Theoretical X-ray absorption spectroscopy database analysis for oxidised 2D carbon nanomaterials

Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra

The use of sophisticated machine learning (ML) models, such as graph neural networks (GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly. However, ensuring the interpretability of these models' predictions remains a challenge. For example, a rigorous understanding of the predicted X-ray absorption spectrum (XAS) generated by such ML models requires an in-depth investigation of the respective black-box ML model used. Here, this is done for different GNNs based on a comprehensive, custom-generated XAS data set for small organic molecules. We show that a thorough analysis of the different ML models with respect to the local and global environments considered in each ML model is essential for the selection of an appropriate ML model that allows a robust XAS prediction. Moreover, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. By comparing this peak assignment to the core and virtual orbitals from the quantum chemical calculations underlying our data set, we demonstrate that it is possible to relate the atomic contributions via these orbitals to the XAS spectrum.

Machine Learning Frontier Orbital Energies of Nanodiamonds

Nanodiamonds have a wide range of applications including catalysis, sensing, tribology, and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new data set ND5k, consisting of 5089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory (DFTB) and their frontier orbital energies are computed using density functional theory (DFT) with the PBE0 hybrid functional. From this data set we derive a qualitative design suggestion for nanodiamonds in photocatalysis. We also compare recent machine learning models for predicting frontier orbital energies for similar structures as they have been trained on (interpolation on ND5k), and we test their abilities to extrapolate predictions to larger structures. For both the interpolation and extrapolation task, we find the best performance using the equivariant message passing neural network PaiNN. The second best results are achieved with a message passing neural network using a tailored set of atomic descriptors proposed here.

Unveiling the structural transformation and activity origin of heteroatom-doped carbons for hydrogen evolution

Heteroatom-doped carbon materials have been widely used in many electrocatalytic reduction reactions. Their structure-activity relationships are mainly explored based on the assumption that the doped carbon materials remain stable during electrocatalysis. However, the structural evolution of heteroatom-doped carbon materials is often ignored, and their active origins are still unclear. Herein, taking N-doped graphite flake (N-GP) as the research model, we present the hydrogenation of both N and C atoms and the consequent reconstruction of the carbon skeleton during the hydrogen evolution reaction (HER), accompanied by a remarkable promotion of the HER activity. The N dopants are gradually hydrogenated and almost completely dissolved in the form of ammonia. Theoretical simulations demonstrate that the hydrogenation of the N species leads to the reconstruction of the carbon skeleton from hexagonal to 5,7-topological rings (G5-7) with thermoneutral hydrogen adsorption and easy water dissociation. P-, S-, and Se-doped graphites also show similar removal of doped heteroatoms and the formation of G5-7 rings. Our work unveils the activity origin of heteroatom-doped carbon toward the HER and opens a door to rethinking the structure-performance relationships of carbon-based materials for other electrocatalytic reduction reactions.

Role of Dopants on the Local Electronic Structure of Polymeric Carbon Nitride Photocatalysts

Polymeric carbon nitride (PCN) is a promising class of materials for solar-to-chemical energy conversion. The increase of the photocatalytic activity of PCN is often achieved by the incorporation of heteroatoms, whose impact on the electronic structure of PCN remains poorly explored. This work reveals that the local electronic structure of PCN is strongly altered by doping with sulfur and iron using X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS). From XAS at the carbon and nitrogen K-edges, sulfur atoms are found to mostly affect carbon atoms, in contrast to iron doping mostly altering nitrogen sites. In RIXS at the nitrogen K-edge, a vibrational progression, affected by iron doping, is evidenced, which is attributed to a vibronic coupling between excited electrons in nitrogen atoms and C-N stretching modes in PCN heterocycling rings. This work opens new perspectives for the characterization of vibronic coupling in polymeric photocatalysts.

DFT study of the influence of impurities on the structural, electronic, optoelectronic, and nonlinear optical properties of graphene nanosheet functionalized by the carboxyl group -COOH

In this work, we propose a modified model of graphene oxide nanosheet (GON), based on the Lerf-Klinowski model, through which we attach a carboxyl group (GON-COOH) to the non-equivalent C atom of coronene-based graphene oxide with formation of sp3-like orbital bond. Beryllium, boron, nitrogen, oxygen, and fluorine atoms are integrated into the GON at identical sites in order to study their impact on the physical and chemical properties of GON. Our aim is to propose new efficient materials for applications in optoelectronics and nonlinear optics (NLO). Chemical reactivity and structural, optical, and nonlinear optical properties of GON and its derivatives GON-X (X: Be, B, N, O, and F atoms) were investigated by using the density functional theory (DFT) at the B3LYP-D3/6-31+G(d,p) level of theory. According to the results obtained, the binding energy per atom of GON compound decreases slightly with addition of atoms of the second period elements of the periodic table. The GON-F compound exhibits the smallest value of gap energy compared to other studied compounds and can then be considered a proficient candidate for photovoltaic applications. In regard to NLO properties, we found that the studied models of GON compound theoretically exhibit a larger value of the first static hyperpolarizability than urea, the reference compound for NLO properties.

X-ray Raman scattering for bulk chemical and structural insight into green carbon

X-ray Raman scattering (XRS) spectroscopy is an emerging inelastic scattering technique which uses hard X-rays to study the X-ray absorption edges of low-Z elements (e.g. C, N, O) in bulk. This study applies XRS spectroscopy to pyrolysis and hydrothermal carbons. These materials are thermochemically-produced carbon from renewable resources and represent a route for the sustainable production of carbon materials for many applications. Results confirm local structural differences between biomass-derived (Oak, Quercus Ilex) pyrolysis and hydrothermal carbon. In comparison with NEXAFS, XRS spectroscopy has been shown to be more resilient to experimental artefacts such as self-absorption. Density functional theory XRS calculations of potential structural sub-units confirm that hydrothermal carbon is a highly disordered carbon material formed principally of furan units linked by the α carbon atoms. Comparison of two pyrolysis temperatures (450 °C and 650 °C) shows the development of an increasingly condensed carbon structure. Based on our results, we have proposed a semi-quantitative route to pyrolysis condensation.

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

Uncovering the Charge Transfer between Carbon Dots and Water by In Situ Soft X-ray Absorption Spectroscopy

Carbon dots (CDs) exhibit outstanding physicochemical properties that render them excellent materials for various applications, often occurring in an aqueous environment, such as light harvesting and fluorescence bioimaging. Here we characterize the electronic structures of CDs and water molecules in aqueous dispersions using in situ X-ray absorption spectroscopy. Three types of CDs with different core structures (amorphous vs graphitic) and compositions (undoped vs nitrogen-doped) were investigated. Depending on the CD core structure, different ionic currents generated upon X-ray irradiation of the CD dispersions at the carbon K-edge were detected, which are interpreted in terms of different charge transfer to the surrounding solvent molecules. The hydrogen bonding networks of water molecules upon interaction with the different CDs were also probed at the oxygen K-edge. Both core graphitization and nitrogen doping were found to endow the CDs with enhanced electron transfer and hydrogen bonding capabilities with the surrounding water molecules.

Electronic structure modifications induced by increased molecular complexity: from triphenylamine to m-MTDATA

The starburst π-conjugated molecule 4,4',4''-tris(N-3-methylphenyl-N-phenyl-amino)triphenylamine (C57H48N4, m-MTDATA), based on triphenylamine (TPA) building blocks, is widely used in optoelectronic devices due to its good electron-donor characteristics. The electronic structure of m-MTDATA was investigated for the first time in the gas phase by means of PhotoElectron Spectroscopy (PES) and Near Edge X-ray Absorption Fine Structure (NEXAFS) spectroscopy. The combination of Density Functional Theory (DFT) calculations with the experimental spectra provides a comprehensive description of the molecular electronic structure. Moreover, by comparing the results with previous TPA measurements, we could shed light on how the electronic structure evolves when the molecular size is increased. We found that the C 1s photoelectron spectra of m-MTDATA and TPA are similar, due to the balance of the counter-acting effects of the electronegativity of the N atoms and the delocalization of the amine lone-pair electrons. In contrast, the increased number of N atoms (i.e. N lone pairs) in m-MTDATA determines a three-peak feature in the outermost valence binding energy region with strong contributions by the N 2pz orbitals. We also obtained a decrease of the HOMO-LUMO gap for m-MTDATA, which points to improved electron donating properties of m-MTDATA with respect to TPA.