Engineering topics
Sun, Wenyu
Publications and source records attributed to Sun, Wenyu.
Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models
Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.
Mechanistic Insights into the Electrochemical Oxidation of 5-Hydroxymethylfurfural on a Thin-Film Ni Anode
The electrochemical oxidation of alcohols is being explored as a favorable substitute for the oxygen evolution reaction owing to its capability to generate high-value products and lower overpotentials. Herein, we present a systematic investigation into the electrochemical oxidation of 5-hydroxymethylfurfural (HMF), a model biomass platform chemical, on a thin-film nickel catalyst, aiming to investigate the underlying reaction mechanism and shed light on the role of the catalyst’s microenvironment and phase on activity and product selectivity. Utilizing a combined experimental and computational approach, we demonstrate that NiOOH is the active phase for HMF oxidation. Additionally, we find a substantial impact of the electrochemical environment, particularly the electrolyte pH, on the reaction. Under highly alkaline conditions (pH = 13), higher activity for HMF oxidation is observed, accompanied by an increased selectivity toward 2,5-furandicarboxylic acid (FDCA) production. Conversely, a less alkaline environment (pH = 11) results in diminished HMF oxidation activity and a higher preference for the partial oxidation product 2,5-diformylfuran (DFF). Mechanistic insights from DFT studies reveal that geminal diols that are present under highly alkaline conditions undergo hydride transfer via HMFCA, while a shift to an alkoxide route occurs at a lower pH, favoring the DFF pathway. Hydride transfer energetics are also strongly affected by the surface Ni oxidation state. Finally, this integrated approach, bridging experimental and computational insights, provides a general framework for investigating the electrochemical oxidation of aldehydes and alcohols, thereby advancing rational design strategies in electrocatalysts for alcohol electro-oxidation reactions.
Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides
Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.
Coupled Experimental–Theoretical Characterization of a Carbon Electrode in Vanadium Redox Flow Batteries using X-ray Absorption Spectroscopy
Vanadium redox flow batteries (VRFBs) have emerged as promising solutions for stationary grid energy storage due to their high efficiency, scalability, safety, near room-temperature operation conditions, and the ability to independently size power and energy capacities. The performance of VRFBs heavily relies on the redox couple reactions of V 2+ /V 3+ and VO 2+ /VO 2 + on carbon electrodes. Therefore, a thorough understanding of the surface functionality of carbon electrodes and their propensity for degradation during electrochemical cycles is crucial for designing VRFBs with extended lifespans. In this study, we present a coupled experimental–theoretical approach based on carbon K edge X-ray absorption spectroscopy (XAS) to characterize carbon electrodes prepared under different conditions and identify relevant functional groups that contribute to unique spectroscopic features. Atomic models were created to represent functional groups, such as hydroxyl, carboxyl, methyl, and aldehyde, bonded to carbon atoms in either sp 2 or sp 3 environments. The interactions between functionalized carbon and various solvated vanadium complexes were modeled using density functional theory. A library of carbon K-edge XAS spectra was generated for distinct carbon atoms in different functional groups, both before and after interacting with solvated vanadium complexes. Here we demonstrate how these simulated spectra can be used to deconvolve ex situ experimental spectra measured from carbon electrodes and to track changes in the electrode composition following immersion in different electrolytes or extended cycling within a functional VRFB. By doing so, we identify the active species present on the carbon electrodes, which play a crucial role in determining their electrochemical performance.
First-principles evaluation of MnO 2 polymorphs as cathode material in lithium-ion batteries
MnO 2 polymorphs show distinct advantages and disadvantages when used as cathode material for Li-ion batteries (LIBs).
Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon
Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the understanding and design of functional materials. In recent years, significant advancements have been made in the development of data science approaches for the automated interpretation of XANES structure–spectrum relationships. However, existing studies have primarily focused on crystalline solids and small molecules, while fewer efforts have been devoted to disordered systems. Thus, in this work, we demonstrate the development of neural network models for predicting and interpreting XANES spectra of amorphous carbon (a-C) from local structural descriptors. Comparison between different structural descriptors expectedly shows that the inclusion of both bond length and bond angle information is necessary for an accurate prediction of the spectra. Among the descriptors considered in this work, we find that the local many-body tensor representation yields the highest accuracy and greatest interpretability so that it can be leveraged to understand the importance of structural motifs in determining XANES spectra. Furthermore, we also discuss performance of neural network models for predicting both local structure features, such as bond lengths and bond angles, and global chemical composition, such as the sp:sp 2 :sp 3 ratio.
Rate Coefficients for OH + NO (+N 2 ) in the Fall-off Regime and the Impact of Water Vapor
The termolecular, association reaction between OH and NO is a source of nitrous acid (HONO), an important atmospheric trace gas. Rate coefficients for the title reaction as recommended by evaluation panels differ substantially at the temperatures and pressures that prevail in the Earth’s boundary layer where the reaction is in the fall-off regime between low- and high-pressure limiting rate coefficients. Using pulsed laser methods for generation and detection of OH, we have reinvestigated the kinetics of the title reaction at pressures of 22–743 Torr (1 Torr = 1.333 hPa) and temperatures (273, 298, and 333 K) in pure N 2 and in N 2 –H 2 O bath gases. In situ optical absorption measurements were used to rule out any bias due to NO 2 or HONO impurities. Our rate coefficients (k 1 ) in N 2 bath gas are parametrized in terms of low-pressure (k o ) and high-pressure (k ∞ ) rate coefficients and a fall-off parameter (F C ) with k 1,0 N 2 = 7.24 × 10 –31 (T/300 K) –2.17 cm 6 molecule –2 s –1 , k 1,∞ = 3.3 × 10 –12 (T/300 K) –3 cm 3 molecule –1 s –1 , and F C = 0.53. Used with the “Troe” expression for termolecular reactions, these parameters accurately reproduce the current data in the fall-off regime and also capture literature rate coefficients at extrapolated temperatures. The presence of water vapor was found to enhance the rate coefficients of the title reaction significantly. The low-pressure limiting rate coefficient in H 2 O bath gas is a factor 5–6 larger than in N 2 , at room temperature (k 1,o H 2 O = 4.55 × 10 –30 (T/300 K) –4.85 cm 6 molecule –2 s –1 ) indicating that H 2 O is much more efficient in quenching the association complex HONO* through collisional energy transfer. Based on measurements in N 2 –H 2 O mixtures, a parametrization of k 1 including both N 2 and H 2 O as third-body quenchers was derived. Neglecting the effect of H 2 O results, e.g., in an underestimation of k 1 by >10% in the tropical boundary layer.
Chemical insights into the multi-regime low-temperature oxidation of di-n-propyl ether: Jet-stirred reactor experiments and kinetic modeling
To further understand the combustion characteristics and the reaction pathways of acyclic ethers, in this work the oxidation of di-n-propyl ether (DPE) was investigated in a jet-stirred reactor (JSR) combined with a photoionization molecular-beam mass spectrometer. The experiments were carried out at near-atmospheric pressure (700 Torr) and over a temperature range of 425–850 K. Based on the experimental data and previous studies on ether oxidation, a new kinetic model was constructed and used to interpret the oxidation chemistry of DPE. In DPE oxidation, a high reactivity at low temperatures and two negative temperature coefficient (NTC) zones were observed. These behaviors are explained in this work by taking advantage of the obtained species information and the modeling analyses: the two NTC zones are caused by the competition of chain branching and termination reactions of the fuel itself and specific oxidation intermediates, respectively. Furthermore, the general requirements to have double-NTC behavior are discussed. A variety of crucial fuel-specific C 6 species, such as ketohydroperoxides and diones, were detected in the species pool of DPE oxidation. Their formation pathways are illuminated based on rate-of-production (ROP) analyses. Propanal was identified as the most abundant small molecule intermediate, and its related reactions have an important impact on the oxidation process of DPE. Both acetic acid and propionic acid were detected in high concentrations. A new formation pathway of propionic acid is proposed and incorporated into the kinetic model to achieve a more accurate prediction for propionic acid mole fractions.