Hasil untuk "Chemical industries"

Priyanka Banerjee, Emanuel Kemmler, Mathias Dunkel et al.

Abstract Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.

T. Vispute, Huiyan Zhang, A. Sanna et al.

E. Beckman

M. Gawande, V. Bonifácio, R. Luque et al.

G. Lofrano, S. Meriç, G. Zengin et al.

Charles M. Cai, Taiying Zhang, R. Kumar et al.

Jinming Fan, Chao Qian, Wilhelm T. S. Huck et al.

Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels - from atoms and functional groups to entire molecules - and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules and realistic chemical environments, we present ChemFlow, a novel hierarchical framework that integrates atomic, functional group, and molecular-level features, facilitating information flow across these levels to predict the behavior of complex chemical mixtures. ChemFlow employs an atomic-level feature fusion module, Chem-embed, to generate context-aware atomic representations influenced by the mixture state and atomic characteristics. Next, bidirectional group-to-molecule and molecule-to-group attention mechanisms enable ChemFlow to capture functional group interactions both within and across molecules in the mixture. By dynamically adjusting representations based on concentration and composition, ChemFlow excels at predicting concentration-dependent properties and significantly outperforms state-of-the-art models in both concentration-sensitive and concentration-independent systems. Extensive experiments demonstrate ChemFlow's superior accuracy and efficiency in modeling complex chemical mixtures.

Ioan-Teodor Trotuș, T. Zimmermann, F. Schüth

Zhanpeng Chen, Weihao Gao, Shunyu Wang et al.

Generating precise 3D molecular geometries is crucial for drug discovery and material science. While prior efforts leverage 1D representations like SELFIES to ensure molecular validity, they fail to fully exploit the rich chemical knowledge entangled within 1D models, leading to a disconnect between 1D syntactic generation and 3D geometric realization. To bridge this gap, we propose MolSculpt, a novel framework that "sculpts" 3D molecular geometries from chemical syntax. MolSculpt is built upon a frozen 1D molecular foundation model and a 3D molecular diffusion model. We introduce a set of learnable queries to extract inherent chemical knowledge from the foundation model, and a trainable projector then injects this cross-modal information into the conditioning space of the diffusion model to guide the 3D geometry generation. In this way, our model deeply integrates 1D latent chemical knowledge into the 3D generation process through end-to-end optimization. Experiments demonstrate that MolSculpt achieves state-of-the-art (SOTA) performance in \textit{de novo} 3D molecule generation and conditional 3D molecule generation, showing superior 3D fidelity and stability on both the GEOM-DRUGS and QM9 datasets. Code is available at https://github.com/SakuraTroyChen/MolSculpt.

Fei Hao, Xin Liu, Jinyao Zhu et al.

BaTiO3 has been investigated nearly several decades due to its excellent ferroelectric and dielectric properties. However, the property evolution of BaTiO3 with a particle size of sub-10 nm is still unclear because of the difficulty during the fabrication and characterization. Here, a series of BaTiO3 nanoparticles with the average particle sizes of 2.8, 4.5 and 8.1 nm are achieved, and their phase structure, ferroelectric and dielectric properties are skillfully characterized. With the particle size decreasing from 8.1 to 2.8 nm, the nanoparticles show a declining non-centrosymmetry, nonlinear response, ferroelectricity and dielectric constant. Nevertheless, the 2.8 nm BaTiO3 nanoparticles still possess observable second harmonic generation, ferroelectric polarization switching behavior and butterfly loop, showing weak but distinguishable ferroelectricity. This research depicts a detailed ferroelectric and dielectric property evolution in sub-10 nm BaTiO3 nanoparticles, which provides theoretical basis and promising candidate for the future ferroelectric applications.

Zhuo Chen, Florian vom Lehn, Heinz Pitsch et al.

The ever-increasing importance of both energy security and sustainability motivates the design of carbon-neutral petroleum replacements from renewable resources. Fuel candidates are conventionally selected from existing databases with limited scope. This work presents a novel artificial intelligence-based fuel design approach, which identifies molecules tailor-made for a particular application by screening millions of candidates. The approach is demonstrated by the design of fuel blending components for spark-ignition engines. A virtual pool consisting of 26.2 million fuel molecules is first developed by considering all possible combinations of predefined structural groups. The practical application potential of these molecules is evaluated based on the joint consideration of various properties estimated by artificial neural network-based quantitative structure–property relationship models. A two-stage design process is performed. In particular, a number of species with novel and complex structures are identified. These are expected to allow for high efficiency and low emissions simultaneously, but have not attracted previous investigation in the literature yet.

Ondřej Jankovský, Petr Lodňánek, Anna-Marie Lauermannová et al.

In response to the global demand for CO2 emissions reduction, Portland cement (PC) replacement with more eco-friendly materials has been focused on in material studies. One of the studied alternatives is magnesium oxychloride cement (MOC), which offers excellent mechanical properties and lower production temperatures. The ecological impact of MOC alone is significant, but if we incorporate waste material as a filler replacement in MOC composites, we can decrease overall emissions even more. In this paper, we focused on the development of an eco-friendly material with a safely incorporated ladle furnace slag (SL). Firstly, the SL was characterized by numerous analytical methods (XRF, XRD, SEM, EDS, STA-MS) to attain knowledge about its elemental and phase composition. In the following step, MOC composite materials with SL used as a silica sand partial replacement were prepared by casting. Such prepared materials were then characterized by XRF, XRD, SEM, EDS, and MIP. Furthermore, their structural and mechanical properties were assessed. Based on the obtained results, an optimized composition of mixtures was used for 3D printing to demonstrate the suitability of this material for this purpose. Finally, X-ray computed micro-tomography imaging was used to study the quality of printed cubes, in particular porosity and the amount of macroscopic defects. This paper presents an innovative approach in which waste SL from steel production can replace silica sand filler in significant quantities, demonstrating that such a designed material is suitable for additive manufacturing.

Subhrajit Mukherjee, Hammad Siddiqi, Payal Maiti et al.

S. Miresmailli, M. Isman

Arif Ullah, Yuxinxin Chen, Pavlo O. Dral

The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials. In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

Yi-Fan Hou, Lina Zhang, Quanhao Zhang et al.

Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, automatic selection of initial data, uncertainty quantification, and convergence monitoring. The versatility of this protocol is shown in our implementation of quasi-classical molecular dynamics for simulating vibrational spectra, conformer search of a key biochemical molecule, and time-resolved mechanism of the Diels-Alder reactions. These investigations took us days instead of weeks of pure quantum chemical calculations on a high-performance computing cluster. The code in MLatom and tutorials are available at https://github.com/dralgroup/mlatom.

Ana Tánori-Lozano, M. Ángeles López-Baca, Adriana Muhlia-Almazán et al.

This study evaluated the effects of clinoptilolite (CTL) and ferulic acid (FA) supplementation on in vitro ruminal fermentation characteristics, gas production, and bacterial abundance. Treatments were arranged in a 2 × 2 factorial design (FA: 0 or 300 ppm; CTL: 0 or 1%) with repeated measures over time (2, 4, 8, 12, 24, 36, 48, and 72 h). Throughout the incubation period, the CTL and FAZ treatments recorded the highest pH values (<i>p</i> ≤ 0.05), maintaining levels closest to neutrality after 72 h. After 48 and 72 h, FA and CTL decreased (<i>p</i> ≤ 0.05) the ammonia concentrations while increasing (<i>p</i> ≤ 0.05) acetate and propionate. The methane, butyrate, and iso-VFA concentrations were unaffected (<i>p</i> > 0.05) by any treatment. FA increased the total gas production throughout the experimental period (<i>p</i> ≤ 0.05). Additionally, FA and CTL significantly reduced the relative abundance of <i>Ruminococcus albus</i> and <i>Streptococcus bovis</i> (<i>p</i> ≤ 0.05), while no significant effects were observed for <i>Selenomonas ruminantium</i> (<i>p</i> > 0.05). These findings suggest that both additives can positively modify the rumen fermentation characteristics and microbial composition, which could significantly contribute to animal nutrition by providing a promising strategy for enhancing rumen fermentation.

M A Omprakas, M Manikandan, S Balakrishnan et al.

Cutting fluid plays an important role in machining operations like milling, drilling, reaming, etc. In production industry such as automobile industries, aerospace industries, etc., soluble cutting fluids are mainly used for machining operations. The maximum service life of the cutting fluid is about 3 to 4 months in that industry, when they mixed with water, the cutting fluid will get dried out or converted into vapour due to heat generation and due to friction between the cutting tool and the components. The repeated use of cutting fluid for machining operations, the chemical and physical property of the cutting fluid will be changed. The proper mixing ratio of cutting fluid can be done with the help of Arduino nano device and valves, sensors. Automatic cutting fluid filling process can be done by proper Arduino coding. In the present study, the pH sensor is used to detect the pH level of cutting fluid and the Solenoid valves are electromagnetic valves, these valves are used to maintain the proper flow of cutting fluid and water mixture ratio, whenever the cutting fluid pH value differs from the fixed value, the pH sensor will indicate in Arduino nano. The mixing of cutting fluid and water is carried out by stirrer mechanism, which is attached at just above the cutting fluid tank. Control valve is used to control the flow of cutting fluid during the machining operation. The electric signal from touch sensor will stops automatically, when the cutting fluid in the tank reaches its maximum limit. In this paper, cutting fluid pH value is maintained automatically by Arduino nano device for CNC Machine in industries is discussed.

Suzane Meriely da Silva Duarte, Allysson Kayron de Carvalho Silva, Katia Regina Assunção Borges et al.

Cervical cancer is caused by a persistent and high-grade infection. It is caused by the Human Papillomavirus (HPV), which, when entering cervical cells, alters their physiology and generates serious lesions. HPV 18 is among those most involved in carcinogenesis in this region, but there are still no drug treatments that cause cure or total remission of lesions caused by HPV. It is known that L-asparaginase is an amidohydrolase, which plays a significant role in the pharmaceutical industry, particularly in the treatment of specific cancers. Due to its antitumor properties, some studies have demonstrated its cytotoxic effect against cervical cancer cells. However, the commercial version of this enzyme has side effects, such as hypersensitivity, allergic reactions, and silent inactivation due to the formation of antibodies. To mitigate these adverse effects, several alternatives have been explored, including the use of L-asparaginase from other microbiological sources, which is the case with the use of the fungus <i>Aspergillus niger</i>, a high producer of L-asparaginase. The study investigated the influence of the type of fermentation, precipitant, purification, characterization, and in vitro cytotoxicity of L-asparaginase. The results revealed that semisolid fermentation produced higher enzymatic activity and protein concentration of <i>A. niger</i>. The characterized enzyme showed excellent stability at pH 9.0, temperature of 50 °C, resistance to surfactants and metallic ions, and an increase in enzymatic activity with the organic solvent ethanol. Furthermore, it exhibited low cytotoxicity in GM and RAW cells and significant cytotoxicity in HeLa cells. These findings indicate that L-asparaginase derived from <i>A. niger</i> may be a promising alternative for pharmaceutical production. Its attributes, including stability, activity, and low toxicity in healthy cells, suggest that this modified enzyme could overcome challenges associated with antitumor therapy.

Alaukik Saxena, Nikita Polin, Navyanth Kusampudi et al.

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multistage machine learning strategy to identify chemically distinct domains in a semi automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real space segmentation via a density based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate like parts by an unsupervised approach based on principle component analysis, or a U-Net based semantic segmentation trained on the former. Following the chemical and geometric analysis, detailed chemical distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped without resorting to the initial voxelization.