jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas D. Jones | Henry R. Brothers | Elokkin Pate-Geames | David H. Jenkins | Konstantinos D. Vogiatzis
Source: Dalton Transactions (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Reaction channels of iron-oxo and iron-imido molecular intermediates Non-heme Fe(IV)-oxo intermediates are integral components of many enzymatic and homogeneous catalytic cycles, where they serve as key oxidants that enable challenging transformations. Numerous studies have focused on developing biomimetic non-heme Fe(IV)-oxo catalysts, elucidating their electronic structures, and understanding the nature of their reactivity. Their high-valent nature endows them with the capacity to perform demanding oxidative transformations, as evidenced by their broad application in C-H activation and alkene epoxidation chemistry. The isoelectronic Fe(IV)-imido analogue is much less studied, presenting significant opportunities for catalyst development in nitrene-transfer chemistry, including C-H amination and alkene aziridination. Mechanistically, nitrene transfer involves an Fe-imido intermediate featuring an N-centered radical. Two competing pathways have been described: a σ-channel and a π-channel, defined by whether the radical character is localized in a σ* or π* orbital. Similar to Fe(IV)-oxo chemistry, the reaction channel preference among Fe(IV)-imido catalysts plays a crucial role in determining their reactivities and selectivities. Herein, the nitrene transfer pathways for these species within both reaction channels are explored and comparative examples between Fe(IV)-oxo and Fe(IV)-imido catalysts are discussed. Homogeneous ; Catalysis ; Reaction intermediate ; Reactive intermediate ; Key (lock) ; Enzyme Metal-Catalyzed Oxygenation Mechanisms ; Porphyrin Metabolism and Disorders ; Microbial metabolism and enzyme function
Source: International Journal of Corrosion and Scale Inhibition (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Corrosion inhibition performance of a hybrid organic–inorganic EDFS-1 inhibitor for steel evaluated by gravimetric, electrochemical, SEM and AFM methods This study reports the synthesis, characterization, and corrosion inhibition performance of a novel inhibitor, EDFS-1, prepared from ethylenediaminetetraacetic acid (EDTA), sodium metasilicate, and phosphoric acid.The optimal molar ratio of 1:3:1 was determined by gravimetric screening, providing the highest inhibition efficiency.EDFS-1 was evaluated for St20 steel in industrial water containing CO2, H2S, and their mixture, showing 84.6-94.2%inhibition at 100 mg/L, depending on the gas environment.In acidic media (0.5-1 M HCl), inhibition efficiency increased with concentration and temperature, reaching 92.98% in 0.5 M HCl and 93.75% in 1 M HCl at 333 K and 150 mg/L.Electrochemical measurements indicated that EDFS-1 significantly reduced corrosion current density while minimally affecting corrosion potential.Surface analyses (SEM and AFM) demonstrated smoother, less pitted steel surfaces in the presence of EDFS-1, confirming the formation of a protective adsorbed film.These results highlight EDFS-1 as an effective and thermally stable inhibitor for steel in CO2-H2S-containing waters and strongly acidic environments. Corrosion ; Gravimetric analysis ; Phosphoric acid ; Ethylenediaminetetraacetic acid ; Adsorption ; Corrosion inhibitor ; Atomic force microscopy ; Sodium hypochlorite Corrosion Behavior and Inhibition ; Carbon Dioxide Capture Technologies ; Calcium Carbonate Crystallization and Inhibition
Authors: Advait A. Bhale | Krishna R. Gupta | Monali R Dakhole | Milind J Umekar
Source: International Journal of Pharmaceutical Chemistry and Analysis (article, 2026-02-16)
Field: Chemistry — Analytical Chemistry

Advances and applications of ion chromatography in pharmaceutical and environmental analysis: A comprehensive review Ion chromatography (IC) has become a highly sensitive and selective method for determining ionic species, and its use in environmental and pharmaceutical investigations is growing. This review focuses on current developments in column technology, detection techniques, and IC equipment that have improved its analytical performance. IC is increasingly essential to the pharmaceutical industry's ability to characterise excipients, counter-ions, APIs, and trace-level contaminants in accordance with international pharmacopeial regulations. Reliable ultra-trace quantification has been made possible by advancements in conductivity, UV–Vis, and mass-based detection.High-pressure IC systems and multidimensional topologies offer better resolution and robustness, while innovations in resin design, such as smaller particles, difunctional exchangers, and enhanced porosity, have boosted chromatographic efficiency. In order to accurately quantify anions, cations, and metal-associated species for pollution tracking and water quality evaluation, IC is still essential in environmental monitoring. Precision for complicated samples is further enhanced by developments in stationary phases, suppressor technology, and eluent chemistry. Sustainable, high-throughput analysis with less solvent and sample consumption is being advanced by the combination of automation, digital platforms, and miniaturised IC systems. Emerging prospects that position IC as a crucial analytical tool for future scientific and regulatory demands are explored together with remaining obstacles, such as matrix interferences and method validation limits. Ion chromatography ; Column chromatography ; Resolution (logic) ; Complex matrix ; Environmental analysis ; Matrix (chemical analysis) Chromatography in Natural Products ; Analytical Chemistry and Chromatography ; Protein purification and stability
Authors: Wei Zhilei | Wenqi Xie | Tao Li | Qiaochu Chai | Y. C. | Lei Zhao | Bingbing Li | Bo Wang | Z. SHI
Source: Journal of the American Ceramic Society (article, 2026-01-29)
Field: Materials Science — Ceramics and Composites

Additive‐Free Combustion Synthesis of Ultrafine Equiaxed α‐Si <sub>3</sub> N <sub>4</sub> Powder for High‐Performance Ceramics ABSTRACT Ultrafine equiaxed α‐Si 3 N 4 powder is a promising raw material for next‐generation high‐performance substrates. However, conventional industrial methods are often hampered by high energy consumption, prolonged production cycle, and the use of polluting additives. Thus, the low‐cost production of high‐purity α‐Si 3 N 4 powder remains a persistent technical challenge. In this study, equiaxed α‐ Si 3 N 4 particles were synthesized via an additive‐free combustion synthesis approach. The influences of N 2 pressure and Si/Si 3 N 4 molar ratio on the combustion process, phase composition, and microstructure of the synthesized powders were systematically investigated. Under optimized conditions—specifically, a N 2 pressure of 3 MPa and a Si to Si 3 N 4 molar ratio of 2.5:1—equiaxed α‐Si 3 N 4 particles with an average size of 0.35 µm were obtained. Furthermore, the growth mechanism of α‐Si 3 N 4 particles was elucidated through heat absorption–assisted quenching and thermo‐kinetic analysis. The resulting sintered Si 3 N 4 ceramic exhibited a bending strength of 767 MPa and a thermal conductivity of 94.5 W·m −1 ·K −1 , respectively. This work provides a cost‐effective and efficient strategy for synthesizing high‐quality α‐Si 3 N 4 powder, demonstrating its potential application as a substrate raw powder for microelectronic applications. Equiaxed crystals ; Ceramic ; Combustion ; Microstructure ; Thermal conductivity ; Raw material ; Quenching (fluorescence) ; Cubic zirconia Advanced ceramic materials synthesis ; Intermetallics and Advanced Alloy Properties ; MXene and MAX Phase Materials
Authors: Ulduz Hashimova | Aliya Gaisina | Khatira Safikhanova
Source: Molecules (article, 2026-01-25)
Field: Chemistry — Analytical Chemistry

Black Gold in Medicine: Rediscovering the Pharmacological Potential This study explores crude oil as a chemically and structurally heterogeneous system with potential pharmaceutical relevance beyond its established roles as an energy and feedstock resource. Recent advances in analytical technologies have enabled the detailed characterization of crude oil constituents at the molecular level, thereby linking structural features to physicochemical properties and possible biological activities. The presented analysis outlines the rationale, methodological considerations, and future research directions for integrating crude oil molecular motifs into the pharmaceutical chemical space. Beyond its conventional role as an industrial and energy resource, crude oil may also hold promise for drug discovery. This study seeks to provide a conceptual framework for reconsidering crude oil as a reservoir of pharmacologically relevant scaffolds and to outline methodological approaches for their systematic assessment. Its rigid sp<sup>3</sup>-rich frameworks, together with sterane/hopane biomarkers, porphyrins, and functional aromatics, structurally overlap with established therapeutic classes and are naturally present in crude oil in suitable abundance, offering opportunities to reduce synthetic effort and expand the chemical space accessible to drug discovery. Advances in petroleomics and in silico methodologies now enable petroleum-derived constituents to be characterized in terms of drug-likeness, bioactivity, and toxicity, providing a framework to reconsider crude oil as an unconventional but analytically and computationally tractable resource for pharmaceutical research. Crude oil ; Chemical space ; In silico ; Drug discovery ; Relevance (law) ; Pharmaceutical sciences ; Pharmaceutical industry ; Resource (disambiguation) Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Forensic Fingerprint Detection Methods
Authors: Xiaoyu Zhang
Source: arXiv (Cornell University) (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

End-to-End Differentiable Learning of a Single Functional for DFT and Linear-Response TDDFT Density functional theory (DFT) and linear-response time-dependent density functional theory (LR-TDDFT) rely on an exchange-correlation (xc) approximation that provides not only energy but also its functional derivatives that enter the self-consistent potential and the response kernel. Here, we present an end-to-end differentiable workflow to optimize a single deep-learned energy functional using targets from both Kohn-Sham DFT and adiabatic LR-TDDFT. To enable this training in a computationally efficient and differentiable manner, we developed a JAX-based two-component quantum chemistry framework (IQC), in which the learned functional provides a self-consistent potential and linear-response kernel via automatic differentiation. This construction permits gradient-based optimization through both the self-consistent-field (SCF) fixed-point equations and the Casida eigenvalue problem. We learn an exchange-correlation functional on excitation energies of small molecules while incorporating one-electron self-interaction cancelation as penalty terms, and we assess its possible transfer to molecular test cases. Time-dependent density functional theory ; Density functional theory ; Differentiable function ; Orbital-free density functional theory ; Energy functional ; Kernel (algebra) ; Functional theory ; Workflow ; Spectrum (functional analysis) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Bahrem Serhat Danis | Amin Tabatabaei Mohseni | Smagul Karazhanov | Esra Özkan Zayim
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Inverse Engineering of Optical Constants in Photochromic Micron-Scale Hybrid Films Photochromic materials enable dynamic optical modulation through reversible transitions between distinct absorption states, with broad potential for smart windows, adaptive optics, and reconfigurable photonic devices. Micron-scale photochromic hybrid films present a particularly attractive platform for these applications, combining straightforward preparation with substantial optical modulation and scalability for high-volume fabrication. However, rational design of such films remains fundamentally constrained by the absence of well-defined optical constants. Unlike homogeneous thin films, micron-scale hybrid photochromic materials comprise active particles dispersed non-uniformly within polymer matrices. Conventional first-principles electromagnetic simulations face substantial computational costs and discrepancies between simulated and experimental particle distributions. Here, we introduce a data-driven framework that extracts effective optical constants directly from minimal experimental transmittance measurements. Our dual-state effective model approximates the complex inhomogeneous photochromic layer as a compressed homogeneous medium characterized by pseudo-refractive indices and pseudo-extinction coefficients for both pristine and UV-irradiated states. Through systematic optimization against experimental data from tungsten oxide-polyvinylpyrrolidone hybrid films, we determine wavelength-dependent pseudo-optical constants and compression ratios that enable accurate prediction of optical modulation within the tested thickness range. Our methodology establishes a framework for engineering hybrid photochromic systems and demonstrates how data-driven modeling can overcome limitations in characterizing complex nanostructured materials. Photochromism ; Photonics ; Hybrid material ; Inverse ; Transmittance ; Modulation (music) ; Inverse problem ; Absorption (acoustics) ; Material Design Photochromic and Fluorescence Chemistry ; Transition Metal Oxide Nanomaterials ; Metamaterials and Metasurfaces Applications
Authors: Akinshola Olabamiji Akinola | E. Prabakaran | Krishna K. Govender | Kriveshini Pillay
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Artificial neural network model of the capacity of diethylenetriamine functionalised zinc oxide-apricot stone shell nanocomposite for sequestering 2,4-dichlorophenoxyacetic acid from wastewater A novel nanocomposite, diethylenetriamine functionalised zinc oxide-apricot stone shell (ZnO@Ap/DETA), has been synthesised for the efficient sequestration of 2,4-dichlorophenoxyacetic acid (2,4-D). The capacity and removal efficiency were evaluated using an artificial neural network (ANN). The characterisation of ZnO@Ap/DETA was performed to determine its physicochemical properties through various instrumental techniques. The nanocomposite displayed a more uniform mesoporous structure, having pores with an average size of 34.065 nm and a significantly enhanced surface area of 26.56 m<sup>2</sup> g<sup>-1</sup>, approximately 13 times greater than that of the pristine apricot stone (AP) shells (2.076 m<sup>2</sup> g<sup>-1</sup>). ZnO@Ap/DETA was utilised to adsorb 2,4-D from synthetic wastewater at pH 3, 50 mg adsorbent dose and 30 mg L<sup>-1</sup> initial 2,4-D concentration, and a real wastewater sample at 18.5 mg L<sup>-1</sup> 2,4-D concentration and 50 mg adsorbent dose, achieving performance efficiencies of 98.6% and 85.41%, respectively, after a 90-minute agitation period at 25 °C. The data on adsorption were consistent with the pseudo-second-order kinetic model and Langmuir isotherm, and thermodynamic investigations suggested that the process was spontaneous, favourable, and exothermic, as evidenced by a Δ<i>H</i>° value of -92.85 KJ mol<sup>-1</sup>. To optimise the adsorption process, an ANN model was developed, comprising five input parameters, two hidden layers, and two output parameters. The developed model successfully predicted 2,4-D removal efficiency with a mean absolute error (MAE) of 0.2952, a mean squared error (MSE) of 0.4227, and a high <i>R</i> <sup>2</sup> of 0.9991. These results highlight the potential of ZnO@Ap/DETA for environmental remediation and for wastewater treatment. Diethylenetriamine ; Zinc ; Wastewater ; Nanocomposite ; Shell (structure) Synthesis of Tetrazole Derivatives ; Nanomaterials for catalytic reactions ; Chemical Synthesis and Reactions
Authors: Amirreza Asadollahzadeh | Amirali Tavakkoli‐Kakhki | Mehrdad Shokooh‐Saremi
Source: IET Optoelectronics (article, 2026-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Application of Metaheuristic Optimisation Methods to the Design of Guided‐Mode Resonance Filters: A Comparative Study ABSTRACT The design of optical elements often requires precise optimisation where metaheuristic algorithms have emerged as a powerful approach. This study aims to optimise a guided‐mode resonance reflectance filter chosen due to its extreme parameter sensitivity, which makes it an exemplary platform for evaluating metaheuristic algorithms in photonics. We rigorously compare six established metaheuristic techniques (particle swarm optimisation (PSO), genetic algorithm (GA), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), imperialist competitive algorithm (ICA), and differential evolution (DE)) using critical performance metrics. This systematic analysis not only benchmarks algorithm effectiveness for guided‐mode resonance filters but also establishes guidelines for optimising parameter‐sensitive photonic devices. Metaheuristic ; Differential evolution ; Parallel metaheuristic ; Genetic algorithm ; Swarm behaviour ; Filter (signal processing) ; Search-based software engineering ; Evolutionary algorithm Optical Coatings and Gratings ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Ryan Stocks | Elise Palethorpe | Amir Karton | Giuseppe M. J. Barca
Source: ChemRxiv (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Double-Hybrid, but not Double-Cost: GPU Accelerated DHDFT for the COMPAS-3 Dataset of Polybenzenoid Hydrocarbons We present the first implementation of double hybrid density functional theory (DHDFT) with all major computational steps accelerated on GPUs.To efficiently utilize GPU hardware, we employ the resolutionofidentity (RI) approximation, transforming electron repulsion integral (ERI) computations into large dense matrix multiplications and enabling basis functions up to g angular momentum.We demonstrate revDSD-PBEP86-D4(noFC)/def2-QZVPP calculations on the entire COMPAS-3x dataset of #39,000 peri-condensed polybenzenoid hydrocarbon isomer geometries (up to 68 atoms) using just 900 nodehours on the Perlmutter supercomputer.For medium-sized organic molecules (up to #3k basis functions), the PT2 component adds minimal cost relative to the initial SCF step.This demonstrates that efficient GPU acceleration reduces the practical computational requirements of DHDFT comparable to conventional hybrid DFT.We additionally benchmark a range of LDA, GGA, and MGGA functionals against the revDSD-PBEP86-D4(noFC) isomerization energies.Without dispersion corrections, the SVWN5 LDA functional (MAD 4.47 kJ/mol) outperforms all tested GGAs and MGGAs.With dispersion corrections, only two MGGAs, led by M06-L-D4 (MAD 3.82 kJ/mol), are able to surpass the SVWN5 results. Noise (video) ; Identification (biology) ; Feature (linguistics) ; Software Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Protein Structure and Dynamics
Authors: Yong Zhang | Feichen Cui | Yunhao Yao | Yijie Yang | Krzysztof Matyjaszewski | Jiajun Yan
Source: ChemRxiv (article, 2026-03-30)
Field: Chemistry — Organic Chemistry

A Hybrid Kinetic Monte Carlo Algorithm for ATRP: Coupling Deterministic Mean-Field Kinetics with Stochastic Chain Growth Kinetic Monte Carlo (kMC) simulations of Atom Transfer Radical Polymerization (ATRP) suffer from severe computational stiffness due to the fast activation-deactivation equilibrium, which consumes the vast majority of computational resources and limits applicability to large-scale or complex architectures. In this work, we propose a Hybrid Mean-Field/kMC Algorithm designed to significantly enhance computational efficiency while maintaining microscopic accuracy. The core strategy involves decoupling the macroscopic concentration evolution from the microscopic chain growth. First, a stiff Ordinary Differential Equation (ODE) solver is employed to pre-compute the macroscopic trajectories of radical and deactivator concentrations. Subsequently, within the Monte Carlo kernel, we introduce the concept of "Effective Activation Cycles." By sampling the number of monomer additions per activation event directly from a geometric distribution, the algorithm bypasses millions of null activation/deactivation loops. Furthermore, a monomer-consumption-based time stepping mechanism and a Poisson-process-driven termination strategy are implemented to ensure the physical fidelity of chain growth. Benchmarking results demonstrate that the hybrid algorithm yields perfect agreement with the exact Stochastic Simulation Algorithm (SSA), achieving a computational speedup of approximately 20 folds. To demonstrate its versatility, the framework is extended to computationally challenging regimes, including continuous catalyst regeneration and diffusion-controlled kinetics. Most notably, by integrating a dynamic disjoint-set topological algorithm, the method successfully bypasses the mathematical singularities of continuum models at the gel point. It accurately resolves extreme kinetic timescales—from the ultrafast transient microgelation in conventional free radical polymerization (FRP) to the delayed network percolation in ATRP—providing a high-fidelity digital twin of macroscopic sol-gel transitions and microscopic topological defects. Monte Carlo method ; Kinetic Monte Carlo ; Solver ; Hybrid Monte Carlo ; Speedup ; Dynamic Monte Carlo method ; Molecular dynamics ; Differential evolution Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications ; biodegradable polymer synthesis and properties
Authors: Carina Yi Jing Lim | Zhenan Bao | Lukas Michalek
Source: ChemRxiv (article, 2026-03-09)
Field: Materials Science — Polymers and Plastics

Coupling Nanoscale Viscoelastic Measurements and Infrared Heating of Polymers with AFM-IR In heterogeneous polymer systems, the viscoelastic response of polymers around interfaces or with nanostructured morphology may deviate from bulk values. However, current measurement techniques tend to mask these local variations, which are crucial for understanding and developing these materials. Coupling the high spatial resolution of atomic force microscopy (AFM) with dynamic mechanical analysis (DMA) offers an avenue to circumvent this limitation. We previously demonstrated the advantages of using a focused infrared (IR) laser on an AFM-IR system to locally heat a sample during nanomechanical measurements, with heating modulated by varying the IR repetition rate. [1] Herein, we present a brief demonstration of coupling nDMA with our previously demonstrated chemistry-selective IR heating. We first measured the storage and loss modulus of poly(propylene glycol) (PPG)-4,4′-methylenebis(phenyl urea) (MPU) at varying IR repetition rates, demonstrating a viscoelastic modulus response with typical heating characteristics upon crossing the glass transition temperature. We further demonstrated stress relaxation of an enaminelinked vitrimer possessing significant bond dynamicity at elevated temperatures. Overall, this method is in the preliminary stages of development, and this working paper aims to serve as a basis for feedback and discussion. Viscoelasticity ; Coupling (piping) ; Polymer ; Relaxation (psychology) ; Dynamic mechanical analysis ; Modulus ; Infrared ; Stress relaxation Polymer composites and self-healing ; Force Microscopy Techniques and Applications ; Luminescence and Fluorescent Materials