Wenhao Zhang

David Berthebaud

Jean-François Halet
email: jean-francois.halet@univ-rennes1.fr

Takao Mori
email: mori.takao@nims.go.jp

Electronic Configurations of 3d Transition-Metal Compounds Using Local Structure and Neural Networks

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INTRODUCTION

Although the physical phenomena for various properties of transition-metal compounds are well understood, it is still difficult today to suggest chemical compositions and crystal structures of transition-metal compounds with desired properties. [START_REF] Tokura | Critical features of colossal magnetoresistive manganites[END_REF][START_REF] Graf | Simple rules for the understanding of Heusler compounds[END_REF][START_REF] Hosono | Exploration of new superconductors and functional materials, and fabrication of superconducting tapes and wires of iron pnictides[END_REF][START_REF] Grin | Inhomogeneity and anisotropy of chemical bonding and thermoelectric properties of materials[END_REF] Given a structure, these properties are available recently thanks to advances in materials simulation via first-principles methods and various analysis tools. [START_REF] Kohn | Lecture: Electronic structure of matter-wave functions and density functionals[END_REF][START_REF] Jones | Density functional theory: Its origins, rise to prominence, and future[END_REF][START_REF] Togo | First principles phonon calculations in materials science[END_REF][START_REF] Madsen | A code for calculating band-structure dependent quantities[END_REF][START_REF] Nelson | STER: Local orbital projections, atomic charges, and chemical-bonding analysis from projector-augmented-wave-based density-functional theory[END_REF] Unfortunately, this often incurs a high computational cost that limits effectiveness in exploring large search spaces and complex compounds with large unit cells. Nevertheless, it is clear that, due to the relatively localized behavior of the d orbitals, chemical interactions between 3d transition-metal elements and their neighboring atoms in these compounds are relatively easy to understand and play a central role in determining their electronic properties, such as band dispersion and density of states (DOS). [START_REF] Burdett | Chemical bonding in solids[END_REF][START_REF] Albright | Orbital interactions in chemistry[END_REF][START_REF] Housecroft | Inorganic chemistry[END_REF] On the other hand, machine learning (ML) statistical methods have been demonstrated as a powerful alternative to DFT calculations, offering the possibility to speed up materials' discovery by learning patterns within large data sets and proposing novel candidates that are statistically likely to have the desired properties. [START_REF] Gaultois | Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties[END_REF][START_REF] Schmidt | Recent advances and applications of machine learning in solid-state materials science[END_REF][START_REF] Chen | A Critical Review of Machine Learning of Energy Materials[END_REF] Indeed, ML models can be highly efficient, portable and able to make prediction for materials properties with good accuracy, [START_REF] Faber | Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error[END_REF] assuming that a standard of "best practices" is followed. [START_REF] Artrith | Best practices in machine learning for chemistry[END_REF] Chemical environments in transition-metal compounds offer a good playground where ML structure-property relationships can be examined with our chemical intuition and interpretation. [START_REF] Burdett | Chemical bonding in solids[END_REF][START_REF] Albright | Orbital interactions in chemistry[END_REF][START_REF] Hoffmann | How Chemistry and Physics Meet in the Solid State[END_REF] Crystal-and-ligand field theory considering only local environments enabled simple theoretical analysis of the band dispersion and DOS in transition-metal compounds long before that first-principles calculations became a common practice. [START_REF] Burdett | Chemical bonding in solids[END_REF][START_REF] Albright | Orbital interactions in chemistry[END_REF][START_REF] Hoffmann | How Chemistry and Physics Meet in the Solid State[END_REF] Therefore, the correspondingly formulated machine learning problem and its results should be relatively simple and easy to analysis with our chemical understanding.

Good ML prediction of materials properties strongly relies on both the learning algorithms and on the representation of the input data. [START_REF] Parsaeifard | An assessment of the structural resolution of various fingerprints commonly used in machine learning[END_REF] For the latter, two main approaches can be used. One featurizes compounds by their composition and the individual properties of the contained elements. The other featurizes compounds using only their structural arrangements. It is naturally desirable to use the latter approach to study the structure-property relationships directly, but encoding materials' atomic neighborhood environments efficiently is not a straightforward task. Nevertheless, some progress has been made in this direction over the last years, and we summarize here a few of the most important works published in this area. Bartok et al. pioneered the local description of chemical systems with different methods to represent atomic neighborhood environments. [START_REF] Bartók | Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons[END_REF][START_REF] Bartók | On representing chemical environments[END_REF] ML frameworks using these representations were recently developed to estimate electronic [START_REF] Chandrasekaran | Solving the electronic structure problem with machine learning[END_REF][START_REF] Ben Mahmoud | Learning the electronic density of states in condensed matter[END_REF] and phononic 24 density of states in condensed matter. Faber et al. compared various structure representations and ML models for getting properties of molecular systems. [START_REF] Faber | Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error[END_REF] Schütt et al. tried to predict the electronic DOS at the Fermi level in crystalline materials using a simple radial distribution function and regression methods. [START_REF] Schütt | How to represent crystal structures for machine learning: Towards fast prediction of electronic properties[END_REF] Later, the same authors developed a deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of organic molecules using purpose-designed deep neural networks that consider atoms and bonds as nodes and edges in a graph. [START_REF] Schütt | Quantumchemical insights from deep tensor neural networks[END_REF][START_REF] Schütt | SchNet-A deep learning architecture for molecules and materials[END_REF] Somewhat similarly, Xie and Grossman proposed a crystal graph convolutional neural network to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Their approach was successfully applied to the design of novel perovskites. [START_REF] Xie | Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties[END_REF] Equivariant tensor field neural networks were also proposed that further preserve rotational properties using symmetry group representations. [START_REF] Thomas | Tensor field networks: Rotation-and translation-equivariant neural networks for 3D point clouds[END_REF] Although the recent graph-based networks [START_REF] Schütt | Quantumchemical insights from deep tensor neural networks[END_REF][START_REF] Schütt | SchNet-A deep learning architecture for molecules and materials[END_REF][START_REF] Xie | Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties[END_REF] are very powerful, they typically use atomic distances and structure connectivity as inputs, which does not describe well the local structure that is important for the transition-metal atoms. Following the insights from local crystal field and ligand field theory, we describe herein an ML model that is based on strictly local structure representations (fingerprints) developed by Uhrin [START_REF] Uhrin | Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments[END_REF] and trained to predict the electronic occupation of up and down spin channels of transition-metal 3d orbitals in solidstate compounds. We report the performance of the machine learning model. We also put emphasis on a detailed analysis of the ML learning errors in complex chemical environments.

THEORY

We first provide a brief overview of the quantum chemistry theory of electronic bands formed from transition-metal orbitals to show that our ML problem mapping the local structure to the electronic configurations of the transition metals is straightforward. [START_REF] Albright | Orbital interactions in chemistry[END_REF][START_REF] Hoffmann | How Chemistry and Physics Meet in the Solid State[END_REF] In simple coordination environments, the formation of the energy bands from atomic orbitals can be considered in two steps: firstly, degenerated atomic 3d orbitals are perturbed by the presence of neighboring anions. Different local symmetries lead to different energy level splittings for the 3d orbitals, as shown for three cases in Figure 1 (b). In the second step, periodicity of the crystal is introduced and Bloch functions, modulated by the reciprocal vector k can be formed. The band structure is therefore given by the dispersion E(k). The positions in energy of the 3d bands are determined by the energies of the MOs of the building motifs while the width of their dispersion is governed by the interaction between building motifs. If the interaction is small, then the bands formed from the 3d orbitals of the transition metals will be narrow or even localized. This is generally true if the metal-metal distance is relatively large, then, the DOS and occupation of the TM 3d orbitals are largely determined by its nearest neighbors (local environment), which consequently determine the electron occupations of the TM 3d orbitals.

The above qualitative analysis is limited in two ways: it is unrealistic to perform such a treatment for thousands of structures and, for more general cases, distortion from ideal symmetry or varying ligand field strength will be case-specific and will affect the energy of the MO levels quantitatively (see Figure 1 (b) for an elongated octahedron). ML can be expected to capture these quantitative effects and make accurate on-the-fly prediction for 3d orbital occupation on the metal centers. Since the theoretical background in our ML formulation is the analytic theory outlined above, we aim to achieve a similar prediction accuracy in 3d orbital occupations using only crystal structures as input.

Nearest-neighbor effects are the most important ones for the electronic configurations of the transition metal atoms. Although less important, atoms that are outside the nearestneighbor environment also affect the energies of the 3d orbital-derived bands. However, it should be noted that by taking more atoms into consideration (such as secondary neighbors), we sacrifice the simplicity of the structure-property relationship we want the ML model to comprehend. Therefore, it is important to study how many surrounding atoms, apart from the nearest neighbors, is necessary to take into account to give a good prediction of the electronic configurations of the transition-metal centers. We answer this question by comparing different choices as input for ML in this work.

Methods Dataset

The entire workflow of training and application of ML method is shown in Figure 2 (a).

The only input for this study is crystal structures, which were gathered from either Materials Project (MP) Database 31 or from the Crystallography Open Database (COD). [START_REF] Gražulis | Crystallography Open Database (COD): an open-access collection of crystal structures and platform for worldwide collaboration[END_REF] H, noble gases, and rare-earth elements were excluded when we searched for compounds in the databases. From all the available compounds containing transition-metal elements, we only selected those that contain 3d transition-metal element the nearest neighbors of which are main group elements in the p block of the Periodic Table. Such a consideration simplifies the transition-metal bonding that the ML needs to identify in this work by excluding strong metal-metal interactions, as discussed previously. Furthermore, B, C, N, O and F elements were also excluded in our study for the following two reasons: 1) the number of these compounds in the database dominates the dataset and could lead to biased training set, and 2) the correlation effects on transition metal are more important in the narrow d bands of nitrides or oxides and large errors are expected from a PBE treatment in DFT calculation. Finally, we restricted the number of atoms per unit cell to be less than 30 to reduce data acquisition time by DFT calculations.

DFT results were obtained for a total of ca. 1800 compounds that satisfied the above criteria. In terms of elemental distribution in this training dataset, the number of compounds containing Cu is slightly larger than the number of compounds containing other transitionmetal elements, which are distributed more or less evenly. For anions, S, Se and Te are the most frequent ones, followed by other anions such as Si, Ge, P, Sn and Cl. Overall, the dataset used does not bias heavily towards any particular metals or anions. A detailed description of how structures were gathered and their properties, including metal, anion distribution and their coordination, can be found in the Supplementary Information (SI).

DFT Calculations

As noted in previous studies, [START_REF] Hegde | Reproducibility in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD[END_REF][START_REF] Noh | A density functional theory investigation of the electronic structure and spin moments of magnetite[END_REF] different calculation schemes and functionals can lead to errors ranging from 1% to 10% in calculated properties such as band gap and total magnetization, which is on the same magnitude of ML prediction errors. [START_REF] Faber | Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error[END_REF] To have consistent results between model predictions and later DFT validation, we prepared training data from in-house spin polarized calculations, using the Quantum Espresso code [START_REF] Giannozzi | QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials[END_REF][START_REF] Giannozzi | Advanced capabilities for materials modelling with QUANTUM ESPRESSO[END_REF] with the GGA PBEsol functional [START_REF] Perdew | Restoring the Density-Gradient Expansion for Exchange in Solids and Surfaces[END_REF] from Pseudo-Dojo 39 with suggested energy cutoffs. Hybrid functionals usually give more accurate results for the electronic structure of transition metal compounds, but they are forbiddingly expensive to use. On the other hand, although meta-GGA functionals improves upon GGA functionals in calculating band gap values, [START_REF] Borlido | Exchangecorrelation functionals for band gaps of solids: benchmark, reparametrization and machine learning[END_REF] it is not clear whether they systematically outperform GGA functionals in predicting other properties, for example, magnetic moment. [START_REF] Isaacs | Performance of the strongly constrained and appropriately normed density functional for solid-state materials[END_REF] Structures for DFT calculations were used without relaxation.

Comparing a few relaxed vs. non-relaxed structures, we observed that the effects on orbital occupations are relatively minor (see SI). Other details of calculations are provided in the corresponding sections in SI. The orbital occupations of the transition-metal d states were calculated using Löwdin population analysis and projection for both spin channels. [START_REF] Ertural | Development of a robust tool to extract Mulliken and Löwdin charges from plane waves and its application to solid-state materials[END_REF] Finally, it should be mentioned that although all DFT calculations were performed within a ferromagnetic configuration, we do not expect the spin moments to change dramatically imposing an antiferromagnetic configuration, since we assume that magnetic structure is determined by spin cooperative interactions, and spin moments are governed by the local coordination. [START_REF] Horton | High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory[END_REF]

Structure Representation

Since we focus on transition metals in their local coordination, the structure representation should faithfully describe the position of the neighboring atoms.

In this work, a two-step procedure is used to generate the local structure representations, as shown in Figure 1 (c)-(f). First, we need to identify the local atoms surrounding the transition metal atom to be used as inputs for local structure representation. We tried three different strategies that we call "first-shell", "Voronoi-bonded" and "cut-off". The "firstshell" method considers only the nearest neighboring atoms in the covalent sphere while the "cut-off" method includes all atoms within a cut-off radius of 5 Å from the transition-metal atom, a distance beyond which chemical interactions between atoms should be negligible.

The "Voronoi-bonded" approach sits in between the above two methods since it includes all the nearest neighbors and also atoms that share common boundaries upon partitioning the inter-atomic space, measured in terms of solid angle. [START_REF] O'keeffe | A proposed rigorous definition of coordination number[END_REF][START_REF] Waroquiers | ChemEnv: a fast and robust coordination environment identification tool[END_REF] Next, to encode the structure arrangement and elemental information, we used the recently introduced structure representations by Uhrin, [START_REF] Uhrin | Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments[END_REF] which is a set of linear combinations of spectral coefficients that are rotational (rotation of the reference frame) and permutational We take BaCoS 2 compound 46 as an example for generating local structure fingerprints with one-hot encoding in Figure 1: We first identify the non-equivalent transition-metal sites in the structure (one in this case) and its neighbors. Every Co atom is surrounded by a square pyramid of S atoms which constitute its first neighbors, Ba atoms as second neighbors and other Co atoms further out. Each kind of element is considered as a set of points for which structure fingerprints can be obtained separately. These fingerprints are put in rows in an input feature matrix (65×117, 117 fingerprints for each of the 65 elements taken from the Periodic Table ). A row would be zeros if the element does not appear in the specific local structure. Although we did not include compounds containing B-F elements in the dataset, we included these elements in the one-hot encoding for uniformity.

The same procedures were carried out for all the non-equivalent transition-metal sites identified in the calculated structures in the preprocessing stage, which form the inputs for the ML model.

Model

We used a multilayer neural network [START_REF] Bishop | Pattern recognition and machine learning; Information science and statistics[END_REF][START_REF] Goodfellow | Deep learning; Adaptive computation and machine learning[END_REF] to learn the electronic occupations from the above obtained descriptors, since neural networks are easy to build, flexible with respect to inputs and are found to give good prediction accuracy in similar tasks. [START_REF] Faber | Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error[END_REF] The design of the network is shown in Figure 2 (b).

As described above, the input to the network are rotational permutational invariant feature matrices. The first two layers consist of self-interaction for fingerprints that aims to extract element-specific structure features from the inputs and to reduce the number of total connections in the following layers. Linear layers are fully connected and diverge to give prediction of the occupation of the spin-up and spin-down channels. We used squared loss as

RESULTS

Evaluation of the Structure Description

To compare the different strategies of representing local structures, mean absolute errors (MAE) were evaluated using a 10-fold cross validation [START_REF] Bishop | Pattern recognition and machine learning; Information science and statistics[END_REF] for the models trained with different inputs. Cross validation is necessary to achieve consistent results because our data set is small and the random partitioning of the test set makes it difficult to compare the predictions systematically. The average error and the standard deviations of the training and testing sets over the models trained with each partition are shown in Figure 2 (c) and (d).

A clear trend can be observed from the errors in Figure 2, namely, the more atoms that are considered in the local structure representation, the better the fitting performance (training error) but the more it suffers from the over-fitting problem. In the end, the best generalization results were achieved by the network trained with only the information on the nearest neighboring atoms ("first-shell"), making better predictions on the unseen samples and leading to the lowest MAE in testing at 0.07-electron occupation.

The following conclusion can be drawn from the above result. Firstly, it again emphasizes that chemical and structure configurations of the nearest neighbor atoms play the dominant role in determining the electronic state of the transition-metal atoms, confirming the theory we outlined earlier, since using the nearest neighbor approach alone achieves a decent prediction. Secondly, it is easier for our neural network to learn the effect of the nearest neighbors of the transition-metal atoms than that of the higher order interactions from the secondary neighbors (over-fitting implies low learning quality). There can be multiple contributing effects: the configuration space of the secondary neighbors is much larger and more complex than that of the nearest neighbors, since the secondary neighbors can consist of simple ionic s-metals, transition metals in the neighboring cell, or even covalently bonded main group atoms in different coordinations. On the contrary, the chemical and structure configurations of the nearest neighbors are much simpler. As a result, the effects of the secondary neighbors are much more difficult to learn with a limited data set. It is also possible that the simple structure of our neural network used here limits the learning performance with more input information such as related to secondary neighbors, but it is not clear how this can be improved. This will be studied in future works.

Analysis of Errors

We dedicate this part to analyze the different sources of errors in our predictions. The errors reported in Figure 3 are the errors of all test sets in the 10-fold cross validation, each predicted by the ML trained with that set left out.

In Figure 3 additional elements). Overall, we find that predictions are most accurate when the formula contains TM, MG and s-block metal with an MAE of 0.066, while the other categories have slightly larger error and more mispredictions far away from the diagonal (MAE = 0.084 for TM + MG and MAE = 0.071 for others). It is possible that the s-block metals, the main effect of which is to donate electrons, play a minor and more predictive role in terms of affecting the TM-anion interaction, and thus help to reduce prediction errors in these compounds.

In Figure 3 (b)-(d) we show errors related to each TM element. The error is relatively small for Sc, Cu and Zn, likely due to their fewer possible oxidation states. On the other hand, errors are relatively large for V and Fe. As it can be seen in the comparison between the calculated and predicted number of unpaired spins, these two elements have the most scattered spin polarization, leading to the more error-prone prediction results.

Finally, Figures 3 (e) and (f) show the error in terms of different coordination geometries and metal-metal bond distances. It is interesting to note that although both are well understood in terms of transition-metal chemistry, prediction for TM atoms in tetrahedral coordination is more accurate. For the metal-metal distance, we find a clear trend that with increasing the metal-metal distance up to 5 Å, the prediction errors decreased. This agrees well with the analysis made above which shows that the approach from local geometry is valid if long-range metal-metal interactions can be safely ignored. Above 5 Å, the prediction error increases again, which is likely due to complex crystal structures with a large unit cell.

Application

We apply our machine learning model to predict the local magnetic moments on the 3d transition metal elements given by m s = 2µ B S p (S p + 1) from the predicted unpaired electron numbers S p = (N occ. ↑ -N occ.

↓ )/2. [START_REF] Jiles | Introduction to magnetism and magnetic materials[END_REF] Crystal structure parameters (for example, CIF files) are the only input that is necessary. Such an application can be useful, for example, to help assess the electronic configurations of the transition-metal compounds when access to DFT electronic structures is difficult (for experimental chemists) or when CPU time is of concern (screening). Long time (hours) needed for carrying out DFT calculations on structures of the data set can be reduced to within a few seconds to obtain magnetic moment prediction for hundreds of compounds on a normal laptop CPU, irrespective of the cell size.

We use the "first-shell" method as the structure description and average the prediction results among 10 separately trained models. [START_REF] Bishop | Pattern recognition and machine learning; Information science and statistics[END_REF] A total of 170 compounds were predicted and tested against DFT calculations. Each of the compounds contain from 30 to 100 atoms per unit cell. Therefore they are not included in the training data sets, and they were randomly chosen from the structure databases as Only one site is predicted for a given compounds and results are sorted according to increasing polarization calculated using DFT method. The error bar shows the standard deviation in the predictions of the ensemble model. MAE and RMSD score are reported in the table for all prediction (Total) and prediction where number of unpaired electron is larger than 0.2 (Mag.)

It was found that the model shows good ability to distinguish magnetic compounds from the non-magnetic ones, although there are occasional overestimation of spin polarization.

The most notable one is the compound Mn 4 Si 7 , which is predicted to have an unpaired electron count around 2 but is in fact almost non-magnetic. In terms of values of predicted magnetization, a MAE value of 0.123 was obtained over all samples, which increased to 0.279 if only the magnetic ones were counted. From Figure 4, the main prediction errors in magnetization come from two regions where prediction underestimated the calculated values. We found that the first region was due to the mispredictions related to V and Cr, while the second region of mispredictions concerns exclusively compounds containing Fe with a tetrahedral coordination. This agrees with our observation in the training that V and Fe show the largest error. It should be pointed out that these compounds share some common features of disruption of long-range ordering that might influence their physical properties. For example, Mn 4 Si 7 and Mn 3 Ge 5 are examples of Nowotny chimney-ladder structures which consist of the interpenetration of [Si/Ge] and [Mn] subsystems with an incommensurate c parameter. [START_REF] Castillo | Magnetic and Thermoelectric Properties of hp-Mn 3 Ge 5[END_REF][START_REF] Sato | Glass-like Lattice Thermal Conductivity and Thermoelectric Properties of Incommensurate Chimney-Ladder Compound FeGe γ[END_REF][START_REF] Le Tonquesse | Crystal structure and high temperature X-ray diffraction study of thermoelectric chimney-ladder FeGe γ (γ ≈ 1.52)[END_REF][START_REF] Le Tonquesse | Influence of Stoichiometry and Aging at Operating Temperature on Thermoelectric Higher Manganese Silicides[END_REF][START_REF] Guo | Thermoelectric Performance of Cr Doped and Cr-Fe Double-Doped Higher Manganese Silicides with Adjusted Carrier Concentration and Significant Electron-Phonon Interaction[END_REF] Similarly, Fe 7 Se 8 and its counterpart Fe 7 S 8 are well known examples of compounds with a superstructure due to ordered intrinsic vacancies. [START_REF] Li | Metal-Insulator Transition Induced by Spin Reorientation in Fe 7 Se 8 Grain Boundaries[END_REF][START_REF] Simon | Exploring the thermoelectric behavior of spark plasma sintered Fe 7-x Co x S 8 compounds[END_REF] The difference between ideal input structures and experimental ones must contribute to prediction deviations. Furthermore, some of these compounds may show a strong itinerant magnetic behavior, as exhibited by Mn 4 Si 7 , for instance, and therefore, local description of the magnetic moment may not be valid. The last two compounds, namely Ba 2 FeSbS 5 and Ba 3 FeSe 5 correspond to the highpressure and low pressure phases of Ba 3 FeS 5 , respectively. [START_REF] Lemley | Ba 15 Fe 7 S 25 and Ba 3 FeS 5 : Crystal structures, Mössbauer, magnetic, and electrical behavior[END_REF][START_REF] Cohen | Phases in the Ba 3 Fe 1+x S 5 series: The structure of β-Ba 9 Fe 4 S 15 and its low-temperature α polymorph[END_REF][START_REF] Berthebaud | crystal structure and electronic properties of the new iron selenide Ba 9 Fe 4 Se 16[END_REF][START_REF] Maier | Sb-5 s lone pair dynamics and collinear magnetic ordering in Ba 2 FeSbSe 5[END_REF][START_REF] Geng | Crystal and Electronic Structures, and Characterizations of Quaternary Antiferromagnetic Sulfides: Ba 2 MFeS 5 (M = Sb, Bi)[END_REF] For the latter, the experimental magnetic moment extracted from the magnetic susceptibility measurement seems to fit well with Fe 4+ ions. However, a Mössbauer analysis rather showed Fe 3+ , 55 suggesting some complexity of the magnetic properties. It is also noteworthy that this compound can deviate from stoichiometry as part of the solid solution Ba 3 Fe 1+x Se 5 (0 ≤ x ≤ 1). [START_REF] Cohen | Phases in the Ba 3 Fe 1+x S 5 series: The structure of β-Ba 9 Fe 4 S 15 and its low-temperature α polymorph[END_REF] Therefore, one important point that should not be overlooked is how the crystal and electronic structures of these compounds with a large unit cell in general are experimentally more complex than assumed in the training data sets, and this complexity contributes to the prediction errors when comparing to experimental results.

Other factors contributing to the deviations between prediction and experimental results include the choice of functionals, geometry relaxation and a more careful determination of the magnetic ground state, which are related to the DFT calculations which form the base of training data. It is known that GGA functionals systematically underestimate of the spin polarization due to the inaccuracy in the treatment of exchange interactions. However, hybrid functionals which could partly overcome this problem, are computationally too much expensive to perform in a high-throughput way. Geometry relaxation could also influence the value of the magnetic moment, but according to the few compounds we tested, its effect is minor in most cases. Finally, we note that DFT self-consistent calculations only determine a local minimum which may not be the true ground state. Exploration of different magnetic structures and carefully determination of the ground state could lead to more accurate DFT results and therefore better ML prediction, although this would come at the expense of CPU time. [START_REF] Noh | A density functional theory investigation of the electronic structure and spin moments of magnetite[END_REF][START_REF] Horton | High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory[END_REF] Finally, it should be stressed again that the machine learning method developed here was trained on a dataset of solid-state compounds containing transition-metals surrounding by a specific type of neighbor atoms to predict their DFT-calculated properties. It is not then expected that it will perform well for oxides or nitrides for instance, due to the specific choice of the training dataset. It will also not perform well for metals or covalently-bonded semiconductors due to the underlying theory of transition-metal-anion interactions.

CONCLUSION

Motivated by the use of local atomic structure to directly estimate the electronic structures of the transition-metal elements in inorganic chemistry, a neural network model using only local structure descriptions and trained from DFT-calculated electronic structures was developed to predict the electronic occupations of the 3d transition metal atoms in inorganic compounds including diverse intermetallic and semiconducting compounds. Trained on DFT calculations, this ML model is rather accurate in predicting the electron occupation of spin-up and spin-down channels for TM 3d orbitals. We also performed decomposition of prediction errors showing that they roughly agree with our chemical intuition. The application of this network model was illustrated to predict the magnetic moments of transition-metal atoms in compounds with large unit cells, that would be costly to calculate with the DFT method.

Although the prediction results of the current model are not fully ideal when comparing to experimental observations, it constitutes a step toward building better ML models based on local structure features to predict electronic properties of inorganic solid-state compounds.

Data and Code Availability

The Python codes used in this work are organized as a package and can be found in Github repository https://github.com/whzhangg/ElectronicConfiguration. Crystal structures and calculated electronic occupations are provided along the code as well as examples. Complete data of DFT input and output can be obtained upon requests from the authors.

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 33

Figure 1 :

 1 Figure 1: (a) Part of the Periodic Table showing the element set: transition metals (yellow), simple metals (light green) and main group anions (blue-green), 65 elements inside the red box are used for one-hot encoding. (b) Schematic illustration of how the symmetry of local polyhedron and distortion determine the energy level splitting of the transition-metal d states. (c) Crystal structure of the example compound BaCoS 2 (d)-(f) Different methods to represent the local structure of Co atoms in the example structure: (d) "Voronoi-bonded", (e) "First-shell" and (f) "Cut-off" (5 Å) with their respective fingerprints.

Figure 2 :

 2 Figure 2: (a) Workflow divided into training and application stages. The input for the model is the local structure fingerprints F generated using MILAD package and one-hot encoding, and the target output is the DFT calculated 3d orbital occupations N 3d ↑/↓ . (b) Detailed structure of the neural network model. Input matrices have the shape 65 × 117, which is reduced to 65 × 40 dimension by self-interaction layers. The matrices are flattened and introduced into fully connected linear layers to give final outputs for the orbital occupation of spin-up and spin-down channels. Training (c) and testing (d) MAE of a 10-fold cross validation with different local environment descriptions as function of the number of epochs. The error bars show standard deviations.

(

  shuffle of atomic index) invariants. It encodes local structure by projecting the atomic density function f c (x) = i f (x -r c i ) to a set of rotational invariant basis functions built from spherical harmonics. Here, r c i is the position of the ith atom of element type c, function f in the summation can be a delta or Gaussian function providing the atomic contribution to the density function f c . As shown in SI, a simple neural network can extract angular and distance distortions perfectly using the structure fingerprints generated from the distorted structure, showing that this representation encodes local geometric faithfully. This is in contrast to the graph-based networks 26-28 typically built from only inter-atomic distances, which does not encode angular information. Finally, elemental information is encoded by one-hot encoding. The element set for one-hot encoding is shown in Figure 1 (a).

  loss function. Random drop-out and weight decay are used to improve the performance of the network 48 while training epochs are determined from 10-fold cross validation. Information on training parameters and further details of the neural network used in this work are provided in SI.

Figure 3 :

 3 Figure 3: (a) Comparison between predicted and calculated values of the occupation of the TM 3d orbitals (N 3d ↑/↓ ) for both spin-up and spin-down channels in each compound of the training set (TM = transition metal, MG = main group). Spin-up and spin-down occupations are plotted together. The gray area refers to error of 0.15 electron. We highlighted several transition-metal type that deviate the most from the diagonal line. MAE and RMSD of all the test samples are shown in the table. (b) and (c) Histograms of calculated and predicted number of unpaired electron (N 3d ↑ -N 3d ↓ ) for the complete training data (they are normalized in such a way that maximum values are the same for each elements). (d)-(f) Prediction error (MAE) for different categories of training samples (blue curve) and their relative distribution (gray bars). All errors refer to the y-axis in (d). Distribution in each plot sums up to 1. A decomposition of figure (e) and (f) for each transition metal element is given in SI.

  long as they satisfy the requirement that 3d transition metal atoms are surrounded by main group anions. Prediction results in terms of unpaired electron numbers S p are compared to the results of DFT calculations, and are shown in Figure 4. The entries were sorted according to the DFT calculated magnetizations.

Figure 4 :

 4 Figure 4: Predicted and calculated number of unpaired electrons for the application dataset.Only one site is predicted for a given compounds and results are sorted according to increasing polarization calculated using DFT method. The error bar shows the standard deviation in the predictions of the ensemble model. MAE and RMSD score are reported in the table for all prediction (Total) and prediction where number of unpaired electron is larger than 0.2 (Mag.)

Table 1

 1 Si 7 P 4c2 2.97 Mn-other 2.49 ± 0.92 0.01 51 Mn 3 Ge 5 P 4 3.05 Mn-octa 3.49 ± 0.44 1.00 52 Fe 7 Se 8 P 3 1 3.17 Fe-octa 3.02 ± 0.46 2.40 53 MnGa 2 S 4 I4 5.45 Mn-tetra 5.32 ± 0.10 5.10 54 Ba 3 FeS 5 P nma 6.27 Fe-tetra 3.62 ± 0.32 5.10 55 V 4 S 9 Br 4 P 4/nmm 2.98 V-other 1.68 ± 1.53 1.77 56 V 2 P 4 S 13 P 1 3.71 V-octa 2.90 ± 0.05 2.80 57 CsMnInTe 3 C2/c 4.31 Mn-tetra 5.34 ± 0.05 5.92 58 K 10 Mn 4 Sn 4 S 17 R3m 3.91 Mn-tetra 5.21 ± 0.19 5.00 59 Ba 3 FeS 4 Br P nma 6.26 Fe-tetra 3.61 ± 0.31 3.85 60 Ba 2 FeSbS 5 P nma 5.84 Fe-tetra 3.67 ± 0.35 5.09 61 Finally, we compared our model with a series of experimental measurements available, as shown in Table 1. The accuracy between prediction and experiments is worse than when comparing to DFT results, with a relatively large error observed for some of them such as Mn 3 Ge 5 , Mn 4 Si 7 , Fe 7 Se 8 , Ba 3 FeS 5 , or Ba 2 FeSbS 5 .

		: Space Group (SG), d M-M ( Å), Metal Coordination (M coord.), ML Predicted
	(m pred s	, µ B ) Spin-Only Magnetic Moments (± standard deviation) and Experimental Mag-
	netic Moments (m exp s , µ B ) of ML Predicted Magnetic Compounds
		Formula	SG d M-M M coord.	m pred. s	m exp. s	ref.
		Cr 5 S 8	P 1 2.96	Cr-octa 3.78 ± 0.07 4.00 50
		Mn 4

Acknowledgement

We acknowledge support from JST Mirai Program JPMJMI19A1 and helpful discussion with Dr. F. Pawula, Dr. I. Ohkubo and Dr. G. Lambard (NIMS, Tsukuba).

Supporting Information Available

Supplementary information can be found free of charge from the publisher website, including details on compound selection, DFT calculations, an overview of rotational invariant descriptor generation and neural network construction. It also contains a full list of compounds used for training and testing, with their properties such as metal-metal distance and coordination types.


J.-B Sirven

P Dewalle

C Quéré

V Fauvet

M Tabarant

S Motellier

L Golanski

A Guiot

M Amdaoud

S Clavaguera

A Roynette

S Pontreau

F.-X Ouf

F Gensdarmes

Assessment of exposure to airborne carbon nanotubes by laserinduced breakdown spectroscopy analysis of filter samples

Exposure assessment is a key step in the evaluation of the risk induced by the handling of engineered nanomaterials. It is a very complex task, because several properties of nanoparticles are assumed to have an effect on their hazard. For exposure monitoring at workplace, real-time onsite measurements are commonly implemented to measure the particles size and number density, whereas the sampled material is subsequently analysed by electron microscopy. A complementary approach would consist in doing onsite chemical analysis of the filter samples, in order to routinely monitor a potential chronic exposure. Laser-induced breakdown spectroscopy (LIBS) has distinctive advantages for that purpose. Therefore, this work aims at evaluating the performances of LIBS to assess the exposure to airborne carbon nanotubes (CNTs) at workplace. As carbon is a ubiquitous element in the environment, our strategy was to target metal impurities of CNTs, aluminum and iron in our case. Then, we proceeded in three steps. First, we optimized the choice of the filter type to get the lowest detection limit for both elements. Secondly, this filter was used to quantitatively measure deposited CNTs. Eventually, we conducted an onsite measurement campaign in an industrial CNTs production plant to evaluate the exposure in a real situation. We demonstrated that we could reach a detection limit for CNTs compliant with the current NIOSH recommendation of 1 µg/m 3 , and that the detected CNTs during the onsite campaign in areas accessible to workers were at an extremely low concentration, several orders of magnitude lower than this recommendation.

JB_Sirven_2017-1.tif

After graduate studies in optics, Jean-Baptiste Sirven discovered the world of elemental analysis during his PhD at University Bordeaux 1 on the detection of heavy metals in soils by laser-induced breakdown spectroscopy (LIBS). When he defended it in 2006, he did not suspect that more than 10 years later he would still be working on this fascinating technique, so simple and so complex at the same time. One member of his thesis jury introduced him at the French Alternative Energies and Atomic Energy Commission (CEA) as a post-doc researcher, where he became a member of the ChemCam team and briefly contributed to the development of the Martian LIBS instrument. When CEA decided to keep him as a permanent fellow, he switched to more down-to-earth, but equally challenging research topics of interest in the nuclear field, where LIBS is a relevant option for a number of applications. His current favorite subjects cover fundamentals of laser ablation and LIBS, development of multivariate methods for LIBS spectra processing, and also detection and analysis of particles. Jean-Baptiste has been the coordinator of the French network on LIBS since 2013.

Introduction

The intense development of industrial applications of engineered nanomaterials has led to their dissemination in our environment, which has been raising strong concerns about their potential adverse effects on human health. This issue is particularly acute for people who produce and handle those materials, who are obviously more exposed to inhalation, ingestion and penetration through the skin. To better evaluate and manage the risk induced by engineered nanomaterials, toxicological studies aim at characterizing their hazard, while in parallel different types of measurements are developed to assess the exposure, in particular at workplace. This latter topic was reviewed by Maynard et al. in 2007, 1 and more recently by Ding et al. in the context of industries and research laboratories. 2 Among the variety of nanomaterials produced, carbon nanotubes (CNTs) raise numerous questions due to their fibrous nature. 3 Different studies focused on exposure assessment to airborne CNTs. [4][5][6][7][START_REF] Meyer-Plath | Annual World Conference on Carbon -Carbon 2015[END_REF] As for other types of nanomaterials, one major challenge is to be able to measure the different properties of the nano-aerosol that may have an effect on the hazard: size, number and mass concentration, composition, shape, surface area, etc. As a matter of fact, the exposure assessment at workplace necessarily combines different techniques to quantify all measurands of interest. Furthermore, the strategy also depends on the expected type of exposure, in relation with the ambient background level. For an acute exposure, for example due to an important leakage on a production process, a granulometer might be sufficient to trigger an alarm. But this is not an option for chronic exposure studies, for which the expected concentration in ambient air is much lower. In this case, filter sampling is necessary in order to accumulate enough material for analysis, and selectivity and sensitivity with respect to the ambient background are required. In addition to real-time measurements and samples observation by electron microscopic techniques, chemical analysis of the sampled material is an essential step to specifically identify, and further to quantify, the presence of airborne nanomaterials. [START_REF] Ono-Ogasawara | [END_REF] In particular, optical spectrometry, mainly Raman and fluorescence, X-ray fluorescence (XRF) spectrometries, [10][11][12] or ICP-MS 13 were used to analyze nanomaterials on a filter. However, as mentioned in ref. 5, those techniques raise the question of their routine onsite implementation for periodic exposure assessment. Laser-induced breakdown spectroscopy (LIBS) is a well-known elemental analytical technique to do onsite or even in situ measurements. The analysis is fast, it is able to detect all elements, and the instrumentation can be compact. Those potential advantages make LIBS relevant for workplace monitoring. Therefore, in this paper we address the issue of exposure assessment to airborne CNTs by LIBS analysis of filter samples. Quantitative analysis of particles deposited on filters by LIBS was demonstrated by several teams, including in early publications by the group of Radziemsky on the detection of Be. 14 Neuhauser et al. obtained detection limits of heavy metals between several tens and several hundreds of ng/cm². 15 In their work, particles were collected on a filter band translated inside an exhaust duct of a waste incineration facility. 100 nm NaF particles were analysed on membrane filters with a limit of detection for F of 160 ng/cm² under helium atmosphere. 16 Metals in diesel particles collected in the exhaust gas of an engine were analyzed for different air/fuel ratios. 17 Size-resolved quantitative analysis of 14 elements deposited on successive collection plates of a cascade impactor was demonstrated by Kuhlen et al. 18 LIBS was also used to measure metals in PM 10 sampled on a nylon membrane during Asian dust and pollution events. Nanoalloys and nanocomposites were recently characterized on silicon substrates. 21 In ref. Then, the objective of this work is to assess the performances of LIBS for onsite monitoring of chronic exposure to airborne CNTs. In particular, we aim at reaching a detection limit compliant with the exposure limit recommended by the NIOSH in 2013, of 1 µg/m 3 for an 8-hour average during 45 years.

24

Yet, as carbon is a ubiquitous element in the environment, we focused on the analysis of metal impurities instead, Al and Fe in our case. To this end, we proceeded in three steps. First, we deposited lab-produced Al 2 O 3 and Fe 2 O 3 nanoparticles on 9 different types of filters to determine the most suitable one for low-level detection of both elements. Then, CNTs deposits were prepared on the selected filter, and their detection limit was determined in laboratory conditions. Eventually, we made an onsite measurement campaign in an industrial CNTs production plant to assess exposure in a real workplace. The final results of this study show that aerosolization of a few CNTs bundles inside the plant was detected, with an equivalent concentration 3 orders of magnitude lower than the exposure limit recommended by NIOSH. Al was a growth medium residue, whereas Fe was a catalyst residue. Their concentration was stable in the powder, equal to 2.3% for Al and 1.9% for Fe (supplier data). After grinding and acid attack (H 2 SO 4 / HNO 3 ) to increase their solubility, they were diluted and sonicated to be dispersed in water at a concentration of ca. 100 mg.L -1 , and then nebulized to deposit the CNTs on polycarbonate filters (Millipore GTTP, 37 mm diameter, 0.22 µm pore diameter, 25-30 µm thickness). By varying the exposure time between 1 and 60 minutes, the CNTs concentration on the filter could be estimated, and further validated by non-destructive total-reflection X-ray fluorescence (TXRF) measurements. The measured Fe concentration on the filter surface was between 0.4 and 6 ng/cm². For carbon calibration, the same setup was used with QMA filters of 25 mm diameter. The enrichment time was between 1 and 30 minutes. Each filter was cut in two. One half was analysed by LIBS, the other half was measured by thermooptical analysis. A Lab OC-EC Aerosol Analyzer (Sunset Laboratory Inc.) was used for that purpose, with the IMPROVE analysis protocol. 25 The deposited carbon mass was calibrated using a sucrose aqueous solution with a concentration of 10 g.L -1 . The total carbon concentration obtained on the filters was between 3 and 6 µg/cm².

Experimental

Onsite measurements.

The onsite campaign was realized in an industrial CNTs production plant. CNTs were synthesized inside a reactor by a Catalytic Chemical Vapor Deposition process. Then, CNTs were guided either to a barrelling workstation, where they were put into barrels for shipping, or to an extruder, where they were incorporated into a polymer matrix to form an extruded string that was subsequently granulated. CNTs were completely confined throughout the production and transport process within the plant, except for the barrelling and extrusion workstations. Therefore, we focused on those two places to measure a potential release of CNTs in the ambient air.

The barrelling workstation was surrounded by plastic blades, with an underpressure maintained inside by a ventilation system. A particle counter (Portable Aerosol Spectrometer, GRIMM) was used for continuous monitoring of the particles number density inside the workstation. During the barrelling operation, a barrel was connected to an isolation valve, filled with CNTs, then the valve was disconnected. This was done remotely from the outside of the workstation. In some cases, the operator had to wait until the particle number concentration had decreased below a predefined threshold value, and then he passed the arms through the plastic blades to put the barrel cover and get the barrel out of the workstation. This phase lasted approximately one minute and was the only moment when a potential release of CNTs inside the workstation might occur. Three sampling points were defined. The first one was the entrance of the extraction system inside the workstation (BW1). The second one was outside the workstation, at the height of respiratory airways of a man (BW2). The third one was the operator himself (BW3).

On the extrusion workstation, the CNTs feed system included a barrel of CNTs connected to a butterfly valve. The system was vibrated to inject CNTs at the extruder entrance, where they were mixed with a polymer matrix at an adjustable temperature. As this zone could potentially release CNTs in the ambient air, three extraction systems were placed above. At the other end of the extruder, the extruded string was granulated by a machine and the granules formed fell into barrels. At this point, CNTs were confined inside the polymer, but a very low risk of CNTs release could not be excluded. Three sampling locations were defined in the extrusion workstation: one above the incorporating zone (EW1), one close to the butterfly valve (EW2) and one close to the granulation machine (EW3). Finally, the centre of the plant was defined as the sampling point for background measurements (BG). 4 different sampling devices were used for the onsite measurement campaign:

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A button sampler with a portable pump (SKC) to measure the inhalable fraction of the aerosol. A "home-made" sampler with the shape of a Y, enabling to take two samples on 25-mm filter cassettes in the same time, with a single pump (Reciprotor).

A portable aerosol sampler integrating a 1 L.min -1 pump, a filter cassette with a 4-mm diameter inlet, and a cyclone with a 4 µm cutpoint (NANOBADGE -NanoInspect, Alcen Group, Paris and CEA, Grenoble).

28-29

For all samplers, polycarbonate filters with 0.2 µm pore diameter were used (Millipore GTTP). Their diameter was 37 or 25 mm, depending on the system. The sampling points and sampling parameters are given in Table 1.

LIBS analysis

The LIBS system is presented in Fig. 2. It included a Nd:YAG laser at 355 nm (Brio, Quantel) with a 20 Hz repetition rate, a pulse duration of 5 ns FWHM and a maximum pulse energy of 25 mJ. An optical system was designed at the laser output to get a top-hat beam at the sample surface. Hence, the sample was ablated by a homogeneous circular laser spot of 220 µm diameter. An achromatic telescope was used to collect plasma emission and to inject it into an optical fibre with 0.22 numerical aperture, 910 µm core diameter and 1 m length.

The fibre was connected to a 300 mm focal length spectrometer (SP2300i, Acton) with a slit width of 100 µm and a 2400 grooves/mm grating centred at 396.5 nm in order to simultaneously measure the sensitive Al lines at 394.40 and 396.15 nm, along with the Fe line at 404.58 nm. The resolving power of the system was λ/Δλ = 5000 and the spectral bandwidth was 18 nm. The detector was an intensified CCD camera (iStar, Andor) with 2048x512 pixels of 13 µm. For calibration of C concentration on QMA filters, the spectrometer was centred at 183 nm in order to measure the C line at 193.09 nm.

For laboratory studies on oxide nanoparticles, measurements were performed with a pulse energy of 21 mJ (i.e. an irradiance of 11 GW/cm² on the sample), a gate delay of 1 µs after the laser pulse and a gate width of 1.5 µs. Those temporal parameters were optimized in order to maximize the signal-to-noise ratio of the Fe line. On each filter, a 25x25 matrix of single-shot spectra was recorded, with a 1 mm step between successive laser shots. The single-shot mode was compulsory for all membrane filters, since each individual laser shot drilled the filter. For QMA filters of 450 µm thickness, 3 laser shots could be accumulated at each point. The acquired spectra were subsequently summed line by line to get 25 replicates of 25 accumulated analysis points. For polycarbonate filters used for CNTs measurements, 20x20 single shot spectra were acquired, and summed 20 by 20. For QMA filters used for calibration of carbon, 6 replicates of 17 accumulated laser shots were recorded per filter.

For the onsite measurement campaign, a pulse energy of 17 mJ was used, with the same temporal parameters. Depending on the available filter surface, between 72 and 484 single-shot spectra were recorded. A measurement was defined as the accumulation of 22 laser shots. Therefore, the number of replicates was between 3 and 22 depending on the sample.

Results and discussion

Laboratory measurements of Al and Fe

The calibration curves of Al and Fe in deposits of oxide nanoparticles made in the laboratory were established for all types of filters, in order to determine the best one for CNTs low-level detection. Fig. 3 shows four of them, for HA, PC, PVC and QMA filters. Similar results, not shown, were obtained for the other filters. For each replicate, the line intensity was measured as the sum of 5 pixels centred on the line wavelength, then it was corrected for the background. For better readability, error bars represent ±2 standard deviations of the line net intensity, calculated from the 25 replicates. The low concentration range of calibration curves is fit by a line whose slope is used to determine the limit of detection according to the standard expression = 3 / ARTICLE J. Name., 2013, 00, 1-3 | 5

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where is the standard deviation of the blank net intensity at the line wavelength. Two blank filters were measured: an unexposed filter and a filter exposed to dry air during 5 minutes (median enrichment time). The standard deviation was determined in both cases, respectively and .

was calculated as the average of and . As shown in Fig. 3, all calibration curves are nonlinear. This is due to self-absorption of the sensitive chosen emission lines, and it is all the more pronounced for Al, since the 396.15 nm line is a resonant one. For the Fe 404.58 nm line, the lower level has an energy of 11976 cm -1 , relatively far from the fundamental level, therefore it is prone to self-absorption only for the most concentrated samples. For Al, we see that the calibration obtained on QMA filters is very bad, whereas the Fe calibration is correct. This is due to the fact that those quartz fibre filters are not free from aluminum, with an unknown and fluctuating concentration from one sample to the other. Therefore, there is an interference with the substrate that cannot be corrected. In addition, error bars are significantly larger for this type of filter than for the other ones. As error bars for Fe are comparable to other calibration curves, the ablation repeatability in the case of Al is not in question. This discrepancy is attributed to noise introduced by the background correction. As an example, for the filter with 690 ng/cm² of Al, the relative standard deviation (RSD) of the Al line net intensity was twice that of the raw intensity. In contrast, the RSD of the Fe line of the sample at 790 ng/cm² of Fe was almost the same before and after background correction. Finally, this clearly excludes QMA filters for analysis of Al-based nanoparticles. Except for that particular case, the signal repeatability is satisfactory, with a typical RSD between 2% and 7% for all types of filters. Also, a good linearity is observed in the low concentration range. Fig. 4 shows the detection limits obtained for all types of filters. We see that they lie between 0.8 and 2.4 ng/cm² for Al, and between 3.5 and 21 ng/cm² for Fe in our experimental conditions. As the background fluctuation was found similar for all membrane filters, the difference in the detection limit is attributed to the calibration line slope obtained in the low concentration range. Indeed, variations of the filter composition and microstructure leads to a different absorption efficiency of the laser energy and to a different interaction regime, therefore to a variable sensitivity from one filter to the other. To our knowledge, analysis of Al and Fe on filters was not published previously, but in comparison, the detection limits of other metals available in the literature extend from 10 to a few hundreds of ng/cm². [30][31] From the obtained surface detection limit LOD s , the equivalent average concentration in the sampled air volume LOD v can be calculated as LOD v = LOD s .π/4.φ²/Q.Δt, with φ the filter exposed diameter, Q the sampling flow rate and Δt the sampling duration. For a typical personal aerosol sampler such as the Nanobadge (see Table 1), with φ = 23 mm, Q = 1 L.min -1 and Δt = 8h, a surface limit of detection of 1 ng/cm² is equivalent in the ambient atmosphere to about 8 ng/m 3 .

In our case, the PC filter was found to be the one with the lowest detection limits both for Al and Fe. As our objective is to detect CNTs at a low concentration based on the analysis of their impurities at the percent level, the detection limit of those elements is the main choice criterion. In addition, in parallel with this study, non-destructive TXRF analyses were performed on several samples prior to LIBS measurements. PC filters are favourable for those analyses, 11 as they are very flat and smooth, and also easily observable by SEM. Therefore, in the following section, the analysis of Al and Fe in CNTs deposits was performed with PC filters. In this case, polymer membrane filters are of course excluded, but QMA ones can be used, like for atmospheric aerosols measurements for instance. 35 Therefore, the CNTs deposits on QMA filters described in the experimental section were analysed by LIBS. Before that, the enrichment process had to be validated by a reference method. Fig. 6 shows the results of OC-EC analysis of the filters. It demonstrates that the elemental carbon concentration, i.e. the carbon nanotubes concentration, is well correlated to the enrichment time, and that the contribution of organic carbon is approximately constant, around 3 µg/cm², and predominant in the total carbon concentration. This organic carbon is the result of atmospheric CO 2 adsorption on quartz fibres. Its concentration in QMA filters depends on their storage conditions and duration. We note that a similar concentration was measured for an unexposed filter, meaning that the CNTs deposition protocol did not enrich the filter in additional organic carbon.

Laboratory measurements of CNTs

For LIBS measurements, the C 193.09 nm line was chosen instead of the more widespread 247.86 nm one. The excitation level is the same in both cases, but the 193.09 nm line has a 10 times higher transition probability. Although this gain was somewhat reduced due to the poor transmission efficiency of optical elements below 200 nm, the length of the optical fibre we used was relatively short (1 m), and the VUV line was found more sensitive than the UV one. Fig. 7 shows the carbon LIBS signal plotted against the total, organic and elemental carbon concentration. We see that the LIBS signal is not correlated to the total carbon, but to the elemental carbon, i.e. to the CNTs concentration on the filters. The following explanation is proposed: the vaporization efficiency of carbon from CNTs and of carbon from adsorbed CO 2 on quartz fibres is different. Therefore, the LIBS signal of carbon can be expressed as

= . [ ] . + ! . [ ] ! .
, with ≠ ! . We checked that the silicon signal was constant for all samples, meaning that the ablated volume was constant from one filter to the other. And from Fig. 6, we see that the concentration of organic carbon is also approximately constant. Finally, the second term of the above equation is roughly constant, and we get a linear correlation of the LIBS signal with the CNTs concentration. This also justifies why the intercept of the linear fit is not zero: this is the organic carbon contribution. The limit of detection of carbon on QMA filters is evaluated at 300 ng/cm² in our experimental conditions, leading to an equivalent concentration in the ambient atmosphere of 2.6 µg/m 3 with the same sampling parameters as previously. It is higher than the NIOSH recommended exposure limit (REL) for CNTs, but the measurement could be improved by accumulating more laser shots. However, we should emphasize that the quantitative analysis of elemental carbon, as demonstrated here, is only possible if the organic carbon contribution is constant for all samples or negligible. This was the case for CO 2 adsorbed in lab-prepared filters, whose contribution could be potentially measured on an unexposed part of the filter in addition to the analysis of the particles deposit. But this is not representative of atmospheric samples, for which a significant, and possibly highly fluctuating contribution of organic aerosols, would prevent any linear calibration. To overcome this limitation, filters could be heated in an oven at a suitable temperature under a controlled atmosphere to eliminate organic carbon prior to LIBS analysis, without modifying the elemental carbon.

Onsite measurement campaign

Having established that airborne CNTs sampled in representative conditions of a chronic exposure could be measured by LIBS with a sufficiently low detection limit, we implemented onsite sampling and analysis in an industrial CNTs production plant, as described in the experimental section. Yet, onsite samples revealed several issues. The first one was that the sampled material did not sufficiently adhere to the filter surface to undergo hundreds of laser shots. Indeed, the LIBS signal was found to decrease up to a factor of 3 during the acquisition, due to aerosolization of deposited particles by successive laser shots. Visual examination of the most enriched filters before and after analysis confirmed this hypothesis. Such a drift had not been observed with labprepared filters. Consequently, in order not to underestimate the measured concentration, only the first replicate, i.e. the first 22 laser shots, was used for quantitative measurements, assuming that it was the most representative of the initial state of the filter before analysis. Then, the error bar shown in the following was calculated as the standard deviation of the net signal over the 22 first single shots, divided by √22.

A second issue was that samples from the inside of the barrelling workstation were logically quite loaded with particles. As they were visibly black instead of white for labprepared, low concentrated samples, absorption efficiency of the laser radiation was much higher. So was the obtained signal, and we checked that the Al and Fe calibration previously obtained could not be used for those samples. Therefore, the LIBS measurement was re-calibrated using a single onsite representative sample that was analysed by ICP-OES, and assuming that the signal was linear over the calibration range. This assumption might be discussed in relation with the possibility of self-absorption with onsite samples. However, the re-calibration was performed with the second filter most concentrated in particles, yielding an Al concentration of 8.2 ng/cm². Over all onsite samples, this led to a maximum Al concentration measured of 13 ng/cm², approximately twice the maximum concentration of the calibration range shown on Fig. 5 for which no self-absorption was detected. In comparison, Fig. 3 shows that for oxide nanoparticles, self-absorption starts at a much higher concentration, of the order of 500 ng/cm² for PC filters. In addition, as shown in sections 3.1 and 3.2.1, a similar detection limit was obtained both for oxide nanoparticles and CNTs, meaning that the laser absorption efficiency is similar for both types of particles and that self-absorption might be expected for CNTs deposits at a comparable concentration level.

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Therefore, we considered that the linearity assumption was reasonably valid for the onsite re-calibration. Fig. 8 shows the measured concentrations of Al and Fe in onsite samples, in units of µg/m 3 in the ambient atmosphere, using sampling parameters of Table 1. The NIOSH REL for CNTs is also shown, considering a concentration of Al and Fe of 3.5% each in the CNTs produced in the plant, as measured by ICP-OES on production samples. The first three of them are samples from the inside of the barrelling workstation, where no operator is allowed to enter, the other ones are from other locations of the production plant, where the staff can move and carry out operations. A first comment can be made about the reproducibility of measurements, looking at two couples of samples taken in the same conditions after two days, BW1a/BW1c and BW2a/BW2b. Although error bars are large due to the low signal accumulation described above, measurements of day 1 and day 3 are within error bars, so the reproducibility was found satisfactory.

As expected, the first three samples exhibit the highest concentrations, with a value for Fe largely exceeding the REL for BW1a and BW1c. The difference between samples BW1a and BW1b, taken at the same place and at the same moment, is due to different sampling parameters and geometry. The CATHIA system has a horizontal slit that sampled the aerosol in the direction of the extraction flow, whereas the Nanobadge sampler was perpendicular to the flow, and with a sampling flow rate ten times lower. Therefore, the CATHIA collected more efficiently large, heavy particles, in this case CNTs bundles that were aerosolized during the barrelling operation, and found a higher concentration than the Nanobadge. In contrast to BW1 filters, all other samples in Fig. 8 show concentrations significantly below the REL, lower than 12 and 18 ng/m 3 respectively for Al and Fe, meaning that the exposure limit was fortunately not reached during normal operation in the CNTs production plant. Yet, a significant Al and Fe signal was detected, and taking into account the CNTs confinement throughout the plant and the sampling conditions inside the barrelling workstation and for other sampling locations, this signal was somewhat surprisingly high. Indeed, if there were CNTs on those filters, we could have expected that their concentration was several orders of magnitude lower than that of the first three ones, placed at the entrance of the extraction system inside the barrelling workstation. In addition, the Fe/Al ratio for those samples seems to vary much more than that of the BW1 filters. Therefore, our assumption was that the Al and Fe signal measured in the plant did not stem from CNTs, but mostly from other compounds present in the background atmosphere. This is likely, because both elements are frequently present in air at the µg/m 3 level, particularly in urban areas, [36][37][38] and then are not specific to CNTs.

The Fe/Al ratio was investigated to check if it could give a more peculiar information on the presence of CNTs. From Fig. 8, we determined that for filters exposed to CNTs it is between 3 and 4.5. The uncertainty on this ratio is quite high, but it is consistent with the 3.4 value measured by ICP-OES on a filter loaded with CNTs. Although both elements have an identical global concentration of 3.5% in CNTs, the Fe/Al ratio is significantly higher than 1 because Fe is a catalyst residue, present inside individual nanotubes, whereas Al is a residue of micrometric Al 2 O 3 particles used as a growth medium for CNTs. Due to the mass of those particles, aerosolized CNTs are depleted in Al, and the sampled material is enriched in Fe. For other samples, the Fe/Al ratio spans from 0.7 to 2.5. Such a ratio is a coarse indicator, but it could help to selectively identify nanotubes with a better confidence than the mere Al or Fe concentration. However, one way to go further is to analyse the shot-to-shot correlation between the Al and Fe signals, shown in Fig. 9 for 3 samples: one from the ambient background in the production plant (BGa), one from the inside of the barrelling workstation (BW1a), for which the ambient background contribution is considered negligible, and one taken during granulation of the extruded string (EW3a). As one can see, no particular correlation is found for the ambient background, whereas a clear correlation can be observed from the BW1a sample. This correlation can be considered as the signature of CNTs, provided their concentration in Al and Fe does not change during production. Looking at the EW3a filter, we see that the obtained plot is a mixture of the two previous ones, with most laser shots showing no correlation like for the background filter, and a few ones exhibiting a correlation similar to that of the BW1a filter. This means that most of the Al and Fe signal for this sample came from the ambient background, and that a few CNTs bundles were sampled on this filter, yielding an abnormally intense LIBS signal with the characteristic Fe/Al correlation. This was supported by SEM observations of background samples and of the Nanobadge samples carried by the operator of the barrelling workstation, which found CNTs bundles with a size up to 20 µm. This confirmed that a very limited aerosolization of CNTs took place in the ambient atmosphere of the plant. However, the maximum CNTs concentration for those samples was estimated at 5 ng/m 3 , well below the REL. Finally, the shot-toshot analysis of the filters enabled to identify the presence of sparse CNTs bundles over the filter, which was not possible using the average Al and Fe concentration. As a conclusion, this onsite measurements campaign was a success, as it showed that we were able to assess onsite the exposure to airborne CNTs in a production plant. Beyond that, the main feedback was that the choice of the filter, that had been defined from laboratory analyses, must be questioned, since it was found not suitable to correctly fix the sampled particles. In particular, the use of a fibre filter such as the QMA one can be raised again, since in this case particles are trapped in the filter volume rather than deposited on the surface, then aerosolization due successive laser shots might be less pronounced. Hence, aerosolization of CNTs powder could be done experimentally in order to better simulate the sampling, to better optimize the filter type, and to calibrate the measurement on more representative samples. For that purpose, different aerosolization devices were proposed in the literature for toxicological studies.

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Conclusions

The objective of this work was to assess the possibility of onsite monitoring of chronic exposure to airborne carbon nanotubes in a workplace atmosphere by LIBS analysis of filter samples. Due to the ubiquitous nature of carbon in the environment, CNTs metal impurities, Al and Fe, were chosen as elements of interest. We proceeded in three steps. We first evaluated the detection limit of both elements for different types of filters. LIBS analysis of lab-prepared Al 2 O 3 and Fe 2 O 3 nanoparticles deposits led to the selection of polycarbonate filters, that had a detection limit of 0.8 and 3.5 ng/cm² respectively for Al and Fe in our experimental conditions. This type of filter was then used to make controlled CNTs deposits, and we got an equivalent detection limit of 0.43 µg/m 3 of CNTs in ambient atmosphere, for sampling parameters typically used with personal samplers. This value is close to the NIOSH recommended exposure limit for CNTs, of 1 µg/m 3 . Finally, a measurement campaign in an industrial CNTs production plant showed that onsite analysis was possible. All samples taken in the area accessible to workers exhibited an Al and Fe concentration much lower than the exposure limit. Yet, by analysing the shot-to-shot correlation of Al and Fe LIBS signals, we showed that most part of the measured Al and Fe concentration stemmed from the background, and we were able to identify a few CNTs bundles sampled on different filters. The equivalent CNTs concentration in the workplace atmosphere was nearly 3 orders of magnitude lower than the exposure limit. Those results demonstrated that LIBS is a suitable technique to perform airborne nanoparticles monitoring with a detection limit compliant with current recommendations, even in the case when the target elements are ubiquitous, and with a possibility to extract maximum information from the samples when shot-to-shot spectra are analysed. Of course the task will be all the easier if discriminant elements are measured, like for example Co, used as a catalyst of CNTs growth in different processes 42 or in composite nanotubes, 43 whose concentration in the ambient atmosphere must be negligible, or B in boron nitride nanotubes, 44 or Ce in CeO 2 nanoparticles. 45 Compared to the TXRF technique, also implemented for laboratory or onsite direct analysis of filters, LIBS has a comparable detection limit, [10][11] with the additional possibility to measure light elements such as carbon, as exemplified in this paper.

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  Engineered TiO 2 and Fe 2 O 3 nanomaterials sampled on filters were analyzed by Dewalle et al.

  ) respectively for Al 2 O 3 and Fe 2 O 3 particles. A 4-way flow splitter enabled to deposit the particles on two filters in the same time. Both ways were equipped with a valve, a flow rate regulator and a pump. A third way was connected to a Tapered

	2.1 Samples 2.1.1 Deposition of oxide nanoparticles. In order to determine the detection limit of CNTs through the measurement of their Al and Fe impurities, a first series of experiments was conducted with Al Element Oscillating Microbalance (TEOM 1105) measuring the deposited mass in real time. The flow rate in the three ways was Q = 3 L.min -1 and was regulated by a mass flow controller. The fourth way was used as an exhaust with a HEPA filter to prevent any release of nanoparticles in the ambient air. The deposited mass on the filter can be expressed as m filter = C v .Q.Δt with Δt the enrichment time, and the mass density as C s = m filter /S with S the filter exposed surface. The maximum enrichment time was 10 min, leading to a total deposited mass of Al or Fe on the filter of the order of 15-20 µg, as measured by the TEOM. This measurement was validated by duplicating several filters and ICP-OES analysis after filter dissolution. The deposit homogeneity was qualitatively controlled by SEM observations. The filters had a 47 mm diameter and were of different nature: quartz fibres (Whatman QMA 1851 (QMA)), PTFE (Millipore FSLW (FS) and Pall Zefluor (Zefluor)), PVC (Pall GLA-5000 (PVC)), cellulose esters (Pall GN-4 Metricel (MEC), Millipore HAWP (HA) and Sartorius Sedim 11104 (CellAc)), and polycarbonate (Whatman Nuclepore (PC) and Millipore ATTP (ATTP)). In total, 9 different models of filters were tested. Except for the QMA filter, all of them were membrane filters with a thickness between 6 and 180 µm and a pore size between 0.2 and 5 µm. For the FS filter, only Al 2 O 3 particles were deposited. 2.1.2 Deposition of CNTs. Commercial multiwall CNTs (Graphistrength C100, Arkema) with Al and Fe impurities were used in this study. Those impurities came from the Catalytic Chemical Vapor Deposition process used for CNTs production:

2 O 3 and Fe 2 O 3 nanoparticles deposited on different types of filters. The filters enrichment setup is shown in Fig. 1. A spark generator (GFG-1000, Palas) was used to (C v

Table 1

 1 Location and parameters of samples during the onsite measurement campaign.

	Location		Sampling	Flow rate	Duration	Filter diameter	Reference
			device	(L.min	-1 )	(min)	(mm)
	Barrelling	Inside (day 1)	Cathia	10.4		305	37	BW1a
		Inside (day 1)	Nanobadge	1		305	25	BW1b
		Inside (day 3)	Cathia	10.4		143	37	BW1c
		Outside (day 1)	Button	2		305	25	BW2a
		Outside (day 3)	Button	2		236	25	BW2b
		Operator (day 1)	Nanobadge	1		305	25	BW3
	Extruder	Source (day 2)	Cathia	10		260	37	EW1a
		Source (day 2)	Nanobadge	1		260	25	EW1b
		Valve (day 2)	Button	2		260	25	EW2
		Granulation (day 2)	Cathia	10.4		260	37	EW3a
		Granulation (day 2)	Nanobadge	1		260	25	EW3b
	Background	(day 2)	Home made	3		400	25	BGa
		(day 3)	Cathia	10		440	37	BGb

  The concentration of Al and Fe impurities in CNTs deposited on polycarbonate filters was measured by LIBS. The calibration is shown in Fig.5. The error bars represent ±1 standard deviation calculated from the 20 replicates. Note that the Al signal is plotted versus the Fe concentration, because on the one hand, only Fe could be measured by TXRF before LIBS analysis, and on the other hand, only the Al LIBS signal was detected. This is not surprising in view of the Fe concentration, close to the detection limit of this element determined previously, and taking into account that 20 laser shots per spectra, instead of 25, were accumulated. (e.g. soot particles), or if the ambient atmosphere is filtered enough to reduce the ambient carbon background.

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	For lines of identical upper energy level, this theoretical ratio is
	the ratio of the lines strengths, in our case (gA) 396.15 /(gA) 394.40 =
	1.97, with A the Einstein coefficient and g the degeneracy
	level. Therefore, this nonlinearity was considered fortuitous
	and attributed to experimental error. Also, the non-zero
	intercept of the calibration line was attributed to a spectral
	interference of the Al 396.15 nm line with a molecular
	emission band, most probably from CN	33 and/or N 2 34 . Using
	the Al and Fe w/w fractions given in section 2.1.2, the
	detection limit was estimated at 1.1 ng/cm² of Al, which was
	consistent with the previous measurements, leading to an
	equivalent Al concentration in the atmosphere of 9.8 ng/m	3 in
	the same sampling conditions (23 mm, 1 L.min	-1 , 8h), and an
	equivalent CNTs concentration of 0.43 µg/m	3 . This result is
	compliant with the exposure limit recommended by the NIOSH
	for CNTs in workplace atmosphere cited in the introduction. In
	comparison, the detection limit of elemental carbon on a 25-
	mm filter by the reference method NIOSH 5040 is 90 ng/cm². 24
	Therefore, LIBS is a suitable technique to monitor the presence
	of impurities in airborne particulate matter, such as CNTs, for
	occupational safety purposes.

3.2.1 Analysis of impurities.

Although the signal intensity on Fig.

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seems to non-linearly depend on the concentration, no evidence of self-absorption was found. Indeed, the intensity ratio of both Al lines at 396.15 and 394.40 nm was found to be 2.17, 1.91 and 2.04 for the samples at 2.1, 4.2 and 6.2 ng/cm² respectively. Those values were not significantly different, and in very good agreement with the value expected when no self-absorption is present.

3.2.2 Analysis of C.

Being able to measure carbon itself would be relevant to monitor more pure CNTs, or other carbon-based 6 | J. Name., 2012, 00, 1-3 Please do not adjust margins Please do not adjust margins compounds

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  Shot-to-shot Al signal intensity versus Fe signal intensity, for three samples: BGa (background), BW1a (inside of the barrelling workstation), and EW3a (granulation process)

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	Al	8.E+03 BGa		8.E+05	BW1a		1.2E+05	EW3a
		6.E+03		6.E+05				8.0E+04
		4.E+03	Al	4.E+05				Al
		2.E+03						4.0E+04
		0.E+00		2.E+05
	-3.E+03 -2.E+03 -1.E+03 0.E+00 1.E+03 2.E+03 3.E+03 -2.E+03 Fe -4.E+03		0.E+00 0.E+00	1.E+05	Fe	2.E+05	3.E+05	0.0E+00 -5.0E+03 0.0E+00 5.0E+03 1.0E+04 1.5E+04 2.0E+04 Fe
	Figure 9
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| J. Name., 2012, 00, 1-3

A CATHIA sampler to measure the thoracic fraction of the aerosol.

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Acknowledgements

This work was part of the GENESIS project (Global Evaluation of Nanocomposites for Emerging Safe Industrial Solutions) supported by OSEO (France).

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