Hasil untuk "Chemical industries"

W. Murphy

C. Chan, T. Ko, H. Hiraoka

J. Zeikus, M. Jain, P. Elankovan

B. Bruggen, M. Mänttäri, M. Nyström

Elizabeth L. Bell, W. Finnigan, Scott P. France et al.

K. Paech, P. Webb, G. Kuiper et al.

Jaesang Lee, Shaily Mahendra, P. Alvarez

F. Awaja, M. Gilbert, G. Kelly et al.

H. No, S. Meyers, W. Prinyawiwatkul et al.

Hyohak Song, S. Lee

Shichun Xu, Zhengxia He, R. Long

Shaowen Cao, F. Tao, Yu Tang et al.

Qinghua Zhu, Joseph Sarkis, K. Lai

Seda Keskin, Defne Kayrak-Talay, Ugur Akman et al.

C. Friend, Bingjun Xu

S. Sudarshan, S. Harikrishnan, Govindarajan RathiBhuvaneswari et al.

Environmental contamination brought on by the discharge of wastewater from textile industries is a growing concern on a global scale. Textile industries produce a huge quantity of effluents containing a myriad of chemicals, mostly dyes. The discharge of such effluents into the aquatic environment results in pollution that adversely affects aquatic organisms. Synthetic dyes are complex aromatic chemical structures with carcinogenic and mutagenic properties in addition to high biological oxygen demand (BOD) and chemical oxygen demand (COD). This complex aromatic structure resists degradation by conventional techniques. The bioremediation approach is the biological clean-up of toxic contaminants from industrial effluents. Biological treatment methods produce less or no sludge and are cost-effective, efficient, and eco-friendly. Microorganisms, mostly microalgae and bacteria, and, in some instances, fungi, yeast, and enzymes decolorize textile dye compounds into simple, non-toxic chemical compounds. Following a thorough review of the literature, we are persuaded that microalgae and bacteria might be one of the potential decolorizing agents substituting for most other biological organisms in wastewater treatment. This article presents extensive literature information on textile dyes, their classification, the toxicity of dyes, and the bioremediation of toxic textile industry effluent utilizing microalgae and bacteria. Additionally, it combines data on factors influencing textile dye bioremediation, and a few suggestions for future research are proposed.

Fabrice Ofridam, Mohamad Tarhini, N. Lebaz et al.

Stimuli-responsive materials in general and pH-responsive polymers in particular have gained increasing interest during the last two decades. Their unique properties, which arise from their ability to exhibit sharp and reversible changes in response to environmental pH conditions, have made them suitable for various applications such as drug delivery and specific body-site targeting, sensing and actuation, membrane functionalization, separation techniques, as well as in agriculture and food industry and even chemical industries. In the present review, the focus is on the general characteristics of pH-responsive polymers in terms of their origin, chemical composition, and preparation. Moreover, some of the important and recent applications are reported and discussed.

Yuchen Zhu, Donghai Zhao, Yangyang Zhang et al.

Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We introduce SpaceGFN, a generative framework that elevates chemical space to a programmable computational object: a controllable degree of freedom enabling explicit construction and adaptive traversal of structured molecular universes. SpaceGFN decouples space definition from exploration. Users specify building blocks and reaction rules to construct chemically and synthetically coherent spaces, while a GFlowNet performs efficient, property-biased sampling within them. In Discovery mode, we demonstrate programmable space design through two strategies. A pseudo-natural product space assembles natural product-like architectures. An evolution-inspired (Evo) space recombines endogenous metabolite fragments via enzyme-consistent transformations, introducing an evolutionary prior into chemical generation. This bias yields favorable shifts in predicted metabolic and toxicological profiles while preserving pharmacological diversity, supported by broad docking enrichment across therapeutic targets. In Editing mode, SpaceGFN enables reaction-consistent lead optimization through a curated toolkit of executable synthetic transformations, allowing local, synthesis-aware modification of existing compounds instead of unrestricted graph mutation. Across 96 drug targets, SpaceGFN achieves strong optimization performance while maintaining structural diversity under synthetic constraints. By integrating programmable chemical universe construction with flow-based exploration and reaction-level editing, SpaceGFN establishes a general paradigm for deliberate navigation of therapeutic chemical space.

Ademola Soyemi, Khagendra Baral, Tibor Szilvasi

Chemical potential of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot satisfy a constant chemical potential condition as solute species can migrate to the interface and deplete the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative constant chemical potential molecular dynamics (iCuMD) simulation, which helps simulate targeted molar concentrations of species in solution. iCuMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target concentration (chemical potential). We demonstrate our approach using solid-liquid and liquid-air interfacial systems as case studies. Specifically, we perform classical force field-based MD simulations of NaCl(aq)-air and NaCl(aq)-graphite interfaces and machine learning interatomic potential (MLIP)-based MD simulations of the Na2SO4(aq)-graphene interface. Our results show that the iCuMD approach efficiently achieves the desired bulk ion concentration within two iterations and can also be integrated with MLIP-driven simulations which enable constant potential simulations with DFT-level accuracy. We show that iCuMD offers a robust and simple computational framework for constant chemical potential simulations as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Marco Eckhoff, Markus Reiher

Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathways, yield, and selectivity. However, the underlying quantum chemical energy calculations require vast computational resources, limiting these explorations severely in practice. Machine learning potentials (MLPs) offer a solution to increase computational efficiency, while retaining the accuracy of reliable first-principles data used for their training. Unfortunately, MLPs will be limited in their generalization ability within chemical (reaction) space, if the underlying training data are not representative for a given application. Within the framework of automated reaction network exploration, where new reactants or reagents composed of any elements from the periodic table can be introduced, this lack of generalizability will be the rule rather than the exception. Here, we therefore evaluate the benefits of the lifelong MLP concept in this context. Lifelong MLPs push their adaptability by efficient continual learning of additional data. We propose an improved learning algorithm for lifelong adaptive data selection yielding efficient integration of new data while previous expertise is preserved. In this way, we can reach chemical accuracy in reaction search trials.