Hasil untuk "Organic chemistry"

N. Ng, S. Brown, A. Archibald et al.

Oxidation of biogenic volatile organic compounds (BVOC) by the nitrate radical (NO3) represents one of the important interactions between anthropogenic emissions related to combustion and natural emissions from the biosphere. This interaction has been recognized for more than 3 decades, during which time a large body of research has emerged from laboratory, field, and modeling studies. NO3-BVOC reactions influence air quality, climate and visibility through regional and global budgets for reactive nitrogen (particularly organic nitrates), ozone, and organic aerosol. Despite its long history of research and the significance of this topic in atmospheric chemistry, a number of important uncertainties remain. These include an incomplete understanding of the rates, mechanisms, and organic aerosol yields for NO3-BVOC reactions, lack of constraints on the role of heterogeneous oxidative processes associated with the NO3 radical, the difficulty of characterizing the spatial distributions of BVOC and NO3 within the poorly mixed nocturnal atmosphere, and the challenge of constructing appropriate boundary layer schemes and non-photochemical mechanisms for use in state-of-the-art chemical transport and chemistry–climate models. This review is the result of a workshop of the same title held at the Georgia Institute of Technology in June 2015. The first half of the review summarizes the current literature on NO3-BVOC chemistry, with a particular focus on recent advances in instrumentation and models, and in organic nitrate and secondary organic aerosol (SOA) formation chemistry. Building on this current understanding, the second half of the review outlines impacts of NO3-BVOC chemistry on air quality and climate, and suggests critical research needs to better constrain this interaction to improve the predictive capabilities of atmospheric models.

Alex N. Byrne, Christopher N. Shingledecker, Edwin A. Bergin et al.

Elemental abundances, which are often depleted with respect to the solar values, are important input parameters for kinetic models of interstellar chemistry. In particular, the amount of carbon relative to oxygen is known to have a strong effect on modeled abundances of many species. While previous studies have focused on comparison of modeled and observed abundances to constrain the C/O ratio, the effects of this parameter on the underlying chemistry have not been well-studied. We investigated the role of the C/O ratio on dark cloud chemistry using the NAUTILUS code and machine learning techniques for molecular representation. We find that modeled abundances are quite sensitive to the C/O ratio, especially for carbon-rich species such as carbon chains and polycyclic aromatic hydrocarbons (PAHs). CO and simple ice-phase species are found to be major carbon reservoirs under both oxygen-poor and oxygen-rich conditions. The appearance of C3H4 isomers as significant carbon reservoirs, even under oxygen-rich conditions, indicates the efficiency of gas-phase C3 formation followed by adsorption and grain-surface hydrogenation. Our model is not able to reproduce the observed, gas-phase C/H ratio of TMC-1 CP at the time of best fit with any C/O ratio between 0.1 and 3, suggesting that the modeled freeze-out of carbon-bearing molecules may be too rapid. Future investigations are needed to understand the reactivity of major carbon reservoirs and their conversion to complex organic molecules.

Ryushi Miyayama, Laura Kay Schaefer, Hiroshi Kobayashi et al.

Meteoroid entry into planetary atmospheres generates bow shocks, resulting in high-temperature gas conditions that drive chemical reactions. In this paper, we perform three-dimensional hydrodynamic simulations of meteoroid entry using the Athena++ code, coupled with chemistry calculations via Cantera to model the non-equilibrium chemistry triggered by atmospheric entry. Our aerodynamical simulations reveal the formation of complex shock structures, including secondary shock waves, which influence the thermodynamic evolution of the gas medium. By tracking thermodynamic parameters along streamlines, we analyze the effects of shock heating and subsequent expansion cooling on chemical reaction pathways. Our results demonstrate that chemical quenching occurs when the cooling timescale surpasses reaction rates, leading to the formation of distinct chemical products that deviate from equilibrium predictions. We show that the efficiency of molecular synthesis depends on the object\textquotesingle s size and velocity, influencing the composition of the post-entry gas mixture. Applying our model to Titan, we demonstrate that organic matter can be synthesized in the present environment of Titan. Also, we find that nitrogen, the dominant atmospheric component, remains stable, while water vapor is efficiently removed, a result inconsistent with equilibrium chemistry assumptions. Moreover, we compare our simulation results with laser experiments and find good agreement in chemical yields. Finally, we also evaluate the impact on Titan\textquotesingle s atmosphere as a whole, showing that meteoroid entry events could have played a significant role in supplying molecules such as HCN during early Titan\textquotesingle s history.

Kaustav Das, Ishita Ghosh, Soumalya Chakraborty et al.

Abstract Altering surface chemistry of functional materials is an attractive route to enable large property enhancements without sacrificing overall structural-order, appealing to diverse fields of application sciences; however, the same remains unexplored for organic crystalline materials. Herein, piezoelectricity in pharmaceutical crystals is reported to show colossal surface charges driven by mechanical fracture — where a collection of dipoles arranged in polar head-to-tail fashion generates opposite surface charges on freshly fractured faces — causing them to actuate large distances over 75 µm in milliseconds. Kelvin probe force microscopy is leveraged to show many-fold surface potential enhancement in fractured surfaces relative to the pristine crystals. Further, complementarity of the surface potentials in a pair of fractured crystal shards and asymptotic decay behaviour with time are observed. Newly formed surfaces of the pharmaceutical crystals show long-lasting charges despite their relatively lower piezo-response confirmed by bulk piezometry. To establish the generality of surface phenomena, statistical analyses (≈50 samples) of post-fracture-attraction behaviour of crystals are performed. Finally, the application of fracture-driven surface charges in industrial processes is achieved by investigating flow-property and tablet-strength of bulk pharmaceutical materials. This multiscale approach unveils the symmetry-dependency of surface charges in fractured materials, and probes the same for utilisation in bulk-property engineering.

Wanshuai Wang, Anqi Zou, Qingtao Yu et al.

<i>Dendrobium</i>, a prominent genus in the <i>Orchidaceae</i> family, has generated significant research attention due to its demonstrated biological potential, particularly its notable anti-inflammatory and antioxidant activities. In this study, two fractions of fermented <i>Dendrobium officinale</i> polysaccharides (FDOPs) were successfully isolated through a multi-stage purification strategy including gradient ethanol precipitation, gel column chromatography, and ion exchange chromatography with <i>Lactobacillus reuteri</i> CCFM863. Structural characterization revealed that both <i>Dendrobium officinale</i> polysaccharide fractions consisted of (1→4)-β-D-Man<i>p</i>, (1→4)-β-D-Glc<i>p</i>, and (1→4)-α-D-Glc<i>p</i> residues. The anti-inflammatory efficacy and keratinocyte-protective potential of FDOPs (FDOP-1A and FDOP-2A) were investigated by using lipopolysaccharide (LPS)-induced RAW264.7 and HaCaT cells models, which showed significant inhibitions on the inflammatory factors of monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-α), nitric oxide (NO), and interleukin-1 beta (IL-1β); recovered levels of filaggrin (FLG), aquaporin 3 (AQP3), transient receptor potential vanilloid 4 (TRPV4), cathelicidin antimicrobial peptide (CAMP)/LL-37, and adiponectin (ADIPOQ); and the reduced protein expression of the TLR4/IκB-α/NF-κB/NLRP3 pathway. Notably, the FDOPs exhibited a remarkable reactive oxygen species (ROS) scavenging capacity, demonstrating superior antioxidant activity. Therefore, FDOPs show dual anti-inflammatory and antioxidant properties, making them suitable as active ingredients for modulating epidermal inflammation and promoting skin barrier repair.

Yunfei Li, Jun Wei, Shaohua Cheng et al.

Spectral detection technology offers non-destructive, in situ, and high-speed capabilities, making it widely applicable for detecting biological and chemical samples and quantifying their concentrations. Water resources, essential to life on Earth, are widely distributed across the planet. The application of spectral technology to underwater environments is useful for wide-area water resource monitoring. Although spectral detection technology is well-established, its underwater application presents challenges, including waterproof housing design, power supply, and data transmission, which limit widespread application of underwater spectral detection. Furthermore, underwater spectral detection necessitates the development of compatible computational methods for sample classification or regression analysis. Focusing on underwater spectral detection, this work involved the construction of a suitable hardware system. A compact spectrometer and LEDs (400 nm–800 nm) were employed as the detection and light source modules, respectively, resulting in a compact system architecture. Extensive tests confirmed that the miniaturized design-maintained system performance. Further, this study addressed the estimation of total phosphorus (TP) concentration in water using spectral data. Samples with varying TP concentrations were prepared and calibrated against standard detection instruments. Subsequently, classification algorithms applied to the acquired spectral data enabled the in situ underwater determination of TP concentration in these samples. This work demonstrates the feasibility of underwater spectral detection for future in situ, high-speed monitoring of aquatic biochemical indicators. In the future, after adding UV LED light source, more water quality parameter information can be obtained.

Chang Liao, Yemin Yu, Yu Mei et al.

In recent years, Large Language Models (LLMs) have achieved significant success in natural language processing (NLP) and various interdisciplinary areas. However, applying LLMs to chemistry is a complex task that requires specialized domain knowledge. This paper provides a thorough exploration of the nuanced methodologies employed in integrating LLMs into the field of chemistry, delving into the complexities and innovations at this interdisciplinary juncture. Specifically, our analysis begins with examining how molecular information is fed into LLMs through various representation and tokenization methods. We then categorize chemical LLMs into three distinct groups based on the domain and modality of their input data, and discuss approaches for integrating these inputs for LLMs. Furthermore, this paper delves into the pretraining objectives with adaptations to chemical LLMs. After that, we explore the diverse applications of LLMs in chemistry, including novel paradigms for their application in chemistry tasks. Finally, we identify promising research directions, including further integration with chemical knowledge, advancements in continual learning, and improvements in model interpretability, paving the way for groundbreaking developments in the field.

Zihan Zhao, Da Ma, Lu Chen et al.

Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require training and tuning for specific tasks. A more generic and efficient solution would be an AI model that could address many tasks and support free-form dialogue in the broad field of chemistry. In its utmost form, such a generalist AI chemist could be referred to as Chemical General Intelligence. Large language models (LLMs) have recently logged tremendous success in the general domain of natural language processing, showing emerging task generalization and free-form dialogue capabilities. However, domain knowledge of chemistry is largely missing when training general-domain LLMs. The lack of such knowledge greatly hinders the performance of generalist LLMs in the field of chemistry. To this end, we develop ChemDFM, a pioneering LLM for chemistry trained on 34B tokens from chemical literature and textbooks, and fine-tuned using 2.7M instructions. As a result, it can understand and reason with chemical knowledge in free-form dialogue. Quantitative evaluations show that ChemDFM significantly surpasses most representative open-source LLMs. It outperforms GPT-4 on a great portion of chemical tasks, despite the substantial size difference. We have open-sourced the inference codes, evaluation datasets, and model weights of ChemDFM on Huggingface (https://huggingface.co/OpenDFM/ChemDFM-v1.0-13B).

Vincent Fan, Yujie Qian, Alex Wang et al.

Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this complex challenge and enable the extraction of reaction data at the document level. OpenChemIE approaches the problem in two steps: extracting relevant information from individual modalities and then integrating the results to obtain a final list of reactions. For the first step, we employ specialized neural models that each address a specific task for chemistry information extraction, such as parsing molecules or reactions from text or figures. We then integrate the information from these modules using chemistry-informed algorithms, allowing for the extraction of fine-grained reaction data from reaction condition and substrate scope investigations. Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69.5%. Additionally, the reaction extraction results of \ours attain an accuracy score of 64.3% when directly compared against the Reaxys chemical database. We provide OpenChemIE freely to the public as an open-source package, as well as through a web interface.

S. Maes, F. De Ceuster, M. Van de Sande et al.

The chemistry of an astrophysical environment is closely coupled to its dynamics, the latter often found to be complex. Hence, to properly model these environments a 3D context is necessary. However, solving chemical kinetics within a 3D hydro simulation is computationally infeasible for a even a modest parameter study. In order to develop a feasible 3D hydro-chemical simulation, the classical chemical approach needs to be replaced by a faster alternative. We present mace, a Machine learning Approach to Chemistry Emulation, as a proof-of-concept work on emulating chemistry in a dynamical environment. Using the context of AGB outflows, we have developed an architecture that combines the use of an autoencoder (to reduce the dimensionality of the chemical network) and a set of latent ordinary differential equations (that are solved to perform the temporal evolution of the reduced features). Training this architecture with an integrated scheme makes it possible to successfully reproduce a full chemical pathway in a dynamical environment. mace outperforms its classical analogue on average by a factor 26. Furthermore, its efficient implementation in PyTorch results in a sub-linear scaling with respect to the number of hydrodynamical simulation particles.

Georgyi Koidan, Nazar Tsyzoryk, Eduard B. Rusanov et al.

Gianluigi Giannelli, Elisa Fasani, Giovanni DalCorso

Szandra Schwarcz, Petra Nyerges, Tímea Ingrid Bíró et al.

The microbiome is capable of modulating the bioavailability of chemotherapy drugs, mainly due to metabolizing these agents. Multiple cytostatic bacterial metabolites were recently identified that have cytostatic effects on cancer cells. In this study, we addressed the question of whether a set of cytostatic bacterial metabolites (cadaverine, indolepropionic acid and indoxylsulfate) can interfere with the cytostatic effects of the chemotherapy agents used in the management of breast cancer (doxorubicin, gemcitabine, irinotecan, methotrexate, rucaparib, 5-fluorouracil and paclitaxel). The chemotherapy drugs were applied in a wide concentration range to which a bacterial metabolite was added in a concentration within its serum reference range, and the effects on cell proliferation were assessed. There was no interference between gemcitabine, irinotecan, methotrexate or rucaparib and the bacterial metabolites. Nevertheless, cadaverine and indolepropionic acid modulated the Hill coefficient of the inhibitory curve of doxorubicin and 5-fluorouracil. Changes to the Hill coefficient implicate alterations to the kinetics of the binding of the chemotherapy agents to their targets. These effects have an unpredictable significance from the clinical or pharmacological perspective. Importantly, indolepropionic acid decreased the IC₅₀ value of paclitaxel, which is a potentially advantageous combination.

Nessreen S. Abdelhamid, Huda Salem AlSalem, Faisal K. Algethami et al.

For the first time, two direct and eco-friendly chromatographic approaches were adapted for the simultaneous estimation of pheniramine maleate (PAM) and its major toxic impurity, 2-benzyl pyridine (BNZ). Method A used reversed-phase ultra-performance liquid chromatography; separation was achieved within 4 min using a C₁₈ column with a developing system of methanol/water (60:40 <i>v</i>/<i>v</i>) with a 0.1 mL/min flow rate. Photodiode array detection was adjusted at 215 nm. The method was linear in the ranges of 5.0–70.0 and 0.05–10.0 µg/mL for PAM and BNZ, correspondingly. Method B used thin-layer chromatography; separation was applied on silica gel TLC F₂₅₄ using ethanol/ethyl acetate/liquid ammonia (8:2:0.1, in volumes) at room temperature, at 265 nm. Linearity was assured at concentration ranges 0.5–8.0 and 0.1–3.0 µg/band for the two components, respectively. Generally, the new UPLC and TLC methods outperform the old ones in terms of quickness, greenness, and sensitivity. Concisely, the greenness features were partially achieved using the Green Analytical Procedure Index (GAPI) and the Analytical Greenness (AGREE) pictograms. In contrast, the usefulness of the novel approaches was assured via the Blue Applicability Grade Index (BAGI) tool.

Taicheng Guo, Kehan Guo, Bozhao Nan et al.

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the field of chemistry remains unclear. In this paper, rather than pursuing state-of-the-art performance, we aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain. We identify three key chemistry-related capabilities including understanding, reasoning and explaining to explore in LLMs and establish a benchmark containing eight chemistry tasks. Our analysis draws on widely recognized datasets facilitating a broad exploration of the capacities of LLMs within the context of practical chemistry. Five LLMs (GPT-4, GPT-3.5, Davinci-003, Llama and Galactica) are evaluated for each chemistry task in zero-shot and few-shot in-context learning settings with carefully selected demonstration examples and specially crafted prompts. Our investigation found that GPT-4 outperformed other models and LLMs exhibit different competitive levels in eight chemistry tasks. In addition to the key findings from the comprehensive benchmark analysis, our work provides insights into the limitation of current LLMs and the impact of in-context learning settings on LLMs' performance across various chemistry tasks. The code and datasets used in this study are available at https://github.com/ChemFoundationModels/ChemLLMBench.

Jing Yang, Zhehong Lu, Xin Zhou et al.

Energetic composite materials (ECMs) are the basic materials of polymer binder explosives and composite solid propellants, which are mainly composed of explosive crystals and binders. During the manufacturing, storage and use of ECMs, the bonding surface is prone to micro/fine cracks or defects caused by external stimuli such as temperature, humidity and impact, affecting the safety and service of ECMs. Therefore, substantial efforts have been devoted to designing suitable self-healing binders aimed at repairing cracks/defects. This review describes the research progress on self-healing binders for ECMs. The structural designs of these strategies to manipulate macro-molecular and/or supramolecular polymers are discussed in detail, and then the implementation of these strategies on ECMs is discussed. However, the reasonable configuration of robust microstructures and effective dynamic exchange are still challenges. Therefore, the prospects for the development of self-healing binders for ECMs are proposed. These critical insights are emphasized to guide the research on developing novel self-healing binders for ECMs in the future.

Honghui Shang, Li Shen, Yi Fan et al.

Quantum computational chemistry (QCC) is the use of quantum computers to solve problems in computational quantum chemistry. We develop a high performance variational quantum eigensolver (VQE) simulator for simulating quantum computational chemistry problems on a new Sunway supercomputer. The major innovations include: (1) a Matrix Product State (MPS) based VQE simulator to reduce the amount of memory needed and increase the simulation efficiency; (2) a combination of the Density Matrix Embedding Theory with the MPS-based VQE simulator to further extend the simulation range; (3) A three-level parallelization scheme to scale up to 20 million cores; (4) Usage of the Julia script language as the main programming language, which both makes the programming easier and enables cutting edge performance as native C or Fortran; (5) Study of real chemistry systems based on the VQE simulator, achieving nearly linearly strong and weak scaling. Our simulation demonstrates the power of VQE for large quantum chemistry systems, thus paves the way for large-scale VQE experiments on near-term quantum computers.

Robert Jandl, Ernst Leitgeb, Michael Englisch

Repeated soil surveys provide opportunities to quantify the effect of long-term environmental change. In recent decades, the topics of forest soil acidification as a consequence of acidic deposition, the enrichment of forest ecosystems with nitrogen, and the loss of carbon due to climate change have been discussed. We used two forest soil surveys that were 20 years apart, in order to establish the direction and magnitude of changes in soil carbon, nitrogen, and soil acidity. Soils have been initially sampled in the late 1980s. The plots were revisited twenty years later. Archived soil samples from the first survey were reanalyzed with the same protocol as the new samples. We found changes in the stocks of soil organic carbon, soil nitrogen, and soil pH. However, the changes were inconsistent. In general, as many sites have gained soil organic carbon, as sites have lost carbon. Most soils have been slightly enriched with nitrogen. The soil pH has not changed significantly. We conclude that changes in the evaluated soil chemical properties are mainly driven by forest management activities and ensuing forest stand dynamics, and atmospheric deposition. We have no convincing evidence that climate change effects have already changed the soil organic carbon stock, irrespective of bedrock type.

Marta Balog, Allison Anderson, Thiago C. Genaro-Mattos et al.

Polypharmacy, or the simultaneous use of multiple drugs to treat a single patient, is a common practice in psychiatry. Unfortunately, data on the health effects of commonly used combinations of medications are very limited. In this study, we therefore investigated the effects and interactions between two commonly prescribed psychotropic medications with sterol inhibiting side effects, trazodone (TRZ), an antidepressant, and aripiprazole (ARI), an antipsychotic. In vitro cell culture experiments revealed that both medications alone disrupted neuronal and astroglial sterol biosynthesis in dose-dependent manners. Furthermore, when ARI and TRZ were combined, exposure resulted in an additive 7-dehydrocholesterol (7-DHC) increase, as well as desmosterol (DES) and cholesterol decreases in both cell types. In adult mice, at baseline, we found that the three investigated sterols showed significant differences in distribution across the eight assessed brain regions. Furthermore, experimental mice treated with ARI or TRZ, or a combination of both medications for 8 days, showed strong sterol disruption across all brain regions. We show ARI or TRZ alone elevated 7-DHC and decreased DES levels in all brain regions, but with regional differences. However, the combined utilization of these two medications for 8 days did not lead to additive changes in sterol disturbances. Based on the complex roles of 7-DHC derived oxysterols, we conclude that individual and potentially simultaneous use of medications with sterol biosynthesis-inhibiting properties might have undesired side effects on the adult brain, with as yet unknown long-term consequences on mental or physical health.

Bulut Tekgül, Heikki Kahila, Petteri Peltonen et al.

In this study, we introduce a novel open-source chemistry model for OpenFOAM to speed-up the reactive computational fluid dynamics (CFD) simulations using finite-rate chemistry. First, a dynamic load balancing model called DLBFoam is introduced to balance the chemistry load during runtime in parallel simulations. In addition, the solution of the cell-based chemistry problem is improved by utilizing an analytical Jacobian using an open-source library called pyJac and an efficient linear algebra library LAPACK. Combination of the aforementioned efforts yields a speed-up factor 200 for a high-fidelity large-eddy simulation spray combustion case compared to the standard OpenFOAM implementation. It is worth noting that the present implementation does not compromise the solution accuracy.