The spatial distribution of the chemical reservoirs in protoplanetary disks is key to elucidate the composition of planets, especially habitable ones. However, the partitioning of the main elements among the refractory and volatile phases is still elusive. Key parameters such as the carbon-to-oxygen C/O elemental ratio and the ionization fraction remain poorly constrained, with the latter potentially orders of magnitude lower than in the interstellar medium. Moreover, the thermal structure of the gas is also poorly known, despite its deep influence on gas-phase chemistry. In this context, ortho-to-para ratios could provide selective and sensitive probes. Recent ALMA observations have measured the spatially resolved column density of ortho-and para-H2CO in the transition disk orbiting TW Hya and derived the radial profile of the ortho-to-para ratio. Yet, current disk models do not include the nuclear-spin-resolved chemistry required to interpret these observations. The present work aims to fill this gap, by combining a parametric disk physical model of TW Hya with the UGAN network, updated to include a comprehensive description of the nuclear-spin-resolved chemistry of formaldehyde. This new model reproduces the observed column density of H2CO to within a factor of 2, as well as the measured ortho-to-para ratio which varies from 1.5 in the outer disk to 3 inside 90au. In particular the low value of this ratio beyond 90au is well explained by our model. However, the statistical value of 3 measured below 70au cannot be reproduced, suggesting that additional processes involving ices may be involved. Our parameter space exploration shows that the abundance of H2CO is highly sensitive to the C/O elemental ratio and to the cosmic-ray ionization rate. Future observations of ortho-and para-H2CO, based on well selected rotational transitions, in a large sample of disks, appear highly desirable.
Abstract Hereditary angioedema (HAE), an autosomal dominant disease that may be fatal in the larynx and gastrointestinal tract, can affect the skin and mucosal surfaces. Indium tin oxide-polyethyleneterephthalate (ITO-PET) electrode based on biosensor is put up in this study to detect C1-Inhibitor (C1-INH). The ITO-PET electrodes were surface have undergone a cleaning procedure. Hydroxylation (NH4OH, H2O2, H2O) was applied to the electrode surfaces. The ITO-PET electrode surfaces were then treated with 3-Aminopropyltrimethoxysilane (3-APTES). Crosslinking with glutaraldehyde was the following step. The concentration of 3-APTES, the concentration of anti-C1-INH, and the length of time that C1-INH was incubated were the ideal conditions. It conducted immobilization, optimization, and analysis stlod udies using techniques such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The linear range, repeatability, reproducibility, regeneration studies, single frequency studies (SFI), and storage of life of the biosensor were carried out in characterization studies. It was discovered that the linear range of the immunosensor was 2 fg/mL to 1500 fg/mL. It discovered the biosensor's (11 weeks) storage life. The LOD value calculated as a result of the study is 0.23 fg/mL and the LOQ value is 0.26 fg/mL. The biosensor was tested using the (SWV) square wave voltammetry technique (0–1.5 V, equal time: 2, frequency: 25 Hz, pulse size: 25 mV). The interaction between antibody C1-INH and C1-INH antigens was observed using a single frequency technique (SFI).The final stage was testing the biosensor on commercial serum studies.
Chemical technology, Physical and theoretical chemistry
Multimodal scientific reasoning remains a significant challenge for large language models (LLMs), particularly in chemistry, where problem-solving relies on symbolic diagrams, molecular structures, and structured visual data. Here, we systematically evaluate 40 proprietary and open-source multimodal LLMs, including GPT-5, o3, Gemini-2.5-Pro, and Qwen2.5-VL, on a curated benchmark of Olympiad-style chemistry questions drawn from over two decades of U.S. National Chemistry Olympiad (USNCO) exams. These questions require integrated visual and textual reasoning across diverse modalities. We find that many models struggle with modality fusion, where in some cases, removing the image even improves accuracy, indicating misalignment in vision-language integration. Chain-of-Thought prompting consistently enhances both accuracy and visual grounding, as demonstrated through ablation studies and occlusion-based interpretability. Our results reveal critical limitations in the scientific reasoning abilities of current MLLMs, providing actionable strategies for developing more robust and interpretable multimodal systems in chemistry. This work provides a timely benchmark for measuring progress in domain-specific multimodal AI and underscores the need for further advances at the intersection of artificial intelligence and scientific reasoning.
Efficient chemical kinetic model inference and application in combustion are challenging due to large ODE systems and widely separated time scales. Machine learning techniques have been proposed to streamline these models, though strong nonlinearity and numerical stiffness combined with noisy data sources make their application challenging. Here, we introduce ChemKANs, a novel neural network framework with applications both in model inference and simulation acceleration for combustion chemistry. ChemKAN's novel structure augments the generic Kolmogorov Arnold Network Ordinary Differential Equations (KAN-ODEs) with knowledge of the information flow through the relevant kinetic and thermodynamic laws. This chemistry-specific structure combined with the expressivity and rapid neural scaling of the underlying KAN-ODE algorithm instills in ChemKANs a strong inductive bias, streamlined training, and higher accuracy predictions compared to standard benchmarks, while facilitating parameter sparsity through shared information across all inputs and outputs. In a model inference investigation, we benchmark the robustness of ChemKANs to sparse data containing up to 15% added noise, and superfluously large network parameterizations. We find that ChemKANs exhibit no overfitting or model degradation in any of these training cases, demonstrating significant resilience to common deep learning failure modes. Next, we find that a remarkably parameter-lean ChemKAN (344 parameters) can accurately represent hydrogen combustion chemistry, providing a 2x acceleration over the detailed chemistry in a solver that is generalizable to larger-scale turbulent flow simulations. These demonstrations indicate the potential for ChemKANs as robust, expressive, and efficient tools for model inference and simulation acceleration for combustion physics and chemical kinetics.
To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemToolAgent, an enhanced chemistry agent over ChemCrow, and conduct a comprehensive evaluation of its performance on both specialized chemistry tasks and general chemistry questions. Surprisingly, ChemToolAgent does not consistently outperform its base LLMs without tools. Our error analysis with a chemistry expert suggests that: For specialized chemistry tasks, such as synthesis prediction, we should augment agents with specialized tools; however, for general chemistry questions like those in exams, agents' ability to reason correctly with chemistry knowledge matters more, and tool augmentation does not always help.
Three-body recombination, or ternary association, is a termolecular reaction in which three particles collide, forming a bound state between two, whereas the third escapes freely. Three-body recombination reactions play a significant role in many systems relevant to physics and chemistry. In particular, they are relevant in cold and ultracold chemistry, quantum gases, astrochemistry, atmospheric physics, physical chemistry, and plasma physics. As a result, three-body recombination has been the subject of extensive work during the last 50 years, although primarily from an experimental perspective. Indeed, a general theory for three-body recombination remains elusive despite the available experimental information. Our group recently developed a direct approach based on classical trajectory calculations in hyperspherical coordinates for three-body recombination to amend this situation, leading to a first principle explanation of ion-atom-atom and atom-atom-atom three-body recombination processes. This review aims to summarize our findings on three-body recombination reactions and identify the remaining challenges in the field.
Tommaso Nottoli, Ivan Giannì, Antoine Levitt
et al.
We present two open-source implementations of the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) algorithm to find a few eigenvalues and eigenvectors of large, possibly sparse matrices. We then test LOBPCG for various quantum chemistry problems, encompassing medium to large, dense to sparse, wellbehaved to ill-conditioned ones, where the standard method typically used is Davidson's diagonalization. Numerical tests show that, while Davidson's method remains the best choice for most applications in quantum chemistry, LOBPCG represents a competitive alternative, especially when memory is an issue, and can even outperform Davidson for ill-conditioned, non diagonally dominant problems.
Chemistry in Titan's N2-CH4 atmosphere produces complex organic aerosols. The chemical processes and the resulting organic compounds are still far from understood, although extensive observations, laboratory, and theoretical simulations have greatly improved physical and chemical constraints on Titan's atmosphere. Here, we conduct a series of Titan atmosphere simulation experiments with N2-CH4 gas mixtures and investigate the effect of initial CH4 ratio, pressure, and flow rate on the production rates and composition of the gas and solid products at a Titan relevant temperature (100 K) for the first time. We find that the production rate of the gas and solid products increases with increasing CH4 ratio. The nitrogen-containing species have much higher yield than hydrocarbons in the gas products, and the N-to-C ratio of the solid products appears to be the highest compared to previous plasma simulations with the same CH4 ratio. The greater degree of nitrogen incorporation in the low temperature simulation experiments suggests temperature may play an important role in nitrogen incorporation in Titan's cold atmosphere. We also find that H2 is the dominant gas product and serves as an indicator of the production rate of new organic molecules in the experiment, and that CH2NH may greatly contribute to the incorporation of both carbon and nitrogen into the solid particles. The pressure and flow rate affect the amount of time of the gas mixture exposed to the energy source and therefore impact the N2-CH4 chemistry initiated by the plasma discharge, emphasizing the influence of the energy flux in Titan atmospheric chemistry.
Dust grains play a central role in the physics and chemistry of cosmic environments. They influence the optical and thermal properties of the medium due to their interaction with stellar radiation; provide surfaces for the chemical reactions that are responsible for the synthesis of a significant fraction of key astronomical molecules; and they are building blocks of pebbles, comets, asteroids, planetesimals, and planets. In this paper, we review experimental studies of physical and chemical processes, such as adsorption, desorption, diffusion, and reactions forming molecules, on the surface of reliable cosmic dust grain analogues as related to processes in diffuse, translucent, and dense interstellar clouds, protostellar envelopes, planet-forming disks, and planetary atmospheres. The information that such experiments reveal should be flexible enough to be used in many different environments. In addition, we provide a forward look discussing new ideas, experimental approaches, and research directions.