Hasil untuk "Chemical technology"

F. Kollmann, W. Cǒté

Yulin Li, Dina Maciel, João Rodrigues et al.

Yulin Li,*,†,‡ Dina Maciel,† Joaõ Rodrigues,*,† Xiangyang Shi,*,†,§ and Helena Tomaś*,† †CQM-Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada 9000-390, Funchal, Portugal ‡The State Key Laboratory of Bioreactor Engineering, Key Laboratory for Ultrafine Materials of Ministry of Education, Engineering Research Centre for Biomedical Materials of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, People’s Republic of China

Zhaohuan Wei, Yaqi Ren, Joshua Sokolowski et al.

School of Physics, University of Electronic Science and Technology of China, Chengdu, China School of Materials and Environmental Engineering, Chengdu Technological University, Chengdu, China Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, New York State Key Laboratory Base of Eco-Chemical Engineering, College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China

J. Albo, M. Álvarez-Guerra, P. Castaño et al.

B. Spigarelli, S. Komar Kawatra

A. Abdolali, Wenshan Guo, H. Ngo et al.

C. Wan, Yebo Li

Pretreatment is a crucial step in the conversion of lignocellulosic biomass to fermentable sugars and biofuels. Compared to thermal/chemical pretreatment, fungal pretreatment reduces the recalcitrance of lignocellulosic biomass by lignin-degrading microorganisms and thus potentially provides an environmentally-friendly and energy-efficient pretreatment technology for biofuel production. This paper provides an overview of the current state of fungal pretreatment by white rot fungi for biofuel production. The specific topics discussed are: 1) enzymes involved in biodegradation during the fungal pretreatment; 2) operating parameters governing performance of the fungal pretreatment; 3) the effect of fungal pretreatment on enzymatic hydrolysis and ethanol production; 4) efforts for improving enzymatic hydrolysis and ethanol production through combinations of fungal pretreatment and physical/chemical pretreatment; 5) the treatment of lignocellulosic biomass with lignin-degrading enzymes isolated from fungal pretreatment, with a comparison to fungal pretreatment; 6) modeling, reactor design, and scale-up of solid state fungal pretreatment; and 7) the limitations and future perspective of this technology.

H. Craighead

��ngela Anglada, A. Urtiaga, I. Ortiz

Qilin Wang, Wei Wei, Y. Gong et al.

Guokai Cui, Jianji Wang, Suojiang Zhang

Manajit Das, Ajnabiul Hoque, Mayank Baranwal et al.

Prediction of complete step-by-step chemical reaction mechanisms (CRMs) remains a major challenge. Whereas the traditional approaches in CRM tasks rely on expert-driven experiments or costly quantum chemical computations, contemporary deep learning (DL) alternatives ignore key intermediates and mechanistic steps and often suffer from hallucinations. We present DeepMech, an interpretable graph-based DL framework employing atom- and bond-level attention, guided by generalized templates of mechanistic operations (TMOps), to generate CRMs. Trained on our curated ReactMech dataset (~30K CRMs with 100K atom-mapped and mass-balanced elementary steps), DeepMech achieves 98.98+/-0.12% accuracy in predicting elementary steps and 95.94+/-0.21% in complete CRM tasks, besides maintaining high fidelity even in out-of-distribution scenarios as well as in predicting side and/or byproducts. Extension to multistep CRMs relevant to prebiotic chemistry, demonstrates the ability of DeepMech in effectively reconstructing 2 pathways from simple primordial substrates to complex biomolecules such as serine and aldopentose. Attention analysis identifies reactive atoms/bonds in line with chemical intuition, rendering our model interpretable and suitable for reaction design.

Luis Itza Vazquez-Salazar, Markus Meuwly

Machine learning (ML) has become a standard tool for the exploration of chemical space. Much of the performance of such models depends on the chosen database for a given task. Here, this aspect is investigated for "chemical tasks" including the prediction of hybridization, oxidation, substituent effects, and aromaticity, starting from an initial "restricted" database (iRD). Choosing molecules for augmenting this iRD, including increasing numbers of conformations generated at different temperatures, and retraining the models can improve predictions of the models on the selected "tasks". Addition of a small percentage of conformers (1 % ) obtained at 300 K improves the performance in almost all cases. On the other hand, and in line with previous studies, redundancy and highly deformed structures in the augmentation set compromise prediction quality. Energy and bond distributions were evaluated by means of Kullback-Leibler ($D_{\rm KL}$) and Jensen-Shannon ($D_{\rm JS}$) divergence and Wasserstein distance ($W_{1}$). The findings of this work provide a baseline for the rational augmentation of chemical databases or the creation of synthetic databases.

Kaliyamoorthy Dass, Nagamuthu Prakash, Pitchaimuthu Mariappan

Mosquitoes transmit a number of serious diseases, including brain fever, chikungunya, dengue fever, filariasis, hemorrhagic fever, Japanese encephalitis, malaria, dengue, and Zika. Whereas the synthetic chemical insecticides are commonly used for mosquito control, and it caused physiological resistance to mosquito species and significant effects on humans and the environment. As a result, plant-based insecticides have emerged as an attractive one. Recent research has revealed that green-synthesized silver nanoparticles derived from plant extracts have significant larvicidal properties. In the scenario we focused on application of nanotechnology. Nanotechnology is an emerging field of science and technology, it has incredible potential in pest management, medicine, and other fields. In this regards current investigation focused on the larvicidal and pupicidal effect of green-synthesized silver nanoparticles using C. aromaticus and W. tinctoria on second, third, and fourth-instar and pupa of Aedes aegypti. The green synthesized silver nanoparticles characterized by UV-Vis spectroscopy, FTIR, XRD, and SEM. The 24-hour LC50 and LC90 values obtained from C. aromatics and W. tinctoria synthesized silver nanoparticles against Ae. aegypti second, third, and fourth instars and pupae. The results showed that green synthesized silver nanoparticles from C. aromaticus are more effective than W. tinctoria nanoparticles used.

Joseph M. Cavanagh, Kunyang Sun, Andrew Gritsevskiy et al.

We show that large language model (LLMs) can be transformed via supervised fine-tuning (SFT) of engineered prompts into SmileyLlama for exploring the chemical space of drug molecules. We benchmark SmileyLlama against pre-trained LLMs and chemical language models (CLM) trained from scratch for generating valid and novel drug-like molecules, and use direct preference optimization (DPO) to both improve SmileyLlama's adherence to a prompt and as part of the iMiner reinforcement learning framework to predict molecules with optimized 3D conformations and high binding affinity to drug targets. By training an LLM to speak directly as a CLM, while retaining most of its natural language capabilities, we show that we can reliably generate molecules with user-specified properties rather than acting only as a chatbot with knowledge of chemistry or as a virtual assistant. While SmileyLlama is geared toward drug discovery, the SFT/DPO/LLM framework can be extended to other chemical, biological, and materials applications.

Yuqing Huang, Rongyang Zhang, Xuesong He et al.

There is a growing interest in the role that LLMs play in chemistry which lead to an increased focus on the development of LLMs benchmarks tailored to chemical domains to assess the performance of LLMs across a spectrum of chemical tasks varying in type and complexity. However, existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals. To this end, we propose \textbf{\textit{ChemEval}}, which provides a comprehensive assessment of the capabilities of LLMs across a wide range of chemical domain tasks. Specifically, ChemEval identified 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks which are informed by open-source data and the data meticulously crafted by chemical experts, ensuring that the tasks have practical value and can effectively evaluate the capabilities of LLMs. In the experiment, we evaluate 12 mainstream LLMs on ChemEval under zero-shot and few-shot learning contexts, which included carefully selected demonstration examples and carefully designed prompts. The results show that while general LLMs like GPT-4 and Claude-3.5 excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge. Conversely, specialized LLMs exhibit enhanced chemical competencies, albeit with reduced literary comprehension. This suggests that LLMs have significant potential for enhancement when tackling sophisticated tasks in the field of chemistry. We believe our work will facilitate the exploration of their potential to drive progress in chemistry. Our benchmark and analysis will be available at {\color{blue} \url{https://github.com/USTC-StarTeam/ChemEval}}.

ZHU Xiuqing, SONG Yihan, GUO Ruqi, ZHU Ying, HUANG Yuyang, LIU Linlin

Soy protein, as one of the major plant proteins, has good functional properties such as solubility, emulsification and gelation properties. Freezing, as one of the effective ways to extend the storage period of products, is widely used in the food and medicine industries. However, freezing can cause changes in the secondary and tertiary structures of soy protein and further affect the functional properties of soy protein such as emulsification and gelation properties, limiting the application of soy protein in frozen foods. At present, understanding the effect of freezing on soy protein and improving its freezing stability has become a research focus, and several methods such as ultrasound, glycosylation and enzymatic crosslinking have been adopted to improve the freezing stability of soy protein. In order to gain a better understanding of the effect of freezing on functional properties of soy protein, this article focuses on the effect of freezing on the emulsification and gelation properties and structure of soy protein, and it summarizes and analyzes the methods used to improve the freezing stability of soy protein products and their mechanisms in order to provide a theoretical basis for the application of soy protein in frozen foods and the improvement of food quality.

D. Marinova, F. Ribarova, M. Atanassova

I. Kozhevnikov

Chengwei Zhang, Yushuang Zhai, Ziyang Gong et al.

Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO-SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials.