Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning

A portable europium complex-loaded fluorescent test paper combined with smartphone analysis for the on-site and visual detection of mancozeb in food samples

The on-site detection of mancozeb in food samples holds immense value for food safety. A red-fluorescent europium complex (Eu-PYDC-Phen) has been prepared and employed as a fluorescence probe for mancozeb detection. The optimized probe suspension exhibits excellent detection performances, including a wide linear range (0-0.24 mM), low detection limit (65 nM), rapid response (2 mins) and high selectivity. Moreover, a portable detection platform was carefully designed, integrating the Eu-PYDC-Phen-based fluorescent test strips with smartphone color recognition software. This innovative platform enables visual and on-site detection of mancozeb in tomato, apple, and lettuce, achieving satisfactory recovery rates (90.34 to 106.50%). Furthermore, the integration of machine learning techniques based on hierarchical clustering algorithm has the potential to further improve the prediction and decision-making efficiency in mancozeb detection. This work provides an economical, convenient, and reliable strategy for on-site detection of pesticide in agricultural products, thereby making a meaningful contribution to food safety.

Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis

Single-atom catalysts (SACs) offer significant potential across various applications, yet our understanding of their formation mechanism remains limited. Notably, the pyrolysis of zeolitic imidazolate frameworks (ZIFs) stands as a pivotal avenue for SAC synthesis, of which the mechanism can be assessed through infrared (IR) spectroscopy. However, the prevailing analysis techniques still rely on manual interpretation. Here, we report a machine learning (ML)-driven analysis of the IR spectroscopy to unravel the pyrolysis process of Pt-doped ZIF-67 to synthesize Pt-Co3O4 SAC. Demonstrating a total Pearson correlation exceeding 0.7 with experimental data, the algorithm provides correlation coefficients for the selected structures, thereby confirming crucial structural changes with time and temperature, including the decomposition of ZIF and formation of Pt-O bonds. These findings reveal and confirm the formation mechanism of SACs. As demonstrated, the integration of ML algorithms, theoretical simulations, and experimental spectral analysis introduces an approach to deciphering experimental characterization data, implying its potential for broader adoption.

Mimicking reductive dehalogenases for efficient electrocatalytic water dechlorination

Electrochemical technology is a robust approach to removing toxic and persistent chlorinated organic pollutants from water; however, it remains a challenge to design electrocatalysts with high activity and selectivity as elaborately as natural reductive dehalogenases. Here we report the design of high-performance electrocatalysts toward water dechlorination by mimicking the binding pocket configuration and catalytic center of reductive dehalogenases. Specifically, our designed electrocatalyst is an assembled heterostructure by sandwiching a molecular catalyst into the interlayers of two-dimensional graphene oxide. The electrocatalyst exhibits excellent dechlorination performance, which enhances reduction of intermediate dichloroacetic acid by 7.8 folds against that without sandwich configuration and can selectively generate monochloro-groups from trichloro-groups. Molecular simulations suggest that the sandwiched inner space plays an essential role in tuning solvation shell, altering protonation state and facilitating carbon-chlorine bond cleavage. This work demonstrates the concept of mimicking natural reductive dehalogenases toward the sustainable treatment of organohalogen-contaminated water and wastewater.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

The normalization of the active surface sites of bimetallic Pd-Pt catalysts, their inhomogeneity, and their roles in methane activation

Multi-metallic catalysts containing Pt species are widely used. As there is no methodology to evaluate the quantity of active surface sites of Pt or other metal species, researchers have only published the total conversion or selectivity of all active surface sites. This study focuses on Pt-Pd bimetallic catalysts and uses both methane reaction kinetics and infrared (IR) spectroscopy to characterize the surface Pd and Pt sites. The surface Pt sites, which were determined from the fitted rate coefficients, were evaluated in the reaction region where the catalyst structure was insensitive to catalytic performance. Another methodology involves IR spectroscopy to normalize the active surface sites. As three typical absorption bands of Pt species were observed during CO chemisorption, spectral deconvolution was conducted to obtain the integrated intensity of the Pd and Pt species, and the quantity of surface Pd and Pt sites was calculated. The two methods have good consistency, and the IR spectra are considered to be more suitable for calculating the quantity of active surface sites. In addition, the IR spectra revealed a correlation between oxidative Pd surface sites and methane reactivity. The ionic Pd sites provide abundant oxygen intermediates in the catalytic reaction and improve the catalytic performance. As for the surface Pd species and bulk Pd species, the XPS results indicate a similar variation in the Pd^δ+/(Pd^δ+ + Pd⁰) ratio vs. Pd/Pt ratio.

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. We reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.

Pressure effects on the metallization and dielectric properties of GaP

In situ impedance measurement, resistivity measurements and first-principles calculations have been performed to investigate the effect of high pressure (up to 30.2 GPa) on the metallization and dielectric properties of GaP. It is found that the carrier transport process changes from mixed grain and grain boundary conduction to pure grain conduction at 5.8 GPa, and due to pressure-induced structural phase transition, the resistance drops drastically by three orders of magnitude at 25.5 GPa. Temperature dependence of resistivity measurements and band structure calculations suggest the occurrence of a semiconductor-metal transition. Combining differential charge density and dielectric analysis, it is observed that the electron localization is weakened, which leads to increased polarization and larger relative permittivity in the zb structure. After the phase transition, both the polarization and the relative permittivity decrease. Pressure increases the complex dielectric constant and dielectric loss factor, due to the increase in relaxation polarization and the scattering effect of carriers. Moreover, by comparing the high-pressure behavior of GaP, GaAs and GaSb, the changes in the electronic structure and electric transport process caused by the phase transition can be understood, which can enable us to better understand the metallization behavior and dielectric properties of Ga-based III-V family semiconductors under pressure, and stimulate the design and modification of other related group III-V semiconductors for optoelectronic devices and sensors.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.