Hasil untuk "Chemical industries"

O. Kurmi, Kin Bong Hubert Lam, Jon G Ayres

The term ‘occupational and environmental health’ includes any act of emission of any substance, likely to be hazardous in nature, which is either not originally present or is present in a higher concentration than normal in the natural atmosphere. Most air pollutants are generated from human activities (e.g. energy, transportation, industry, agriculture), but natural events in the living (e.g. methane emissions in wetlands) and non-living environment (e.g. volcanic eruptions) also contribute to atmospheric air pollution, although their relative importance has declined since the Industrial Revolution and the advent of modern fossil fuel-based economies. Pollutants may be classified as (1) primary (emitted directly into the atmosphere) or secondary (formed in the air through chemical reactions with other pollutants and gases); (2) indoor or outdoor; (3) gaseous or particulate.

K. Niinimäki, G. Peters, H. Dahlbo et al.

C. Campanale, C. Massarelli, Ilaria Savino et al.

The distribution and abundance of microplastics into the world are so extensive that many scientists use them as key indicators of the recent and contemporary period defining a new historical epoch: The Plasticene. However, the implications of microplastics are not yet thoroughly understood. There is considerable complexity involved to understand their impact due to different physical–chemical properties that make microplastics multifaceted stressors. If, on the one hand, microplastics carry toxic chemicals in the ecosystems, thus serving as vectors of transport, they are themselves, on the other hand, a cocktail of hazardous chemicals that are added voluntarily during their production as additives to increase polymer properties and prolong their life. To date, there is a considerable lack of knowledge on the major additives of concern that are used in the plastic industry, on their fate once microplastics dispose into the environment, and on their consequent effects on human health when associated with micro and nanoplastics. The present study emphasizes the most toxic and dangerous chemical substances that are contained in all plastic products to describe the effects and implications of these hazardous chemicals on human health, providing a detailed overview of studies that have investigated their abundance on microplastics. In the present work, we conducted a capillary review of the literature on micro and nanoplastic exposure pathways and their potential risk to human health to summarize current knowledge with the intention of better focus future research in this area and fill knowledge gaps.

Kevin Yang, Kyle Swanson, Wengong Jin et al.

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

Priyanka Banerjee, Andreas Eckert, Anna K. Schrey et al.

Abstract Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

G. Crini, E. Lichtfouse

R. Kishor, D. Purchase, G. Saratale et al.

Abstract Textile industry wastewater (TIWW) is considered as one of the worst polluters of our precious water and soil ecologies. It causes carcinogenic, mutagenic, genotoxic, cytotoxic and allergenic threats to living organisms. TIWW contains a variety of persistent coloring pollutants (dyes), formaldehyde, phthalates, phenols, surfactants, perfluorooctanoic acid (PFOA), pentachlorophenol and different heavy metals like lead (Pb), cadmium (Cd), arsenic (As), chromium (Cr), zinc (Zn) and nickel (Ni) etc. TIWW is characterized by high dye content, high pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD), total dissolved solids (TDS), total suspended solids (TSS), total organic carbon (TOC), chlorides and sulphates. Thus, requires adequate treatment before its final discharge into the water bodies to protect public health and environment. The treatment of TIWW is a major challenge as there is no particular economically feasible treatment method capable to adequately treat TIWW. Therefore, there is a need to develop a novel, cost-effective and eco-friendly technology for the effective treatment of TIWW. This review paper emphasizes on the different textile industry processes, wastewater generation, its nature and chemical composition, environmental impacts and health hazards and treatment approaches available for TIWW treatment. It also presents various analytical techniques used to detect and characterize TIWW pollutants and their metabolites, challenges, key issues and future prospectives.

R. Mariscal, P. Maireles-Torres, M. Ojeda et al.

T. Sander, Joël Freyss, M. V. Korff et al.

Sauvik Raha, M. Ahmaruzzaman

Extensive research in nanotechnology has been conducted to investigate new behaviours and properties of materials with nanoscale dimensions. ZnO NPs owing to their distinct physical and chemical properties have gained considerable importance and are hence investigated to a detailed degree for exploitation of these properties. This communication, at the outset, elaborates the various chemical methods of preparation of ZnO NPs, viz., the mechanochemical process, controlled precipitation, sol–gel method, vapour transport method, solvothermal and hydrothermal methods, and methods using emulsion and micro-emulsion environments. The paper further describes the green methods employing the use of plant extracts, in particular, for the synthesis of ZnO NPs. The modifications of ZnO with organic (carboxylic acid, silanes) and inorganic (metal oxides) compounds and polymer matrices have then been described. The multitudinous applications of ZnO NPs across a variety of fields such as the rubber industry, pharmaceutical industry, cosmetics, textile industry, opto-electronics and agriculture have been presented. Elaborative narratives on the photocatalytic and a variety of biomedical applications of ZnO have also been included. The ecotoxic impacts of ZnO NPs have additionally been briefly highlighted. Finally, efforts have been made to examine the current challenges and future scope of the synthetic modes and applications of ZnO NPs.

Wesley M. Cohen, R. Nelson, John P. Walsh

F. Chemat, M. Abert-Vian, A. Fabiano-Tixier et al.

Abstract Green extraction of natural products is based on design of extraction processes which will reduce or eliminate energy consumption and petroleum solvents, while ensuring a safe extract and quality. It is a concept to meet the challenges of the 21st century protecting both our environment and consumers, and in the meantime, enhance competition of academia and industries to be more ecologic, economic and innovative. This review will present definition, principles and current status of green extraction. We will discuss future challenges with applications in the agro-food sectors, cosmetics and perfumery, biofuels and fine chemicals.

V. Chandel, Deblina Biswas, Swarup Roy et al.

Pectin is a heterogeneous hydrocolloid present in the primary cell wall and middle lamella in all dicotyledonous plants, more commonly in the outer fruit coat or peel as compared to the inner matrix. Presently, citrus fruits and apple fruits are the main sources for commercial extraction of pectin, but ongoing research on pectin extraction from alternate fruit sources and fruit wastes from processing industries will be of great help in waste product reduction and enhancing the production of pectin. Pectin shows multifunctional applications including in the food industry, the health and pharmaceutical sector, and in packaging regimes. Pectin is commonly utilized in the food industry as an additive in foods such as jams, jellies, low calorie foods, stabilizing acidified milk products, thickener and emulsifier. Pectin is widely used in the pharmaceutical industry for the preparation of medicines that reduce blood cholesterol level and cure gastrointestinal disorders, as well as in cancer treatment. Pectin also finds use in numerous other industries, such as in the preparation of edible films and coatings, paper substitutes and foams. Due to these varied uses of pectin in different applications, there is a great necessity to explore other non-conventional sources or modify existing sources to obtain pectin with desired quality attributes to some extent by rational modifications of pectin with chemical and enzymatic treatments.

Seogjoo J. Jang, Goun Kim, Young Min Rhee

This perspective provides a succinct history of Fermi's golden rule (FGR), overview of its derivation, assumptions, and representative forms. Major applications of FGR, mostly in the field of chemical physics, are reviewed. These illustrate the broad applicability and success of FGR. Ambiguities and open issues encountered in practical applications of FGR are clarified. Recent advances in generalizations of FGR and computational methods for practical applications are addressed.

Tianle He, Peixuan Xue, Zongtao Yu et al.

Fe-based oxygen carriers (OCs) exhibit significant potential for biomass chemical looping gasification (BCLG) to produce hydrogen. However, variations in OC composition and operating conditions strongly affect BCLG performance. In this study, Fe-based OCs were optimized by integrating experimental results with machine learning (ML) techniques, considering both material composition and operational parameters. Experimental evaluation identified Fe8Al2 as the most effective OC, achieving a hydrogen yield of 22.83 mmol/g biomass. These experimental data were combined with literature datasets to train an XGBoost model, yielding a robust predictive performance (R2 > 0.824). Interpretable ML analyses using Shapley Additive Explanations (SHAP) and partial dependence plots (PDP) revealed that the steam-to-biomass ratio and Fe content were the most influential factors for hydrogen production. This integrated approach demonstrates a viable pathway for OC optimization by supplementing limited datasets with targeted experimental data, thereby advancing hydrogen production from BCLG.

Stefan Gugler, Markus Reiher

Machine learning has emerged as a powerful tool for predicting molecular properties in chemical reaction networks with reduced computational cost. However, accurately predicting energies of transition state (TS) structures remains a challenge due to their distinct electronic characteristics compared to stable intermediates. In this work, we investigate the limitations of structural descriptors in capturing electronic differences between minima and TS structures. We explore $Δ$-machine learning approaches to predict correlation energy corrections using both Hartree-Fock (HF) and density functional theory (DFT) as reference methods. Our results demonstrate that learning the energy difference between DFT and coupled cluster methods outperforms direct learning and HF-based $Δ$-learning. We also assess the effectiveness of combining electronic descriptors with structural ones but find that simple electronic features do not significantly enhance the prediction of TS energies. These findings highlight the need for more sophisticated descriptors or integrated approaches to accurately predict the electronic energies of TS structures within chemical reaction networks.

Murtaza Zohair, Vidushi Sharma, Eduardo A. Soares et al.

Designing optimal formulations is a major challenge in developing electrolytes for the next generation of rechargeable batteries due to the vast combinatorial design space and complex interplay between multiple constituents. Machine learning (ML) offers a powerful tool to uncover underlying chemical design rules and accelerate the process of formulation discovery. In this work, we present an approach to design new formulations that can achieve target performance, using a generalizable chemical foundation model. The chemical foundation model is fine-tuned on an experimental dataset of 13,666 ionic conductivity values curated from the lithium-ion battery literature. The fine-tuned model is used to discover 7 novel high conductivity electrolyte formulations through generative screening, improving the conductivity of LiFSI and LiDFOB based electrolytes by 82% and 172%, respectively. These findings highlight a generalizable workflow that is highly adaptable to the discovery of chemical mixtures with tailored properties to address challenges in energy storage and beyond.

Raphael J. F. Berger, Maria Dimitrova

Instead of computing magneticallly induced (MI) current densities (CD) via the wave function and their quatum mechanical definition one can also use the differential form of the Ampère-Maxwell law to obtain them from spatial derivatives of the induced magnetic field. In magnetic molecular response calculations, the latter can be done by numerical derivativation of the so called ``nucleus-independent chemical shifts'' (NICS) which are avaialable to many standard quantum chemical programs. The resulting numerical MICD data is in contrast to other numerically obtained MICDs computed via the wave function route, virtually divergence-free.

Kai Foerstl, C. Reuter, E. Hartmann et al.

Toshiki Ikejima, Koji Mizukoshi, Yoshimune Nonomura

Tactile perception plays a crucial role in the perception of products and consumer preferences. This perception process is structured in hierarchical layers comprising a sensory layer (soft and smooth) and an affective layer (comfort and luxury). In this study, we attempted to predict the evaluation score of sensory and affective tactile perceptions of materials using a biomimetic multimodal tactile sensor that mimics the active touch behavior of humans and measures physical parameters such as force, vibration, and temperature. We conducted sensory and affective descriptor evaluations on 32 materials, including cosmetics, textiles, and leather. Using the physical parameters obtained by the biomimetic multimodal tactile sensor as explanatory variables, we predicted the scores of the sensory and affective descriptors in 10 regression models. The bagging regressor demonstrated the best performance, achieving a coefficient of determination (<i>R</i>²) of >0.6 for fourteen of nineteen sensory and eight of twelve affective descriptors. The present model exhibited particularly high prediction accuracy for sensory descriptors such as “moist” and “elastic”, and for affective descriptors such as “pleasant” and “like”. These findings suggest a method to support efficient tactile design in product development across various industries by predicting tactile descriptor scores using physical parameters from a biomimetic tactile sensor.