Hasil untuk "Chemistry"

Mingwei Ding, Chen Huang, Yibo Hu et al.

Automating multistep computational chemistry tasks remains challenging because reasoning, workflow specification, software execution, and high-performance computing (HPC) execution are often tightly coupled. We demonstrate a decoupled agent-skill design for computational chemistry automation leveraging OpenClaw. Specifically, OpenClaw provides centralized control and supervision; schema-defined planning skills translate scientific goals into executable task specifications; domain skills encapsulate specific computational chemistry procedures; and DPDispatcher manages job execution across heterogeneous HPC environments. In a molecular dynamics (MD) case study of methane oxidation, the system completed cross-tool execution, bounded recovery from runtime failures, and reaction network extraction, illustrating a scalable and maintainable approach to multistep computational chemistry automation.

Vipul Bundake, Veena Khilnani, Archana Kale et al.

A pot experiment of maize was carried during summer seasons of March–July, 2023 and 2024 at the experimental field of Rashtriya Chemicals and Fertilizers, Mumbai, India, to assess the impact of multi nano micronutrients formulation (NM) on maize growth. The experiment was structured using a Completely Randomized Block Design with 12 treatments, including control with only water, Recommended Dose of Fertilizer (RDF), and different concentrations of NM having zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron (B) ranging from 20 mg to 0.15 mg 15 kg-1 of soil, as well as commercial micronutrients and micronutrient salts. Results revealed that application of 100% RDF+0.312 mg (T9) and 0.156 mg (T10) of nano micronutrients with drenching recorded better results of nutrient uptake (NU), apparent recovery (ANR) and agronomic efficiency (ARE). The NU (kg ha-1) of nitrogen (120.368), potassium (101.422), Cu (0.114), Fe (1.235), Mn (0.107) and Zn (6.069) was higher in T9 when compared to 100% RDF. The ANR was 9154.19% higher in T10 and 158.28% higher for Nitrogen(N), Phosphorus (P), and Potassium compared to 100% RDF. The protein and chlorophyll content were better in T9 and T10 of nano micronutrients respectively. The applications of T9 and T10 was found to be most effective in NU, ARE, ANR, protein content and chlorophyll content. Higher nutrient content in soil was found in treatment with lower concentrations. Overall, lower concentration of nano micronutrients appeared to be more effective for all traits.

Zhucong Li, Bowei Zhang, Jin Xiao et al.

Large Language Model (LLM)-based agents have demonstrated the ability to improve performance in chemistry-related tasks by selecting appropriate tools. However, their effectiveness remains limited by the inherent prediction errors of chemistry tools. In this paper, we take a step further by exploring how LLMbased agents can, in turn, be leveraged to reduce prediction errors of the tools. To this end, we propose ChemHAS (Chemical Hierarchical Agent Stacking), a simple yet effective method that enhances chemistry tools through optimizing agent-stacking structures from limited data. ChemHAS achieves state-of-the-art performance across four fundamental chemistry tasks, demonstrating that our method can effectively compensate for prediction errors of the tools. Furthermore, we identify and characterize four distinct agent-stacking behaviors, potentially improving interpretability and revealing new possibilities for AI agent applications in scientific research. Our code and dataset are publicly available at https: //anonymous.4open.science/r/ChemHAS-01E4/README.md.

Devansh Vimal, Cole Mathis, Westley Weimer et al.

Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.

Anjana Barange, Meitram Niraj Luwang, Santosh Kumar Meena

Govindhasamy Murugadoss, Nachimuthu Venkatesh, Pandurengan Sakthivel et al.

Quantum dots (QDs) are employed in photocatalytic applications because of their distinctive optical characteristics, such as high absorption coefficients and tunable bandgaps, enabling efficient visible light absorption and charge carrier generation. This study focuses on synthesizing homogeneous bismuth-doped tin sulfide (Bi-doped SnS) QDs for environmental remediation. Bi-doped SnS QDs with varying Bi concentrations are prepared via a facile, cost-effective chemical method, and their structural, optical, and morphological characteristics are analyzed through X-ray diffraction (XRD), UV–Vis spectroscopy, and transmission electron microscopy (TEM). TEM results confirm that the catalysts are highly homogeneous and tiny (<5 nm). Photocatalytic activity is assessed through the breakdown of Crystal Violet (CV) and Methylene Blue (MB) when exposed to visible light. High efficiencies of 89.0 % and 95.8 % are achieved for CV and MB, respectively, outperforming undoped SnS. Kinetic analysis reveals a pseudo-first-order reaction, providing insights into the underlying degradation kinetics. A plausible mechanism is proposed, elucidating how Bi-ion doping enhances photocatalytic performance and facilitates dye degradation. Additionally, toxicity evaluation using Vigna radiata seeds demonstrates the efficacy of the degradation process. Treated dye solutions (D-CV and D-MB) show no significant changes in intracellular ROS levels compared to untreated dye and control solutions, confirming reduced toxicity. These findings highlight the enhanced photocatalytic performance of Bi-doped SnS QDs and their potential in environmental purification, advancing the understanding of QD-based photocatalysts for sustainable applications.

Luna He, Nannan Hou, Rong Li

In this work, a life cycle assessment (LCA) approach was employed to evaluate the environmental impact of viscose fibers made from cotton, wood and bamboo pulp, in accordance with the ISO 14040 standard. Utilizing SimaPro 9.5.0 software, the research employed the ReCiPe 2016 (H) V1.13 and IPCC 2013 GWP100a methodologies to assess the life cycle of viscose fibers from “cradle to gate”, quantifying their environmental impacts. The findings revealed that during the pulp production stage, the cotton cultivation process contributed significantly to environmental impacts. Notably, bamboo pulp exhibited the lowest endpoint impact category. In the fiber production stage, the treatment, utility, and impregnation processes were identified as having prominent environmental impacts, with high carbon emissions primarily attributed to GWP 100-fossil in the utility and treatment processes. The production of 1 ton of viscose fiber using cotton as the raw material exhibits the highest environmental burden, with a total impact of 506.92 Pt. Wood-based production shows a moderate environmental impact of 470.74 Pt, while bamboo demonstrates the most favorable environmental profile at 453.43 Pt. Sensitivity analysis highlighted steam consumption as the most sensitive factor influencing environmental outcomes. Additionally, electricity usage and chemical reagents emerged as sensitive factors in the production of viscose fibers from different raw materials.

Zhiwei Zhang, Dawei Pan, Yan Liang et al.

Field determination and ecological risk assessment of dissolved lead (Pb) were performed at two Yellow Sea sites in China using a continuous automated electrochemical system (CAEDS). This CAEDS instrument includes an automatic triple filter sampler and an electrochemical detection water quality analyzer, which might be operated automatically four times daily. The dissolved Pb concentrations varied from 0.29 to 1.57 μg/L in the South Yellow Sea over 16 days and from 0.32 to 2.28 μg/L in the North Yellow Sea over 13 days. During the typhoon and algal bloom periods, the Pb concentration was as high as ten times greater than usual. According to the calculation of contamination factors (C_f) and subsequent analysis, seawater quality was classified as Grade II. Through species sensitivity distribution (SSD) method experiments and ecological risk analysis, an average risk quotient (RQ) below 1 for both areas was obtained, indicating a low-to-moderate ecological risk. This system will be helpful for Pb monitoring and assessment in seawater and contribute to the biogeochemical cycling study of Pb.

Husson Ara, Nasreen Naz, Ayesha Walid et al.

Background: Adenomyosis is an important benign gynecological condition among females with variable signs and symptoms. Prompt detection of suspicious cases is important for the effective management of the disease. The objective of the current study was to determine the frequency of adenomyosis on transvaginal ultrasound (TVS), its diagnostic accuracy, and the identification of associated factors in women with symptoms of adenomyosis. Methods: This cross-sectional study was carried out at the radiology department of Dow University Hospital, Karachi, Pakistan from January 2022 to March 2023. All married females of reproductive age group presented with symptoms of adenomyosis for more than 7 days were included. Adenomyosis on TVS and histopathology were noted. Moreover, associated factors of adenomyosis were also studied. Results: Of 280 patients, adenomyosis on TVS was observed in 180 (64.3%) patients whereas on histopathology in 176 (62.9%) patients.  Diagnostic accuracy of adenomyosis on TVS showed that sensitivity was 89.20%, specificity 77.88%, positive predicted value 87.22%, negative predicted value 81.00%, and accuracy was found to be 85.00%. A significantly higher proportion of adenomyosis was observed among women who had infertility (p<0.001), symptoms of dysmenorrhea (p <0.001), dyspareunia (p<0.002), urinary symptoms (p <0.001), and GI symptoms (p<0.001). Conclusion: TVS is a valuable imaging modality for identifying adenomyosis, especially in patients with clinical symptoms. Furthermore, there is a significant association between adenomyosis and various clinical symptoms, including infertility, dysmenorrhea, dyspareunia, urinary symptoms, and gastrointestinal symptoms.

Wenqiang Liu, Li Yan, Ningning Ma et al.

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.

Andres M Bran, Sam Cox, Oliver Schilter et al.

Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their full potential by overcoming steep learning curves. Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related problems. Moreover, these models lack access to external knowledge sources, limiting their usefulness in scientific applications. In this study, we introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design. By integrating 18 expert-designed tools, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent, three organocatalysts, and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Surprisingly, we find that GPT-4 as an evaluator cannot distinguish between clearly wrong GPT-4 completions and Chemcrow's performance. Our work not only aids expert chemists and lowers barriers for non-experts, but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.

Emil Dimitrov, Goar Sanchez-Sanz, James Nelson et al.

Accurate and scalable methods for computational quantum chemistry can accelerate research and development in many fields, ranging from drug discovery to advanced material design. Solving the electronic Schrodinger equation is the core problem of computational chemistry. However, the combinatorial complexity of this problem makes it intractable to find exact solutions, except for very small systems. The idea of quantum computing originated from this computational challenge in simulating quantum-mechanics. We propose an end-to-end quantum chemistry pipeline based on the variational quantum eigensolver (VQE) algorithm and integrated with both HPC-based simulators and a trapped-ion quantum computer. Our platform orchestrates hundreds of simulation jobs on compute resources to efficiently complete a set of ab initio chemistry experiments with a wide range of parameterization. Per- and poly-fluoroalkyl substances (PFAS) are a large family of human-made chemicals that pose a major environmental and health issue globally. Our simulations includes breaking a Carbon-Fluorine bond in trifluoroacetic acid (TFA), a common PFAS chemical. This is a common pathway towards destruction and removal of PFAS. Molecules are modeled on both a quantum simulator and a trapped-ion quantum computer, specifically IonQ Aria. Using basic error mitigation techniques, the 11-qubit TFA model (56 entangling gates) on IonQ Aria yields near-quantitative results with milli-Hartree accuracy. Our novel results show the current state and future projections for quantum computing in solving the electronic structure problem, push the boundaries for the VQE algorithm and quantum computers, and facilitates development of quantum chemistry workflows.

Jayatee Kanwar, Inga Kamp, Peter Woitke et al.

The analysis of the mid-infrared spectra helps understanding the composition of the gas in the inner, dense and warm terrestrial planet forming region of disks around young stars. ALMA has detected hydrocarbons in the outer regions of the planet forming disk and Spitzer detected \ce{C2H2} in the inner regions. JWST- MIRI provides high spectral resolution observations of \ce{C2H2} and a suite of more complex hydrocarbons are now reported. Interpreting the fluxes observed in the spectra is challenging and radiation thermo-chemical codes are needed to properly take into account the disk structure, radiative transfer, chemistry and thermal balance. Various disk physical parameters like the gas-to-dust ratio, dust evolution including radial drift, dust growth and settling can affect the fluxes observed in the mid-IR. Still, thermo-chemical disk models were not always successful in matching all observed molecular emission bands simultaneously. The goal of this project is two-fold. We analyse the warm carbon chemistry in the inner regions of the disk, i.e. within 10 au to find pathways forming \ce{C2H2} potentially missing from the existing chemical networks. Second, we analyse the effect of the new chemistry on the line fluxes of acetylene. We use radiative thermo-chemical disk code {P{\small RO}D{\small I}M{\small O}} to expand the hydrocarbon chemistry that occurs in a typical standard T Tauri disks. We used the UMIST and the KIDA rate databases for collecting reactions for the species. We include a number of three-body and thermal decomposition reactions from STAND2020 network. We included isotopomers for the species that were present in the databases. The chemistry is then analysed in the regions that produce observable features in the mid-infrared spectra. The effect of expanding the hydrocarbon chemistry on the mid-infrared spectra is studied. Acetylene is formed via two ....

Uwe Frank, Jana Dienstbier, Florentin Tischer et al.

The increasing complexity in particle science and technology requires the ability to deal with multidimensional property distributions. We present the theoretical background for multidimensional fractionations by transferring the concepts known from one dimensional to higher dimensional separations. Particles in fluids are separated by acting forces or velocities, which are commonly induces by external fields, e.g., gravitational, centrifugal or electro-magnetic fields. In addition, short-range force fields induced by particle interactions can be employed for fractionation. In this special case, nanoparticle chromatography is a recent example. The framework for handling and characterizing multidimensional separation processes acting on multidimensional particle size distributions is presented. Illustrative examples for technical realizations are given for shape-selective separation in a hydrocyclone and for density-selective separation in a disc separator.

Patrick Reiser, Marlen Neubert, André Eberhard et al.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Mingli Huang, Xiayi Yao, Zhongsheng Tan et al.

Building deep shafts in water-rich granite formations with large fissures has difficulties, such as high-water pressure and high construction risks, and is prone to water inrush and shaft flooding. This paper relies on the No. 1 vertical auxiliary shaft project of Gaoligongshan tunnel and obtains the uneven distribution of water pressure on the outside of the lining in the horizontal direction through on-site monitoring data. In order to explain this phenomenon, based on the statistical parameters of actual fractures in the field and the Monte Carlo method, the DFN built in FLAC3D6.0 is used to generate a discrete fracture network, and a dual medium model, considering the distribution of large fractures, is established. The reason for the uneven distribution of water pressure is obtained through research: the large fissures in the surrounding rock make the hydraulic conductivity of each part of the stone body formed after grouting of the surrounding rock different. This results in different osmotic pressures from the hydrostatic pressure outside the grouting ring to the outside of the lining through the grouting ring. Based on the distribution characteristics of water pressure outside the lining, the safety of the lining under non-uniform pressure is studied. The lining safety factor is defined as the ratio of the lining’s normal service limit state load to the actual load. The normal service limit state load is the load when the RFPA software is used to establish a load-structure model to simulate the load when the lining has obvious cracks under the action of external load; the actual load is the monitoring load. The new method and mine design code method are used to evaluate the lining safety and make a comparative analysis. The results show that the new method can effectively calculate the lining safety factor and has a larger safety reserve.

Huiyong Wang, Hongmei Yu, Ayman AL-Zubi et al.

The synthesis of room temperature phosphorescent carbon dots (RTP-CDs) without any matrix is important in various applications. In particular, RTP-CDs with dual modes of excitation are more interesting. Here, we successfully synthesized matrix-free carbonized polymer dots (CPDs) that can generate green RTP under visible and ultraviolet light dual-mode excitation. Using acrylic acid (AA) and ammonium oxalate as precursors, a simple one-pot hydrothermal method was selected to prepare AA-CPDs. Here, acrylic acid is easy to polymerize under high temperature and high pressure, which makes AA-CPDs form a dense cross-linked internal structure. Ammonium oxalate as a nitrogen source can form amino groups during the reaction, which reacts with a large number of pendant carboxyl groups on the polymer chains to further form a cross-linked structure. The carboxyl and amino groups on the surface of AA-CPDs are connected by intermolecular hydrogen bonds. These hydrogen bonds can provide space protection (isolation of oxygen) around the AA-CPDs phosphor, which can stably excite the triplet state. This self-matrix structure effectively inhibits the non-radiative transition by blocking the intramolecular motion of CPDs. Under the excitation of WLED and 365 nm ultraviolet light, AA-CPDs exhibit the phosphorescence emission at 464 nm and 476 nm, respectively. The naked-eye observation exceeds 5 s and 10 s, respectively, and the average lifetime at 365 nm excitation wavelength is as long as 412.03 ms. In addition, it successfully proved the potential application of AA-CPDs in image anti-counterfeiting.

Junping Hu, Youxing Liu

Exploiting non-noble metals catalysts with excellent performance for hydrogen evolution reaction (HER) has aroused gigantic interest, but still challenges. Herein, we reported the graphene oxide (GO) supported Fe doped Ni(OH) _2 hexagonal nanosheets (GO-Fe,Ni HHNs) for HER firstly. SEM and XRD were carried out to investigate the morphology and crystal structure of the GO-Fe,Ni HHNs systematically. The elements and bonding of the sample were measured via using XPS. In addition, we found that the recrystallization treatment could significantly improve the morphology and crystallinity of the nanosheets. Most important, the GO-Fe,Ni HHNs possessed exceptional catalytic performance and stability in PBS solution. The overpotential (10 mA cm ^−2 ) and Tafel slope were 190 mV and 110 mV dec ^−1 , respectively, significantly better than that of other non-noble metal catalysts reported before.

Cláudia P. Passos, Rita M. Costa, Sónia S. Ferreira et al.

Coffee brews have High Molecular Weight (HMW) compounds with described immunostimulatory activity, namely polysaccharides and melanoidins. Melanoidins are formed during roasting and are modified during brews technological processing. In addition, brews have Low Molecular Weight (LMW) compounds, namely free chlorogenic acids and caffeine, with well-known anti-inflammatory properties. However, this study shows that both espresso and instant coffee brews did not present immunostimulatory neither anti-inflammatory in vitro activities. It is possible that the simultaneous existence of compounds with antagonistic effects can mitigate their individual effects. To test this hypothesis, an ultrafiltration separation process was applied, studying the behavior of coffee brews’ HMW on retention of LMW compounds. Several ultrafiltration sequential cycles were required to separate retentates from LMW compounds, suggesting their retention. This effect was higher in instant coffee, attributed to its initial higher carbohydrate content when compared to espresso. Separation of HMW and LMW compounds boosted their immunostimulatory (6.2–7.8 µM nitrites) and anti-inflammatory (LPS induced nitrite production decrease by 36–31%) in vitro activities, respectively. As coffee anti-inflammatory compounds are expected to be first absorbed during digestion, a potential in vivo fractionation of LMW and HMW compounds can promote health relevant effects after coffee intake.

Kristen L. Reese, Amy Rasley, Julie R. Avila et al.

Abstract We conducted comprehensive (untargeted) metabolic profiling of volatile organic compounds (VOCs) emitted in culture by bacterial taxa Francisella tularensis (F. tularensis) subspecies novicida and Bacillus anthracis (B. anthracis) Sterne, surrogates for potential bacterial bioterrorism agents, as well as selective measurements of VOCs from their fully virulent counterparts, F. tularensis subspecies tularensis strain SCHU S4 and B. anthracis Ames. F. tularensis and B. anthracis were grown in liquid broth for time periods that covered logarithmic growth, stationary, and decline phases. VOCs emitted over the course of the growth phases were collected from the headspace above the cultures using solid phase microextraction (SPME) and were analyzed using gas chromatography-mass spectrometry (GC-MS). We developed criteria for distinguishing VOCs originating from bacteria versus background VOCs (originating from growth media only controls or sampling devices). Analyses of collected VOCs revealed methyl ketones, alcohols, esters, carboxylic acids, and nitrogen- and sulfur-containing compounds that were present in the bacterial cultures and absent (or present at only low abundance) in control samples indicating that these compounds originated from the bacteria. Distinct VOC profiles where observed for F. tularensis when compared with B. anthracis while the observed profiles of each of the two F. tularensis and B. anthracis strains exhibited some similarities. Furthermore, the relative abundance of VOCs was influenced by bacterial growth phase. These data illustrate the potential for VOC profiles to distinguish pathogens at the genus and species-level and to discriminate bacterial growth phases. The determination of VOC profiles lays the groundwork for non-invasive probes of bacterial metabolism and offers prospects for detection of microbe-specific VOC biomarkers from two potential biowarfare agents.