impair speech recognition in cocktail-party scenarios [START_REF] Popham | Inharmonic speech reveals the role of harmonicity in the cocktail party problem[END_REF] .

Harmonic sounds are easier to discriminate than inharmonic sounds over time delays, suggesting more efficient coding of harmonic sounds in memory [START_REF] Mcpherson | Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory[END_REF] . Harmonic signals are also more easily detected in noise than are inharmonic signals, pointing to an important role of harmonicity in auditory scene analysis (McPherson et al., 2020) .

Taken together, these studies suggest that harmonicity is important for processing different auditory features. It is thus likely that the ease of detection of deviances in features, such as timbre, intensity or location, could depend on harmonicity. This view is also consistent with a predictive coding account of auditory perception, where sensory information, usually cast as ascending prediction error, is weighted by the information entropy (i.e., uncertainty or inverse precision) of the stimuli [START_REF] Clark | Surfing uncertainty: Prediction, action, and the embodied mind[END_REF][START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Rao | Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects[END_REF] . As inharmonic sounds have higher spectral entropy, prediction errors elicited by inharmonic deviants may be down-weighted in comparison to harmonic deviants.

2

In this study, we used electroencephalography to investigate the brain responses to harmonic and inharmonic sounds in both typical listeners and participants with congenital amusia (CA)an impairment in pitch processing that cannot be attributed to causes such as intellectual disability, lack of music exposure, or brain damage [START_REF] Peretz | Neurobiology of Congenital Amusia[END_REF] .

Aiming for ecological validity, our present study used sounds commonly occurring in popular music: piano tone (harmonic, low spectral entropy) and hi-hat cymbal (inharmonic, high spectral entropy). In a multi-feature oddball paradigm [START_REF] Näätänen | The mismatch negativity (MMN): Towards the optimal paradigm[END_REF][START_REF] Paavilainen | The mismatch-negativity (MMN) component of the auditory event-related potential to violations of abstract regularities: A review[END_REF] , we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants. These ERPs are elicited without attentional control and index the brain's ability to perform automatic comparisons of incoming auditory stimuli [START_REF] Garrido | The mismatch negativity: A review of underlying mechanisms[END_REF][START_REF] Paavilainen | The mismatch-negativity (MMN) component of the auditory event-related potential to violations of abstract regularities: A review[END_REF] . In contrast to traditional paradigms that employ a single type of deviant, multi-feature paradigms record MMN responses to several sound features in a relatively short time, which make them an optimal choice to address our question [START_REF] Kliuchko | A window into the brain mechanisms associated with noise sensitivity[END_REF][START_REF] Vuust | New fast mismatch negativity paradigm for determining the neural prerequisites for musical ability[END_REF] .

Based on previous studies highlighting the role of harmonicity in auditory scene analysis, we hypothesized that timbre, intensity, and location deviants would elicit larger MMN and P3a responses for harmonic than for inharmonic sounds. Finally, given that listeners with congenital amusia are impared in the processing of pitch information (which relies on harmonicity), here we also explored whether amusics process harmonic and inharmonic sounds differently from their matched non-musician controls [START_REF] Cousineau | The basis of musical consonance as revealed by congenital amusia[END_REF][START_REF] Marin | Perception of musical timbre in congenital amusia: Categorization, discrimination and short-term memory[END_REF] .

Methods

The code and materials employed to conduct the experiment and analyses presented here can be found at: https://doi.org/10.17605/OSF.IO/JSEU8 . Due to data protection regulations, data cannot be publicly shared, but can be made privately available upon reasonable request.

Participants

Thirty-four participants took part in the experiment (same participants as in Quiroga- [START_REF] Quiroga-Martinez | Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences[END_REF], 17 amusics and 17 matched controls (see Table 1). All subjects were recruited in the Lyon area in France, were French speakers, and were screened for congenital amusia using the Montreal Battery of Evaluation of Amusia (MBEA) [START_REF] Peretz | Varieties of Musical Disorders[END_REF] . The total MBEA scores including six subtests were significantly lower for amusics than for controls, t(25.6) = -10.49, p < 3 .001; as were the average combined scores for the three pitch subtests, t(23.7) = -11.36, p < .001. An individual was considered amusic if the total MBEA score was less than 23 (maximum score = 30) or their average score for the pitch subtests was lower than 21.7 (maximum score = 30). We measured pitch discrimination thresholds (PDT) using an adaptive tracking staircase procedure [START_REF] Tillmann | Congenital amusia: A short-term memory deficit for non-verbal, but not verbal sounds[END_REF] . PDTs were significantly larger for amusics in comparison to controls. The groups did not differ significantly in age, years of education, or musical training. The study was conducted and approved by a national ethics committee.

Participants gave their written informed consent prior to the experiment and received a small financial compensation for their time.

Stimuli

Two types of sound sequences were presented to participants, corresponding to a modified version of the optimal MMN paradigm [START_REF] Näätänen | The mismatch negativity (MMN): Towards the optimal paradigm[END_REF] . In the piano condition, participants listened to repeating piano tones with a fundamental frequency at C3 pitch (F0 = 262 Hz). In the hi-hat condition, broad-band percussive hi-hat cymbal sounds were used (Figure 1). The sounds were samples taken from the Cubase sample library (Steinberg Media Technology, version 8). The spectral entropies of the sounds were 2.94 bits and 9.66 bits for the piano and hi-hat tones, respectively (as calculated with AntroPy v.0.1.4; [START_REF] Vallat | AntroPy: Entropy and complexity of (EEG) time-series in Python (0.1.4)[END_REF] . During the experiment, one sound was played repeatedly, and a deviant was pseudorandomly introduced in every group of four consecutive stimuli in the sequence. No two deviants were played consecutively, and no deviant feature was presented again before a whole iteration of the five features was played. Each condition (piano or hihat) was presented in a separate block lasting approximately 13 minutes. Both sequences included the same number of standard and deviant tones. The piano block and the hi-hat block, together with two other conditions involving melodies with different complexity levels (presented in Quiroga- [START_REF] Quiroga-Martinez | Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences[END_REF], were counterbalanced between participants and their order matched across groups. Other conditions were included in the experiment, which have been reported elsewhere (Quiroga- [START_REF] Quiroga-Martinez | Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences[END_REF] . The sounds lasted 250 ms each, were loudness normalized, and were presented excluding any silent gaps between the sounds. Pitch, intensity, timbre, location, and rhythm deviants were introduced in the melodies. Pitch deviants appeared in the piano condition but were not part of the analysis because there are no pitch deviants in the hi-hat condition (results for the pitch deviant in the piano block can be seen in Figure 2 in Quiroga- [START_REF] Quiroga-Martinez | Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences[END_REF]. All deviants were created with Adobe Audition (Adobe Systems Incorporated, version 8) by modifying the standard tones as follows: intensity: -12 dB gain; timbre: a combination of filters (low-shelf -10 dB at 500 Hz, peak +10 dB at 2 kHz, notch filter at 6 kHz, all filters Q = 1); location: leftward bias (20 ms time shift between channels); rhythm: -60 ms for sound onset. Note that rhythm violations implied a shortening of the preceding tone and a lengthening of the actual deviant tone by 60 ms. The rhythm deviants were excluded from the analysis due to baseline contamination issues, as reported in Quiroga- [START_REF] Quiroga-Martinez | Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences[END_REF]. Overall, a total of 2339 standards and 153 (5%) deviants per feature were presented in each of the two conditions.

Procedure

Participants were informed about the procedures at their arrival, willingly gave their written consent and filled out the required forms. Subsequently, EEG caps were placed on their scalp accompanied by conductive gel applied to the electrodes. A Sennheiser HD280 Pro headset was carefully placed on top of the EEG cap with foam padding to avoid pressure on electrodes.

The impedances were checked again after the headphones were on. All participants had the sound volume set to an identical, comfortable level. During testing, participants were looking at a computer screen from a distance of about 1.5 m, sitting on an armchair inside a sound-attenuated booth, electrically-shielded with a Faraday cage. They were told sounds would be playing in the background and were instructed to watch a movie of their choice while ignoring the auditory stimuli. Additionally, they were asked to remain still and relaxed, knowing there would be pauses between blocks during which they could stretch and change posture.

During stimulation, the blocks were presented in such a way that for 9 matched pairs of participants, the counterbalanced piano and hi-hat conditions, together with the two additional complexity conditions, came before two counterbalanced additional conditions, whereas for the remaining pairs the order was inverted. Two additional blocks were included at the end of the experiment, in which participants listened freely to entire pieces of music. Their analysis, however, is beyond the scope of this article and will be reported elsewhere. The whole recording session lasted around one hour and a half, plus half an hour of preparation.

EEG recording and preprocessing

Scalp potentials were recorded with a 64-channel Biosemi system with active electrodes and a sampling rate of 1024 Hz. Additional electrodes were used to track horizontal and vertical eye movements. Data analyses were conducted with MNE-Python v.0.23.0 [START_REF] Gramfort | MNE software for processing MEG and EEG data[END_REF] .

EEG signals were first cleaned from eyeblink artifacts using independent component analysis with a semiautomatic routine (fastICA algorithm). Visual inspection was used as a quality check.

After removing ICA components, the raw signals were filtered with a pass band of 0.5 -35 Hz and re-referenced to the mastoids. Epochs from -100 ms to 400 ms from tone onsets were extracted and baseline corrected with a prestimulus baseline of 100 ms. Epochs with an amplitude exceeding 150 μV were rejected to further clean the data from remaining artifacts. For each participant, event-related potentials (ERP) were obtained by averaging epochs for the standard tones and each of the deviant features separately, per condition. Standard tones preceded by a deviant were excluded from the averages. Both deviant-specific MMN and P3a

responses were calculated by subtracting standard from deviant ERPs for each feature and condition.

Statistical analyses

We performed analyses on mean amplitudes and latencies for both MMN and P3a responses.

MMN peak latencies were extracted within a time window of 70 -250 ms, whereas P3a peak latencies were extracted for 150 -350 ms. Mean amplitudes were obtained from electrodes Fz, F1, F2, F3, F4, FCz, FC1, FC2, FC3, FC4 and calculated as the average activity ± 25 ms around the participant-wise peak, for each condition (piano or hi-hat) and feature (intensity, location, timbre). The chosen electrodes correspond to those that typically show the largest responses in MMN studies and exhibited the largest P50 amplitudes (thus making sure that they properly captured auditory evoked activity). Using R v. 4.0.5 (R Core Team, 2021) and the ezANOVA library [START_REF] Lawrence | ez: Easy Analysis and Visualization of Factorial Experiments (4.4-0[END_REF] , several mixed ANOVAs including within-and between-subject effects were run on the mean amplitudes and latencies of MMN and P3a difference waves. Factors included group (amusics and control), condition (piano and hi-hat), and sound feature (intensity, location, and timbre). Mauchly's test of sphericity was checked for each ANOVA and no correction was applied. Post-hoc, pairwise contrasts were also performed with the emmeans library [START_REF] Lenth | Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity[END_REF] after fitting linear mixed-effects models using lme4 library [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF] with the two main effects and their interactions as predictors.

Results

Consistent with the literature [START_REF] Näätänen | The mismatch negativity (MMN) in basic research of central auditory processing: A review[END_REF][START_REF] Polich | Neuropsychology and neuropharmacology of P3a and P3b[END_REF], the MMN and P3a manifested themselves as a fronto-central negativity and a fronto-central positivity, respectively (Figure 2 and 3).

MMN analysis

Mixed effects ANOVA analyses revealed significant main effects and two-way interactions on MMN mean amplitudes and peak latencies for group, condition, and feature (Figure 5). Lines connect measurements for individual participants.

For mean amplitudes (see Appendix 1), the main effect of condition was significant F(1,32) = 101.129, η 2 = 0.338, p < .001, with larger MMNs for piano tones compared to hi-hat tones. No significant main effects of group and feature were found, with F(1,32) = 0.063, p = .803 and F(2,64) = 1.131, p = .329 respectively. No significant interaction was found between group and condition F(1,32) = 0.486, p = .491. Furthermore, the interaction between group and feature was not significant either, with F(2,64) = 2.561, p = .085. Interestingly, a significant interaction was found for condition and feature with F(2,64) = 24.485, η 2 = 0.068, p < .001. No significant three-way interaction between group, condition and feature was observed, F(2,64) = 0.775, p = .465. By looking at the post hoc pairwise comparison (Table 2), we can observe that there are significant differences in amplitude, contrasting hi-hat and piano conditions, for all features. We notice in Table 2 that the estimates of between-condition MMN differences (hi-hat -piano) for intensity and location were relatively high compared to timbre. This is reflected in the significant differences in between-condition contrasts comparing intensity to timbre (p < 0.001) and location to timbre (p = 0.001) but not intensity to location (p = 0.504). Taken together, these differences account for the significant condition-by-feature interaction.

For mean latencies (see Appendix 2), the main effect of feature was significant F(2,64) = 9.252, η 2 = 0.073, p < .001. No significant main effects were found for group and condition, with F(1,32) = 0.195, p = .662 and F(1,32) = 0.475, p = .496 respectively. No significant interaction was found for group-by-condition F(1,32) = 0.064, p = .802 or group-by-feature F(2,64) = 0.252, p = .778. Interestingly, an interaction was found for condition and feature with F(2,64) = 11.520, η 2 = 0.105, p < .001. No significant three-way interaction between group, condition and feature was observed, F(2,64) = 0.457, p = .635. By looking at the post-hoc pairwise comparisons decomposing the condition-by-feature interaction (Table 3), we can observe that there are significant differences in latencies, contrasting hi-hat and piano conditions, for the intensity with 23.62 milliseconds (earlier MMN for piano tones, p < 0.001) and timbre with -19.68 milliseconds (earlier MMN for hi-hat tones, p = 0.002). No significant differences in latency between conditions were found for location (p = 0.570). Similarly to MMN mean amplitudes (Table 2), the condition-by-feature interaction could be explained with significant differences in between-condition contrasts comparing intensity to timbre (p < 0.001) and location to timbre (p = 0.025) but not intensity to location (p = 0.065). For P3a mean amplitudes (see Appendix 3), the main effect of feature was significant with F(2,64) = 24.580, η 2 = 0.130, p < .001. No significant main effects of group and condition were found F(1,32) = 0.223, p = .640 and F(1,32) = 0.863, p = .360. Interestingly, a significant condition-by-feature interaction was found with F(2,64) = 44.171, η 2 = 0.180, p < .001. No significant interactions were found for group-by-condition with F(1,32) = 0.341, p = .563 and group-by-feature with F(2,64) = 0.518, p = .598. No significant three-way interaction was found for group-by-condition-by-feature with F(2,64) = 1.493, p = .232. By looking at the post-hoc pairwise comparisons (Table 4), we can observe that there is a significant difference in amplitude, both for location and timbre in both groups, contrasting hi-hat and piano conditions. However, whereas for location, P3a was smaller for hi-hat tones compared to piano tones (as already observed for the MMN), for timbre P3a was larger for hi-hat tones compared to piano tones. This is reflected in the significant difference between location and timbre for differences between conditions (p < 0.001). Furthermore, while intensity contrast was by itself not significant (p = .123), there was a significant difference between intensity and timbre for differences between conditions (p < 0.001). Taken together, these results explain the condition-by-feature interaction.

P3a analysis

For mean P3a latencies (see Appendix 4), the main effects of condition and feature were significant, F(1,32) = 14.474, η 2 = 0.033, p < .001 and F(2,64) = 45.605, η 2 = 0.289, p < .001, respectively. No significant main effect was found for group F(1,32) = 0.297, p = .590. No significant interaction was found for group-by-feature F(2,64) = 1.203, p = .307. Interestingly, an interaction was found for group-by-condition F(1,32) = 5.094, η 2 = 0.012, p = .031 and condition-by-feature F(2,64) = 4.892, η 2 = 0.039, p = .011. No significant three-way interaction between group, condition and feature was observed, F(2,64) = 0.242, p = .786.

Table 5. Pairwise contrasts of P3a peak latencies between conditions for each feature separately, as well as pairwise comparisons between features for differences between conditions (contrasts of contrasts).

Data is pooled from both groups. Significant contrasts are highlighted in bold and marked with an asterisk.

Standard effect sizes (d) are calculated as the difference between conditions divided by the square root of the sum of the residual and the random effects variance. Post hoc pairwise comparisons (Table 5) revealed significant differences in latencies, contrasting hi-hat and piano conditions, for intensity with 31.59 milliseconds and location with 18.53 milliseconds (earlier P3a with piano tones in both cases). For timbre, no significant differences in latency between conditions were found (p = .440). Furthermore, while the difference between hi-hat and piano conditions was significant for the control group (23.12 milliseconds; 95% CI: 9.40-36.83; t(160) = 3.33; d = 0.57; p = .001), this was not the case for amusics (5.90 milliseconds; 95% CI: -7.81-19.62; t(160) = 0.85; d = 0.15; p = .396).

Feature

Discussion

In this study we found that the harmonicity of musical sounds enhances MMN in response to intensity, timbre and location deviants, both in control and amusic participants. This is consistent with the hypothesis that harmonicity influences the processing of auditory features other than pitch. In the following, an explanation based on precision-weighting of prediction errors will be discussed.

Larger MMN for harmonic sounds

Harmonic sounds produced larger MMNs than inharmonic sounds, as evidenced by statistically significant main effects of condition (piano vs. hi-hat) on MMN amplitudes. Piano tones had clear harmonicity, whereas hi-hat tones had a more broadband spectrum (Figure 1). Other acoustic features were matched between the two: duration, impulsivity (sharp attack and fast decay as both types of sounds are generated by a single percussion), intensity, location; both had ecological validity. One possible interpretation for the larger MMN amplitudes in harmonic sounds comes from a predictive coding account of perception [START_REF] Clark | Surfing uncertainty: Prediction, action, and the embodied mind[END_REF][START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Rao | Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects[END_REF] . According to predictive coding, the brain employs a generative model to form predictions about the incoming sensory stimuli. The discrepancies between predictions and the sensory data, in the form of prediction errors, are used to update the generative model and enable better predictions in the future. This system is suggested to be hierarchical, with higher levels predicting the future states of lower levels, and the hierarchy is thought to be reflected in the neuroanatomical structure of the nervous system [START_REF] Kanai | Cerebral hierarchies: Predictive processing, precision and the pulvinar[END_REF] . In predictive coding, the generative model is only updated by prediction errors with low self-estimated sensory uncertainty, or high precision . This precision-weighting process enables the system to "filter out" sensory data that is noisy, uncertain or otherwise unreliable and is analogous to attention [START_REF] Kanai | Cerebral hierarchies: Predictive processing, precision and the pulvinar[END_REF][START_REF] Koelsch | Predictive Processes and the Peculiar Case of Music[END_REF] . ERP responses to deviances, such as MMN, are thought to be electrophysiological markers of ascending precision-weighted prediction errors [START_REF] Garrido | Evoked brain responses are generated by feedback loops[END_REF][START_REF] Garrido | Evoked brain responses are generated by feedback loops[END_REF][START_REF] Garrido | The mismatch negativity: A review of underlying mechanisms[END_REF][START_REF] Koelsch | Predictive Processes and the Peculiar Case of Music[END_REF][START_REF] Lecaignard | Neurocomputational Underpinnings of Expected Surprise[END_REF][START_REF] Winkler | Evidence from auditory and visual event-related potential (ERP) studies of deviance detection (MMN and vMMN) linking predictive coding theories and perceptual object representations[END_REF][START_REF] Garrido | Evoked brain responses are generated by feedback loops[END_REF][START_REF] Garrido | The functional anatomy of the MMN: A DCM study of the roving paradigm[END_REF], 2009;[START_REF] Koelsch | Predictive Processes and the Peculiar Case of Music[END_REF][START_REF] Lecaignard | Neurocomputational Underpinnings of Expected Surprise[END_REF][START_REF] Winkler | Evidence from auditory and visual event-related potential (ERP) studies of deviance detection (MMN and vMMN) linking predictive coding theories and perceptual object representations[END_REF]; see also [START_REF] Grimm | The processing of frequency deviations within sounds: Evidence for the predictive nature of the Mismatch Negativity (MMN) system[END_REF] for a related, predictive account of MMN generation ) .

Precision of prediction errors is related to Shannon entropy, a metric that captures the amount of information content (uncertainty, expected surprise) of a given signal [START_REF] Shannon | A Mathematical Theory of Communication[END_REF] . In individual sounds, entropy crucially depends on the amount of periodicity in the waveform shape and is thus directly related to harmonicity. Harmonic sounds have low spectral entropy because their waveforms can be reliably predicted using low amounts of information (as their frequency spectra consist of F0 and its integer multiples). Conversely, sounds that are inharmonic have higher spectral entropy and their waveforms are harder to predict (the frequency components do not form a harmonic series). Thus, the precision of prediction errors arising from deviances in inharmonic sounds is bound to be lower than in harmonic sounds. Consequently, the lower

Differences between features in harmonic and non-harmonic sounds

We found that the strength of MMN responses was dependent on sound feature in different ways for the two types of sounds (piano vs. hi-hat), as evidenced by the significant interaction effect between condition and feature for MMN amplitudes and latencies. While the MMN was generally larger for harmonic than for inharmonic sounds, the differences between MMN amplitudes were less pronounced for timbre than for intensity and location deviants. Additionally, the MMN amplitudes for intensity and location deviants in the inharmonic condition were very small. MMN latencies were also dependent on sound feature, and this relationship was moderated by condition. For intensity deviants, harmonic sounds produced earlier peaks than inharmonic sounds, which is consistent with the idea that harmonic sounds are processed faster. Conversely, for timbre deviants, harmonic sounds produced later MMN peaks. This effect may be attributed to a large P3a response that followed the initial negativity (see below), possibly partly masking the MMN, especially for the hi-hat sound. No differences in latency between harmonic and inharmonic stimuli were found for the location deviants. As broadband noise stimuli are easier to locate than tones [START_REF] Terhune | Localization of Pure Tones and Click Trains by Untrained Humans[END_REF] , the broadband nature of the hi-hat sound may have counterbalanced the expected harmonicity effect. Additionally, MMN latency for location deviants was short (in comparison to latencies observed for classical MMN responses [START_REF] Näätänen | The mismatch negativity (MMN) in basic research of central auditory processing: A review[END_REF] , suggesting that processing of location could be performed faster, on lower levels of the auditory hierarchy.

While MMN amplitudes were smaller for timbre than for other features, P3a amplitudes were larger, and were larger for hi-hat tones than piano tones. P3a indicates when the mental representation of a stimulus is updated [START_REF] Donchin | Surprise!… Surprise?[END_REF] . Because of its longer latency, P3a is suggested to originate at higher levels of the auditory processing hierarchy than the MMN [START_REF] Koelsch | Predictive Processes and the Peculiar Case of Music[END_REF] . The P3a is also associated with the initiation of an automatic attention-orienting response to stimulus deviance [START_REF] Polich | Neuropsychology and neuropharmacology of P3a and P3b[END_REF] . Stronger P3a in our study could indicate a stronger implicit shift of attention to timbre deviants in comparison to other features. This shift may happen faster for timbre deviants, as evidenced by shorter P3a latencies. Furthermore, these strong and fast P3a responses for timbre could have interfered with activity in earlier latencies, potentially resulting in a diminished MMN response.

The distinct pattern of responses for timbre deviants may have to do with the filtering applied to the sounds to generate the deviants. Due to differences in the spectral characteristics of the piano and hi-hat, the filter changed these sounds in a different way. Because the hi-hat sound had a more uniformly distributed frequency spectrum, the filter introduced a timbral change that might have been perceived as a stronger deviance in comparison to the piano sound. It is thus possible that for the timbre condition, the different patterns of MMN and P3a might have resulted not from differences related to harmonicity, but from a more salient spectral change in the hi-hat condition. This problem is inherently related to comparing real-world harmonic and inharmonic sounds that (necessarily) differ in frequency spectra. One way to address this problem would be to investigate natural sounds with artificially manipulated harmonic components (as in [START_REF] Popham | Inharmonic speech reveals the role of harmonicity in the cocktail party problem[END_REF]. Another approach could involve fully synthesized sounds with carefully controlled manipulations in the frequency spectra, yet this may negatively impact the ecological validity of the stimuli. This filter-related restriction however applies only to timbre deviants and does not extend to other auditory features.

Preserved MMN and P3a in congenital amusia for harmonic and non-harmonic sounds

We found significant MMN and P3a responses, both in CA and control participants and no significant main effects of group or group-condition, group-feature or group-feature-condition interactions. This was true for mean amplitudes as well as mean latencies in both MMN and P3a analyses, with the exception of a group-by-condition interaction in P3a latency (discussed below). Whether or not the processing deficits in congenital amusia extend to the processing of harmonicity is debated. It was reported that amusics are insensitive to harmonicity cues, contrarily to beating cues [START_REF] Cousineau | The basis of musical consonance as revealed by congenital amusia[END_REF]; but see [START_REF] Graves | Consonance perception in congenital amusia: Behavioral and brain responses to harmonicity and beating cues[END_REF] , and they might rely on roughness cues more than harmonicity cues to evaluate the pleasantness of a sound [START_REF] Marin | Perception of musical timbre in congenital amusia: Categorization, discrimination and short-term memory[END_REF] . On the other hand, the identification of pitch processing areas with fMRI by contrasting harmonic sounds with noises lead to results very similar to that obtained in controls [START_REF] Norman-Haignere | Pitch-Responsive Cortical Regions in Congenital Amusia[END_REF] , yet with subtle differences revealed by multivariate analyses (Albouy, Caclin, et al., 2019) . During active pitch short-term memory tasks, functional impairments are observed in a fronto-temporal network in congenital amusia [START_REF] Albouy | Impaired pitch perception and memory in congenital amusia: The deficit starts in the auditory cortex[END_REF]Albouy, Peretz, et al., 2019) [START_REF] Moreau | Pitch discrimination without awareness in congenital amusia: Evidence from event-related potentials[END_REF][START_REF] Zendel | Attending to Pitch Information Inhibits Processing of Pitch Information: The Curious Case of Amusia[END_REF] , whereas abnormalities might be observed in more complex sound sequences (Quiroga- [START_REF] Quiroga-Martinez | Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences[END_REF]. Taken together, these results provide further evidence that deficits in CA might mainly result from abnormalities at higher levels of the perceptual hierarchy, possibly related to impaired top-down connectivity in the frontotemporal network [START_REF] Albouy | Impaired pitch perception and memory in congenital amusia: The deficit starts in the auditory cortex[END_REF][START_REF] Peretz | Neurobiology of Congenital Amusia[END_REF] . Our results can be also interpreted as evidence that low-level auditory processing in CA remains greatly spared, not only for pitch, but also for harmonicity, even though the deficit might lead to delayed processing with impaired auditory cortex contributions (see [START_REF] Albouy | Impaired pitch perception and memory in congenital amusia: The deficit starts in the auditory cortex[END_REF]. This is consistent with the hypothesis that CA individuals might not have conscious access to the results of low-level processing and might thus be less able to use them in perceptual decision making [START_REF] Peretz | Neurobiology of Congenital Amusia[END_REF] .

In P3a latency analysis, we found a significant group-by-condition interaction. This was accompanied with a significant main effect of condition (piano sounds yielded generally shorter P3a latencies than hi-hat sounds) but not a main effect of group. The strength of this effect is however very small (η 2 = 0.012), much smaller than the main effects of condition (η 2 = 0.033) or feature (η 2 = 0.289). This interaction can be interpreted as suggesting that for inharmonic sounds, the CA individuals require more processing time to consciously detect deviance.

However, because of the small effect size further research is needed to clarify this finding.

Limitations and conclusions

A limitation of our study is that subjects listened to the stimuli passively, without any perceptual tasks. Our goal was to focus on low-level processing of auditory deviances and attention-orientation as measured with the MMN and P3a, respectively. We chose a passive listening approach because it is optimal for evaluating the MMN. More active tasks (such as actively detecting deviants with button-presses) could be used to investigate neural processing that involves higher levels of the perceptual hierarchy, with a focus on later ERPs. These types of tasks could also arguably reveal differences in processing specific to CA that were not found in the current study. Finally, it would be beneficial to investigate a more diverse set of auditory stimuli with different levels of inharmonicity to better understand the relationship between the spectral properties of sound and the MMN. Piano and hi-hat sounds differ not only in harmonicity, but in other features as well (e.g., amplitude envelope, spectral content). Future studies may attempt to control for this issue using either complex tones with jittered frequencies, or use natural sounds with artificially manipulated harmonics. In a recent study with synthetic tones (Graves et al., in revision), we observed larger MMNs for harmonic deviants within a sequence of inharmonic standards than the reverse (inharmonic deviants within a sequence of harmonic standards), paving the way for future studies were individual sound entropies could be carefully controlled in oddball sequences.

The results of the present study provide neurophysiological evidence consistent with the notion that harmonicity plays an important role in human auditory perception, starting in early stages of sound processing in the auditory networks. A detailed investigation into the relationships between early auditory ERPs and harmonicity can provide valuable information about the nature of auditory information processing in the brain.

Figure 1 .

 1 Figure 1. (A) Waveforms for standard hi-hat and piano sounds. (B) Example of the stimulus deviants in each condition for each feature (red rectangles), represented as a power spectrogram showing left and right audio channels for two sound conditions. Vertical axes show (log-spaced) frequency while horizontal axes show the time in seconds. For every condition, standard sounds are intertwined with different feature deviants. Note: the order of the stimuli in this figure is for illustration purposes, see main text for stimulus ordering procedures. (C) Normalized auto-correlations of standard piano and hi-hat sounds in comparison with white noise. Piano sounds are much more periodic than hi-hat sounds and white noise.

Figure 2 .

 2 Figure 2. Time-course of grand-average MMN (blue) and P3a (red) topographies for different features (location, timbre, and intensity), groups (amusics and controls) and conditions (piano and hi-hat)

Figure 4 .

 4 Figure 4. Effect of condition on MMN and P3a responses for each group. Gray traces represent difference waves between piano and hi-hat condition for each participant. Shaded areas indicate 95% confidence intervals (for the difference of difference waves). The displayed activity corresponds to the channel Fz.

Figure 5 .

 5 Figure 5. Mean MMN amplitudes (left) and peak latencies (right) as a function of conditions and features in both groups. Boxes display median and interquartile ranges. Beans depict the estimated densities.

Figure 6 .

 6 Figure 6. Mean P3a amplitudes (left) and peak latencies (right) as a function of conditions and features in both groups. Boxes display median and interquartile ranges. Beans depict the estimated densities. Lines connect measurements for individual participants.

  amplitudes of MMN signals for inharmonic sounds may result from a stronger down-weighting of sensory signals due to the spectral entropy of the acoustic input. Similar effects of information entropy on prediction error responses measured by MMN (or its magnetic counterpart, the MMNm) were reported previously for rhythmic (Lumaca et al., 2019) and melodic sequences (Quiroga-Martinez et al., 2019; Quiroga-Martinez et al., 2020) . Furthermore, Takegata et al. (2008) investigated MMN in response to duration deviants for noise and other auditory stimuli. They found smaller MMN amplitudes in the noise condition in comparison to other (harmonic) stimuli. Although consistent with findings of the present study, these results were interpreted in terms of lower ecological validity of noise. The results of the present study suggest an effect of precision-weighting on prediction errors on a very short timescale during the unfolding of auditory signals, i.e. related to the instantaneous processing of the spectral content of sound.

Table 1 .

 1 Participant demographics (mean ± SD, t statistics, degrees of freedom and p -values). MBEA: Montreal Battery of Evaluation of Amusia (maximum score= 30, average of the six sub-tests of the battery); MBEA pitch: average of the three sub-tests of the battery assessing pitch (maximum score= 30); PDT: Pitch Discrimination Threshold. For music training, Mann-Whitney U test results are reported.

		Amusics	Controls	t	df	p
	Sample size	17	17	-	-	-
	Female	8	9	-	-	-
	Right-handed	13	14	-	-	-
	Education (years)	15.06 (± 2.7)	15.12 (± 2.34) -0.07	31.4	.946
	Music training (years)	0	0.24 (± 0.66)	127.5 (U)	-	.163
	Age (years)	38.43 (± 15.96)	37.61 (±	0.15	31.9	.886
			17.02)
	MBEA	21.79 (± 1.81)	27.09 (± 1.04) -10.49	25.6	<.001
	MBEA pitch	20.79 (± 2.15)	27.43 (± 1.09) -11.36	23.7	<.001
	PDT (semitones)	1.57 (± 1.53)	0.31 (± 0.17)	3.375	16.4	.004

Table 2 .

 2 Pairwise contrasts of MMN mean amplitudes between conditions for each feature separately, as well as pairwise comparisons between features for differences between conditions (contrasts of contrasts). Data is pooled from both groups. Significant contrasts are highlighted in bold and marked with an asterisk. Standard effect sizes (d) are calculated as the difference between conditions divided by the square root of the sum of the residual and the random effects variance. The condition by feature interaction was not further modulated by group, p = .465.

	Feature	Contrast	Difference (µV) CI 2.5% CI 97.5%	t	d	p
	Intensity	hi-hat -piano	3.10	2.53	3.66	10.82	1.92	<0.001*
	Location	hi-hat -piano	2.54	1.97	3.10	8.87	1.57	<0.001*
	Timbre	hi-hat -piano	1.09	0.52	1.65	3.81	0.67	<0.001*
	(hi-hat -piano intensity) -
	(hi-hat -piano location)	0.56	-0.42	1.54	1.39	0.35	0.504
	(hi-hat -piano intensity) -
	(hi-hat -piano timbre)	2.01	1.03	2.99	4.96	1.24	<0.001*
	(hi-hat -piano location) -
	(hi-hat -piano timbre)	1.45	0.47	2.43	3.58	0.9	0.001*

Table 3 .

 3 Pairwise contrasts of MMN peak latencies between conditions for each feature separately, as well as pairwise comparisons between features for differences between conditions (contrasts of contrasts). Data is pooled from both groups. Significant contrasts are highlighted in bold and marked with an asterisk. Standard effect sizes (d) are calculated as the difference between conditions divided by the square root of the sum of the residual and the random effects variance.

	Feature	Contrast	Difference (ms)	CI 2.5%	CI 97.5%	t	d	p
	Intensity	hi-hat -piano	23.62	11.49	35.75	3.85	0.89 <0.001*
	Location	hi-hat -piano	3.50	-8.63	15.63	0.57	0.13	0.570
	Timbre	hi-hat -piano	-19.68	-31.81	-7.55	-3.20	-0.74	0.002*
	(hi-hat -piano intensity) -
	(hi-hat -piano location)	20.12	-0.9	41.13	2.32	0.76	0.065
	(hi-hat -piano intensity) -
	(hi-hat -piano timbre)	43.29	22.28	64.31	4.98	1.63 <0.001*
	(hi-hat -piano location) -
	(hi-hat -piano timbre)	23.18	2.16	44.19	2.67	0.87	0.025*

Table 4 .

 4 Pairwise contrasts of P3a mean amplitudes between conditions for each feature separately, as well as pairwise comparisons between features for differences between conditions (contrasts of contrasts). Data is pooled from both groups. Significant contrasts are highlighted in bold and marked with an asterisk. Standard effect sizes (d) are calculated as the difference between conditions divided by the square root of the sum of the residual and the random effects variance.

	Feature	Contrast	Difference (µV) CI 2.5%	CI 97.5%	t	d	p
	Intensity	hi-hat -piano	-0.60	-1.37	0.16	-1.55	-0.28	0.123
	Location	hi-hat -piano	-1.69	-2.46	-0.93	-4.36	-0.78 <0.001*
	Timbre	hi-hat -piano	2.94	2.18	3.71	7.58	1.35 <0.001*
	(hi-hat -piano intensity) -(hi-hat -
	piano location)		1.09	-0.24	2.42	1.99	0.5	0.146
	(hi-hat -piano intensity) -(hi-hat -
	piano timbre)		-3.55	-4.88	-2.22	-6.46	-1.63 <0.001*
	(hi-hat -piano location) -(hi-hat -
	piano timbre)		-4.64	-5.97	-3.31	-8.44	-2.13 <0.001*

  . Our results suggest that individuals with CA do not differ from matched controls in terms of early neural processing of auditory features other than pitch (see Quiroga-Martinez et al., 2021, for converging evidence). Event-related potential studies with pitch deviants in classical oddball paradigms also report intact MMN responses in CA listeners, indicating normal pitch processing at an early level of auditory processing

Funding information

This work was conducted in the framework of the LabEx CeLyA (''Centre Lyonnais d'Acoustique", ANR-10-LABX-0060) and of the LabEx Cortex (''Construction, Cognitive Function and Rehabilitation of the Cortex", ANR-11-LABX-0042) of Université de Lyon, within the program ''Investissements d'avenir" (ANR-16-IDEX-0005) operated by the French National Research Agency (ANR). DQ, EB, and PV are supported by the Danish National Research Foundation (DNRF 117).

Abbreviations

Open practices

The code and materials employed to conduct the experiment and analyses presented here can be found at: http://doi.org/10.17605/OSF.IO/JE4XZ. Due to data protection regulations, data cannot be publicly shared, but can be made privately available upon reasonable request.

Appendices

Appendix 1. ANOVA table for MMN amplitude analysis: main effects, two-way and three-way interaction effects on group, condition, and feature. Significant effects are marked with an asterisk.

Effect


Dr Kalliopi Ladomenou
email: kladomenou@uoc.gr

Dr Michael Papadakis

Prof Georgios A G Landrou

Dr Michel Giorgi

Dr Charalambos Drivas

Stella Kennou

Dr Renaud Hardré

Dr Julien Massin

Athanassios G Coutsolelos

Maylis Orio
email: maylis.orio@univ-amu.fr

Coutsolelos

Prof. S Kennou

Nickel Complexes and Carbon Dots for Efficient Light-Driven Hydrogen Production

Keywords: carbon dots, nickel catalyst, hydrogen production, photocatalysis, solar energy

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Clean and renewable energy production is part of many recent studies all over the world. It is vital to develop new technologies since the combustion of fossil fuels is strongly related to global warming and climate change due to the production of greenhouse gases such as CO2. A reaction able to provide a solution to the above issue is the photochemical water splitting with the production of H2. [1][2] Hydrogen is considered an ideal solar gas since it can easily produce a large amount of energy and, upon its combustion, it does not produce any harmful products. [3] The photochemical hydrogen production process mimics natural photosynthesis and the key challenge of this process is the efficient development of catalysts able to produce hydrogen from protons. [4][5] Also, this approach requires materials able to absorb light and carry out charge separation resulting in the presence of electrons on one side and holes on the other site. These chargeseparated states must be stable enough to drive the chemical reaction towards effective hydrogen production. [6][7] Promising light-harvesting materials are carbon dots as they feature efficient electron transfer properties and photostability. They are noble metal-free and are synthesized with low-cost preparation techniques on a large scale. [8][9][10][11] These types of light harvesters have already been used for hydrogen production by our group and others, and they produce promising results, despite the limited number of studies that have been performed. [12][13] On the other hand, it is important to use appropriate catalysts that can efficiently produce hydrogen. In the last decade, a plethora of firstrow transition metal complexes based on Co, Ni, Cu, Fe, and Mo have been developed towards H2 evolution. [14][15][16][17][18][19] Many photocatalytic systems that use molecular catalysts coupled with a wide range of different photosensitizers have been reported in the literature. They can catalyze the visible light-driven hydrogen evolution and produce an efficient amount of hydrogen but they often lack prolonged stability upon their irradiation. [20][21][22][23][24][25] Among these catalysts, we can distinguish Ni-based complexes with thiosemicarbazone ligands. [26][27][28][29][30] These type of ligands have already been studied and proved to be redox-active. [31][32][33][34][35][36][37][38][39] Furthermore, the presence of S-and N-atoms in the ligand permits the protonation of the ligand and can serve as proton relays. [40][41][42] In the literature there are two examples that uses a nickel(II) thiosemicarbazone complex as a catalyst able to produce H2 upon photo irradiation. In the first example the catalyst is placed on a Si surface. [29] In the second example the same catalyst was used with CdS nanorods as a photosensitizer. [30] The system after irradiation in the visible light was able to produce H2 using ascorbic acids as sacrificial electron donor (SED). The authors mentioned that the photosensitizer and the catalyst were stable upon light irradiation, but the ascorbic acid decomposed. Therefore, the combination of these type of nickel complexes with other materials such as carbon dots as light harvesters produce a very promising system for effective hydrogen evolution. Moreover, it is not yet clear what is the effect of the chemical nature of the ligand on the activity of thiosemicarbazone-based catalysts. Thus, we decided to explore the influence of the presence of an electron-donating or an electron-withdrawing group located at the para position of the phenyl ring. In a recent study, no correlation between the presence of electron-donating groups of the same ligand and the resulting electrocatalytic H2 production could be extrapolated. [28] More specifically, the most electron-donating group N(CH3)2 exhibited a turnover number (TON) of 10, whereas the least electron-donating group CN mediated a TON of 8. Since, as far as we know, there are no other examples in the literature that examined the structure-activity relationship in such nickelbased catalysts, it is important to study such structural effect of the catalysts in the context of hydrogen photo-production. In this regard, the only catalysts that have been studied are polypyridine cobalt(II) complexes in tetradentate, pentadentate, and hexadentate ligands, bearing electron-donating and electronwithdrawing groups in different positions. [43][44][45][46][47] The results of these studies indicated that it remains unclear how the modification of the ligand might affect the activity of the catalysts. Herein, we present the study of a photocatalytic hydrogen evolution system consisting of two types of carbon dot materials as light harvesters in combination with six different thiosemicarbazone nickel complexes as molecular catalysts. More specifically, the light-harvesting materials are carbon dots (Cdot) and nitrogen-doped carbon dots (NCdot) (Figure 1). The molecular hydrogen evolution catalysts are nickel(II) based complexes with thiosemicarbazone ligands bearing different types of substituents, namely NiN(CH3)2, NiN(CH3)2CN, NiSCH3, NiOCH3, NiCN, NiPh (Figure 1), whose synthesis was described in our previous work. [27][28] This set of complexes also includes a new catalyst in our series, NiPh (see Supporting Information for details). The hydrogen production is studied in aqueous solution with the use of a sacrificial electron donor (SED), tris(2carboxyethyl)phosphine/ascorbic acid (TCEP/Asc) 0.1 M each in a (1:1) ratio at pH=5, under white led irradiation. This synthetic approach benefits from easy preparation and modification of the catalysts by adding different electron-donating groups at the para position of the phenyl ring of the thiosemicarbazone ligands. In addition, the use of carbon dots exhibits several advantages such as ease and low-cost preparation of the light-harvesting material, making the whole system very promising for hydrogen production. In this report, the photocatalytic hydrogen production experiments were optimized. Fluorescence spectroscopy studies were performed to elucidate the reaction mechanism of our system in the presence of carbon dot at different concentration of nickel catalysts.

Results and Discussion

Synthesis and characterization of carbon dots. The preparation of carbon dot materials was done according to our previous published work. [12] To extend the characterization of these photosensitizers, we performed 1 H-NMR and X-ray photoelectron spectroscopy (XPS) analysis. As shown in Figure S8, the 1 H NMR signals of the starting materials of carbon dots, namely citric acid and ethylenediamine, are not present in the NCdot spectrum, whereas proton signals are present in the range from 6.2 to 1.0 ppm. XPS analysis was done to identify the elemental composition and the type of chemical bonds that are present in carbon dot materials (Figure 1). The samples, named Cdot and NCdot, were pressed as received onto thick Pb sheets to be introduced into the ultra-high vacuum chamber. XPS measurements were carried out using unmonochromatized Al Ka line at 1486.6 eV (12 kV with 20 mA anode current) and a Leybold EA-11 analyzer with a constant pass energy of 100 eV. The analyzed area was approximately a 2x5 mm 2 rectangle, positioned near the geometric center of each sample. XPS analysis was carried out at a 0 degrees take-off angle (normal to the sample area). In all XPS spectra, the Binding Energy (BE) of the C-C bond of the C 1s peak at 284.8 eV was used as a measured BE reference. From the wide scan survey spectra (not shown), the photoelectron peaks of C and O were detected for all the samples, as well as N for the NCdot sample and Na for the Cdot one. A small amount of Pb from the Pb sheet was also present. Detailed scans of the most prominent photoelectron peak of each element present, as well as the Na Auger region, were taken to have an insight into their chemical state. Quantitative analysis was done after the area of the peaks was measured upon utilizing a Shirley background, as summarized in Table S4. In Figure 2, the C 1s region is shown for the two samples. For both, spectra deconvolution was done to discern the different chemical species. The fitting was done constraining the position of the peaks to be ±0.2 eV of reported values and the full width at half maximum (FWHM) to be equal among them. Also, the areas of the C-N and COONa peaks were correlated with the area of the N 1s and Na 1s spectra respectively. For both spectra, the most prominent peak at 284.8 binding energy (BE) is attributed to C-C bonds from the carbon dots. At higher BEs, there are the different C bound to O species, namely the C-O group at 286.4±0.2 eV, the C=O one at 287.9±0.2 eV, and the COOH one at 289.2±0.2 eV. [START_REF] Briggs | Surface Analysis of Polymers by XPS and Static SIMS[END_REF] These functional groups originate from the thermal decomposition of the precursors and possible oxidation from the ambient environment when handled. Additionally, for the Cdot sample, another peak was used for the COONa group at 288.0±0.2 eV, [START_REF] Kishi | [END_REF] and for the NCdot sample, the peak at 285.7±0.2 eV corresponds to C-N species. [START_REF] Briggs | Surface Analysis of Polymers by XPS and Static SIMS[END_REF] Similar to the C 1s region, deconvolution for the O 1s region (Figure 2b) was done using the same constraints. The oxygen species used for the deconvolution were C=O (531.3±0.2 eV), C-O/-OH (533.1±0.2 eV), [START_REF] Briggs | Surface Analysis of Polymers by XPS and Static SIMS[END_REF] and COONa (531.2±0.2 eV). [50] At 535.7±0.1 eV, there is also the Na KL1L23 Auger photoelectron peak. At both C 1s and O 1s deconvolutions, another peak at low BE was used, named contamination, which is from the Pb sheet and was not taken into account. Their positions and relative intensities are summarized in Table S5. Figure S9 shows the N 1s region for the NCdot sample. A single peak can be seen at 400.3±0.1 eV and is attributed to C-N-H bonds. [51] The XPS spectra of the Na 1s and Na KL23L23 regions are shown in Figure S10. A single peak can be seen for the Na 1s and is located at 1071.5±0.1 eV. For the accurate identification of the Na functional group, the modified Auger parameter was estimated as the sum of the binding energy of the photoelectron peak and the kinetic energy of the Auger peak, which is found to be 2061.3±0.2 eV, is attributed to COONa. [START_REF] Kishi | [END_REF] Photocatalytic H2 production. In our recent work, we described the synthesis of carbon dots (Cdot) and nitrogen-doped carbon dots (NCdot) as well as their physical and structural characterization. [12] In addition, we studied the ability of these materials to evolve H2 production quite efficiently with the use of molecular cobalt catalysts. Therefore, we wanted to extend our previous effort by using the carbon dot materials with different transition metal-based catalysts. In this study, we thus performed hydrogen evolution experiments using these two types of carbon dots in combination with a series of Ni thiosemicarbazone catalysts which display different groups at the para position of the phenyl ring of the ligand. Our initial photocatalytic hydrogen evolution efforts proved unproductive, thus are presented in detail at the supporting information section. The most successful photocatalytic system was the one that used NCdot as photosensitizer, a mixture of 0.1 M each, TCEP/Asc (1:1) as SED at pH=5 and a white led as a light source. When Cdot was used as photosensitizer, no hydrogen could be detected, possibly due to the better charge transfer of NCdot, since the presence of nitrogen produce a material with n-domains as already have been mentioned by us and others. [9,12,52] The presence of C-N species in nitrogen doped carbon dots are shown with XPS analysis of the material. In order to optimize the best catalyst's concentration various quantities 20, 50, and 100 nmol were used keeping constant the amount of photosensitizer (10 mg) as shown in Figure 3. The maximum performance of 148 TON was reached when 50 nmol of the catalyst NiN(CH3)2 was used. Then, we performed the catalytic experiments with all six Ni catalysts and the results are presented in Figure 4. Most of the photocatalytic reactions reached a plateau after about 30 h of white led irradiation. Interestingly, a similar conclusion was reached for a series of cobalt polypyridine complexes recently reported in the literature. In this study, the authors concluded that the most dominating factor for effective photocatalysis was the position of the substituent and not its chemical nature. [44] Additionally, control experiments were done in the absence of the NiN(CH3)2 catalyst, in the absence of the NCdot photosensitizer, in the absence of TCEP/Asc, and in the dark. In any case, no H2 was produced which indicates that all components are essential for H2 production in our system. To exclude the presence of Ni nanoparticles due to the degradation of the catalyst, mercury poisoning experiments were performed. Photocatalytic experiments in the presence of mercury showed no change in H2 production (TONCAT = 135), thus confirming the stability of the catalyst upon photocatalytic reaction. Moreover, when 50 nmol of NiCl2 was used as an alternative to the Ni molecular catalysts, no H2 was evolved, supporting the essential role of Ni complexes as catalysts. Regeneration experiments were done upon photobleaching of the photosensitizer, 10 mg of carbon dots were added and the system started producing H2. In these photocatalytic systems, H2 production was detected after about 13 h of irradiation for all catalysts. This was also the case in our previous work, where under similar conditions H2 production was observed after 10 to 15 hours. [12] The only difference was the light source and the molecular catalyst. Since, in this report the delay in H2 detection was similar for all nickel catalysts, we thought to investigate the photosensitizer. For that reason, we irradiated 10 mg of NCdots in the presence of SED for 13 h and we measured the quantum yield (QY) which was 16 % compared to the initial material with QY=26 %. The XRD pattern of the material before and after light irradiation showed no difference (Figure S12). In addition, we collected TEM images before and after irradiation (Figure 6). The images show that, after light irradiation, the material retains its spherical morphology but the diameter of some particles is much bigger (Figure 6 c andd) compared to the carbon dots before irradiation (Figure 6 a andb). Consequently, upon light irradiation, the morphology of the material changes, and possibly must obtain a specific form in order to start producing H2. In order to prove that the transformation of the photosensitizer is done exclusively upon light irradiation we stirred all components of our best photocatalytic system in the dark overnight. Then, we started the irradiation of the system with a white led. Once again, the system started producing H2 after about 10 h of irradiation and reached 132 TONCAT after 30 h, which is similar to our best system (148 TONCAT).Therefore, light irradiation is responsible for the transformation of the photosensitizer and this alteration is important for the carbon dots to absorb light and finally produce H2. In order to study the performance of our system concerning the delay of hydrogen production, we first irradiated the photosensitizer with the SED for 13 h. Then we added NiN(CH3)2 catalyst and the mixture was purged with nitrogen for 15 min in order to remove oxygen, Next, after 5 h of irradiation the system started producing H2 and a reached a plateau (TONCAT = 135) after 20 h (Figure S13). Therefore, our photocatalytic system work faster if we pre-irradiate just the photosensitizer and then add the catalyst.

Photocatalytic mechanism. The thermodynamic ability for photochemical hydrogen evolution was evaluated by measuring the redox potentials of all nickel catalysts and the NCdot photosensitizer (Table 1). The valence band (VB) and the conduction band (CB) of nitrogen-doped carbon dots were calculated in our previous work. [12] The redox potentials of nickel catalysts were recorded in anhydrous DMF as listed in our recent report. [28] Based on all these values, the ΔG (PS/Cat) were calculated for all the catalysts and the resulting negative potentials nicely explain the photocatalytic activity of all nickel catalysts From the measured data, it is obvious that hydrogen production is thermodynamically allowed for all the catalysts used in these systems. Furthermore, photoluminescence experiments were performed (Figures S14-S19) to clarify the mechanism of the photocatalytic reaction. The emission experiments were performed with a constant amount of NCdots in the presence of various concentrations of nickel catalysts and using water as solvent.

Table 1. Redox potentials (eV vs. NHE) of the photosensitizer and catalysts with the thermodynamic driving forces of electron transfer processes ΔG1 (PS/Cat) and ΔG2 (PS/Cat) (eV).

Compounds EVB Each catalyst was dissolved in a concentrated solution of DMSO, from which various amounts of catalysts were added including the concentration used for the photocatalytic experiments. Then, the emission spectra were monitored upon excitation at 340 nm and in all cases, the emission intensity was significantly decreased up to more than 90 %. Only in the case of NiOCH3 and NiN(CH3)2CN catalysts, the quenching was about 75 % and 85 %, respectively. Next, the Stern-Volmer and quenching constants were calculated according to the literature (Table 2), the fitting curves were generated (Figures S20-S25) and the values of the ascorbic acid were obtained from our previous work. [12] . For all catalysts, the Ksv is much bigger compared to ascorbic acid (Ksv = 13.8) and the quenching constants of all catalysts (KQ = 1.1 x 10 12 -9.0 x 10 12 ) are greater compared to the SED's one (KQ = 1.3 x 10 9 ). Under our photocatalytic experimental conditions using 0.1 M of SED and 16 μΜ of each catalyst, the quenching is more pronounced in the case of ascorbic acid. Consequently, the photoinduced electron is transferred from the ascorbic acid to the CB of carbon dots (reductive quenching), since the concentration of the SED is much greater compared to the catalyst's one. A summary of all the above experimental studies leads us to propose a possible mechanism of H2 evolution as shown in Scheme 1.

Scheme 1. Photocatalytic mechanism for H2 evolution using nitrogen-doped carbon dots as photosensitizer, TCEP/Asc as sacrificial electron donor and Ni complexes as catalysts.

Upon photoexcitation of carbon dots by visible light irradiation, the electrons are transferred to the CB. The hole that is formed in the VB of the photosensitizer is filled by an electron transferred from the SED via a reductive quenching process. The photogenerated electrons are then transferred to nickel catalysts, which are subjected to subsequent protonation and reduction steps following a ligand-assisted metal-centered pathway and leading to the formation and release of H2. [53]

Conclusion

The present study reports the performances of new photocatalytic hydrogen evolution systems composed of carbon dot materials and a series of noble metal-free complexes. While nitrogen-doped carbon dots are employed as light harvesting materials, nickel complexes with redox active ligands play the role of catalytic centers. The latter correspond to a series of thiosemicarbazone complexes for which the phenyl ring substituents were chosen to cover a wide range of electron-donating properties. This synthetic design was meant to modulate the electronic properties of the catalysts based on the different Hammett Sigma constants at the para position of the benzene groups. We evaluated the photocatalytic capability of the resulting systems to promote hydrogen evolution in aqueous solution, in the presence of a sacrificial electron-donor and under light irradiation. The conditions of the photoreaction were optimized to determine the parameters affecting the hydrogen evolution experiments (i.e. irradiation type, SED nature, catalyst concentration and nature, and ratio). This allowed us to probe the most efficient photocatalytic system, which was found to be the NCdots associated with the NiN(CH3)2 catalyst with a calculated TON of 148 when using a TCEP/Asc (1:1) mixture at pH=5 and under white led radiation for 30 h. Our results indicate that the chemical nature of the substituent in the para position of the ligand indeed influences the photocatalytic behaviour of the systems and their hydrogen evolution performances. However, consistently with our previous study on the electrocatalytic performances of these complexes, it remains difficult to find a clear rational between the catalysis parameters, i.e. the TON values, and the electrondonating ability of the substituents (N(CH3)2 > NCH3CN > OCH3 > SCH3 > Ph > CN). Recent reports on cobalt complexes suggest that the position of the substituent rather than their chemical nature might be a key element to better control and understand the photocatalytic reaction. Hence, this encourages us to pursue the current work in our group on chemically-modified thiosemicarbazone nickel complexes to decipher the key elements that control their catalytic capabilities and improve their performances for both electro-and photoproduction of hydrogen.

Experimental Section

General. All reagents and solvents were purchased from usual commercial sources and used without further purification, unless otherwise stated. The synthesis of NCdot were reported in detail in our previous work. [12] The nickel thiosemicarbazone complexes were prepared according to procedures previously described and their characterizations were also reported. [27][28] The alternative synthesis and full characterization of the complex NiPh are described in the Supplementary Information (Figures S1-S7 and Tables S1-S3). CCDC numbers 208130, 2081301 and 2081302 c contains the supplementary crystallographic data for this paper. 1 H NMR spectra of the compounds were recorded on Bruker AMX-500

MHz and Bruker DPX-300 MHz spectrometers. The solution of the sample was in deuterated solvent by using the solvent peak as the internal standard. X-Ray powder diffraction (XRD) Bruker Model D8 equipped with twin-twin technology, Geometry 2theta/theta. A transmission electron microscopy (TEM) system (Model JEM-2100F electron microscope (JEOL, Japan)) was used for morphology characterization of C-Dots. A dilute carbon dot stock solution was deposited onto the grid for subsequent HRTEM imaging, using an 80 kV accelerating voltage. High-resolution mass spectra were recorded on a Bruker ultrafleXtreme MALDI-TOF/TOF spectrometer.

Photophysical Measurements. The UV-Vis absorption spectra of all compounds in solution were obtained using a Shimadzu UV-1700 spectrophotometer in quartz cuvettes of 1 cm path-length. The emission spectra of all derivatives in solution were measured on a JASCO FP-6500 fluorescence spectrophotometer equipped with a red-sensitive WRE-343 photomultiplier tube (wavelength range: 200-850 nm).

Photocatalytic H2 Evolution Experiments. The photocatalytic reactions were performed in 3 ml aqueous solution of TCEP/Asc (1:1) 0.1 M each at pH 5.0. The TCEP and Asc are both serving as reversible sacrificial electron donors. In all experiments 10 mg of the photosensitizer was used. The appropriate amount of catalyst was added in the reaction vessel from a stock solution of each Ni catalyst dissolved in the aprotic polar solvent DMSO. A 10 ml flask was used with a rubber septum where the mixture was purged with nitrogen for 15 min in order to remove oxygen. The reaction mixture was continuously stirred with irradiated with a 100 W white led emitting lamp. At certain time intervals 100 μl was removed from the headspace of the flask and was analyzed by Shimadzu GC 2010 plus chromatograph with a TCD detector and a molecular sieve 5 Å column (30 m -0.53 mm) in order to measure the amount of H2 that was produced. The H2 amount produced was quantified using a calibration curve and in all cases, the reported H2 production and the Turn Over Number (TON) is the average of three independent experiments.

Table of Contents

A noble metal-free photocatalytic system was designed by combining nitrogen-doped carbon dots as photosensitizers and a dimethylaminothiosemicarbazone nickel catalyst. This promising system is shown to photoinduce hydrogen production (TONCAT = 148) upon irradiation in aqueous medium.

Institute and/or researcher Twitter usernames: Maylis Orio@orio52503713

Figure 1 .

 1 Figure 1. Chemical structures of a) carbon dots, b) Ni catalysts and c) SED.

Figure 2 .

 2 Figure 2. XPS spectra of the a) C 1s and b) O 1s regions together with their deconvolution for all the samples. Dashed lines represent the unique species of each sample.

Figure 3 .

 3 Figure 3. Photocatalytic hydrogen production plots of 10 mg NCdots in three different amounts of the catalyst NiN(CH3)2: 20, 50 and 100 nmol. The photocatalytic experiments were conducted in aqueous TCEP/Asc (1:1) 0.1 M each at pH = 5. All the results presented in the hydrogen production plots are the average values of three independent measurements (within 5-10% error).

Figure 4 .

 4 Figure 4. Photocatalytic hydrogen production plots of 10 mg NCdot with 50 nmol nickel catalysts: NiN(CH3)2, NiN(CH3)2CN, NiSCH3, NiOCH3, NiCN, NiPh. All photocatalytic experiments were performed in aqueous TCEP/Asc (1:1) 0.1 M each at pH = 5. All the results presented in the hydrogen production plots are the average values of three independent measurements (within 5-10% error).

Figure 5 .

 5 Figure 5. Photocatalytic hydrogen production (μmol H2) upon irradiation of 10 mg NCdots in the presence of 50 nmol of various nickel catalysts in aqueous TCEP/Asc (1:1) 0.1 M each at pH = 5. All the results presented in the hydrogen production plots are the average values of three independent measurements (within 5-10% error).

Figure 6 .

 6 Figure 6. TEM images of NCdots before light irradiation (a and b) and after 12 h of white led irradiation (c and d).

  Oxidation potentials were measured by cyclic voltammetry and were referenced to NHE by addition of +0.193V. [b] Values were calculated as the difference of the oxidation potentials and the optical band gap energy E00, calculated in eV. [c] E00 values were calculated form the intersection between the normalized absorption and emission spectra. [d] Reduction potentials were measured by cyclic voltammetry and were referenced to NHE.

Table 2 .

 2 Stern-Volmer constant Ksv and quenching constant KQ of all nickel catalysts.

		Ksv (M	-1 )	KQ=Ksv/τ (M	-1 s	-1 )
	NiN(CH3)2	43574	4.1 x 10	12
	NiN(CH3)2CN	21128	2.0 x 10	12
	NiSCH3	94414	9.0 x 10	12
	NiOCH3	11726	1.1 x 10	12
	NiCN	52555	5.0 x 10	12
	NiPh	63313	6.0 x 10	12
	Asc	13.8	1.3 x 10	12

Acknowledgements

This research was funded by the General Secretariat for Research and Technology (GSRT) and Hellenic Foundation for Research and Innovation (HFRI; project code: 508). This research has also been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1EDK-01504). In addition, this research has been co-financed by the European Union and Greek national funds through the Regional Operational Program "Crete 2014-2020," project code OPS:5029187. Moreover, the European Commission's Seventh Framework Program (FP7/2007-2013) under grant agreement no. 229927 (FP7-REGPOT-2008-1, Project BIO-SOLENUTI) and the Special Research Account of the University of Crete are gratefully acknowledged for the financial support of this research. The authors gratefully acknowledge financial support of this work by the French National Research Agency (ANR-19-CE05_0030_01).