Hasil untuk "Chemical technology"

T. Sakakura, Kazufumi Kohno

S. Esplugas, D. Bila, Luiz Gustavo T Krause et al.

C. P. Léon, A. Frías-Ferrer, J. González-garcía et al.

M. Momirlan, T. Veziroǧlu

T. Ren, M. Patel, K. Blok

M. Gealt, M. Alexander

J. Gardner, P. Bartlett

D. Dix, K. Houck, Matt T. Martin et al.

H. Becker, C. Gärtner

R. Tice, C. Austin, R. Kavlock et al.

Background: In 2008, the National Institute of Environmental Health Sciences/National Toxicology Program, the U.S. Environmental Protection Agency’s National Center for Computational Toxicology, and the National Human Genome Research Institute/National Institutes of Health Chemical Genomics Center entered into an agreement on “high throughput screening, toxicity pathway profiling, and biological interpretation of findings.” In 2010, the U.S. Food and Drug Administration (FDA) joined the collaboration, known informally as Tox21. Objectives: The Tox21 partners agreed to develop a vision and devise an implementation strategy to shift the assessment of chemical hazards away from traditional experimental animal toxicology studies to one based on target-specific, mechanism-based, biological observations largely obtained using in vitro assays. Discussion: Here we outline the efforts of the Tox21 partners up to the time the FDA joined the collaboration, describe the approaches taken to develop the science and technologies that are currently being used, assess the current status, and identify problems that could impede further progress as well as suggest approaches to address those problems. Conclusion: Tox21 faces some very difficult issues. However, we are making progress in integrating data from diverse technologies and end points into what is effectively a systems-biology approach to toxicology. This can be accomplished only when comprehensive knowledge is obtained with broad coverage of chemical and biological/toxicological space. The efforts thus far reflect the initial stage of an exceedingly complicated program, one that will likely take decades to fully achieve its goals. However, even at this stage, the information obtained has attracted the attention of the international scientific community, and we believe these efforts foretell the future of toxicology.

Romaric Gérardy, D. Debecker, J. Estager et al.

The ever increasing industrial production of commodity and specialty chemicals inexorably depletes the finite primary fossil resources available on Earth. The forecast of population growth over the next 3 decades is a very strong incentive for the identification of alternative primary resources other than petro-based ones. In contrast with fossil resources, renewable biomass is a virtually inexhaustible reservoir of chemical building blocks. Shifting the current industrial paradigm from almost exclusively petro-based resources to alternative bio-based raw materials requires more than vibrant political messages; it requires a profound revision of the concepts and technologies on which industrial chemical processes rely. Only a small fraction of molecules extracted from biomass bears significant chemical and commercial potentials to be considered as ubiquitous chemical platforms upon which a new, bio-based industry can thrive. Owing to its inherent assets in terms of unique process experience, scalability, and reduced environmental footprint, flow chemistry arguably has a major role to play in this context. This review covers a selection of C2 to C6 bio-based chemical platforms with existing commercial markets including polyols (ethylene glycol, 1,2-propanediol, 1,3-propanediol, glycerol, 1,4-butanediol, xylitol, and sorbitol), furanoids (furfural and 5-hydroxymethylfurfural) and carboxylic acids (lactic acid, succinic acid, fumaric acid, malic acid, itaconic acid, and levulinic acid). The aim of this review is to illustrate the various aspects of upgrading bio-based platform molecules toward commodity or specialty chemicals using new process concepts that fall under the umbrella of continuous flow technology and that could change the future perspectives of biorefineries.

Ana Dobrinčić, S. Balbino, Z. Zorić et al.

Over the years, brown algae bioactive polysaccharides laminarin, alginate and fucoidan have been isolated and used in functional foods, cosmeceutical and pharmaceutical industries. The extraction process of these polysaccharides includes several complex and time-consuming steps and the correct adjustment of extraction parameters (e.g., time, temperature, power, pressure, solvent and sample to solvent ratio) greatly influences the yield, physical, chemical and biochemical properties as well as their biological activities. This review includes the most recent conventional procedures for brown algae polysaccharides extraction along with advanced extraction techniques (microwave-assisted extraction, ultrasound assisted extraction, pressurized liquid extraction and enzymes assisted extraction) which can effectively improve extraction process. The influence of these extraction techniques and their individual parameters on yield, chemical structure and biological activities from the most current literature is discussed, along with their potential for commercial applications as bioactive compounds and drug delivery systems.

R. Mahinroosta, Lalantha Senevirathna

Contamination of soils with poly- and perfluoroalkyl substances (PFAS) has become a challenging issue due to the adverse effects of these substances on both the environment and public health. PFAS have strong chemical structures and their bonding with soil makes them challenging to eliminate from soil environments. Traditional methods of soil remediation have not been successful in their reduction or removal from the environment. This paper provides a comprehensive evaluation of existing and emerging technologies for remediating PFAS contaminated soils with guidance on which approach to use in different contexts. The functions of all remediation technologies, their suitability, limitations, and the scale applied from laboratory to the field are presented as a baseline for understanding the research need for treatment in soil environments. To date, the immobilization method has been a significant part of the remediation solution for PFAS contaminated soils, although its long-term efficiency still needs further investigation. Soil washing and thermal treatment techniques have been tested at the field scale, but they are expensive and energy-intensive due to the use of a large volume of washing solvent and the high melting point of PFAS, respectively; both methods need a large initial investment for their installation. Other remediation technologies, such as chemical oxidation, ball milling, and electron beams, have been progressed in the laboratory. However, additional research is needed to make them feasible, cost-effective and applicable in the field.

Hangjuan Ren, P. Koshy, Wen-Fan Chen et al.

D. Geldart

Jack Jon Hinsch, Kazushi Fujimoto

Proton exchange membrane fuel cells (PEMFCs) are a promising clean energy technology, offering high efficiency and near-zero operational emissions for stationery and automotive applications. However, their widespread adoption remains limited by insufficient durability, driven by the degradation of the catalyst layer and proton exchange membrane under realistic operating conditions. While the macroscopic consequences of degradation are well established experimentally, the atomistic and molecular mechanisms that initiate and propagate failure remain incompletely understood. This review synthesizes recent advances in computational modelling, spanning density functional theory, molecular dynamics, and emerging machine learning potentials, to examine how chemical, mechanical, electrochemical, and contamination driven degradation mechanisms operate across multiple length and time scales. Key topics include radical-induced membrane degradation, platinum dissolution and carbon support corrosion, mechanical fatigue under electrical and hygrothermal cycling, and the impact of ionic and gaseous contaminants. A central finding is that these degradation pathways are not independent, but form strongly coupled feedback loops that no existing computational framework has been designed to capture this coupling simultaneously. Future directions are proposed, with emphasis on multiscale modelling frameworks and the application of machine learning interatomic potentials to the electrified interface.

Roman V. Li, Oleg A. Igoshin, Eugine B. Krissinel et al.

Reaction-diffusion processes play an important role in a variety of physical, chemical, and biological systems. Conventionally, the kinetics of these processes are described by the law of mass action. However, there are various cases where these equations are insufficient. A fundamental challenge lies in accurately accounting for the microscopic correlations that inevitably arise in bimolecular reactions. While approaches to describe microscopic correlations in many specific cases exist, no general theory for multistage reactions has been established. In this article, we apply the quantum field theory approach to derive kinetic equations for general multistage reactive systems termed CMET (complete modified encounter theory). CMET can be formulated as a set of coupled partial differential equations that can be easily integrated numerically, thereby serving as a versatile tool for investigating reaction-diffusion processes. Across multiple case studies, we demonstrated that CMET reproduces the kinetics predicted by many other theories within their respective scopes of applicability.

A. Burggraaf, L. Cot

Hun Kim, Jaewoo So

In a multi-cell network, interference management between adjacent cells is a key factor that determines the performance of the entire cellular network. In particular, in order to control inter-cell interference while providing a high data rate to users, it is very important for the base station (BS) of each cell to appropriately control the transmit power in the downlink. However, as the number of cells increases, controlling the downlink transmit power at the BS becomes increasingly difficult. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based transmit power control scheme to maximize the sum rate in multi-cell networks. In particular, the proposed scheme incorporates a long short-term memory (LSTM) architecture into the MADRL scheme to retain state information across time slots and to use that information for subsequent action decisions, thereby improving the sum rate performance. In the proposed scheme, the agent of each BS uses only its local channel state information; consequently, it does not need to receive signal messages from adjacent agents. The simulation results show that the proposed scheme outperforms the existing MADRL scheme by reducing the amount of signal messages exchanged between links and improving the sum rate.

Nannan Wang, Siqi Huang, Xiangpeng Liu et al.

To address the challenges posed by complex backgrounds and the low occurrence in photovoltaic cell images captured by industrial sensors, we propose a novel defect detection method: MRA-YOLOv8. First, a multi-branch coordinate attention network (MBCANet) is introduced into the backbone. The coordinate attention network (CANet) is incorporated to mitigate the noise impact of background information on the detection task, and multiple branches are employed to enhance the model’s feature extraction capability. Second, we integrate a multi-path feature extraction module, ResBlock, into the neck. This module provides finer-grained multi-scale features, improving feature extraction from complex backgrounds and enhancing the model’s robustness. Finally, we implement alpha-minimum point distance-based IoU (AMPDIoU) to the head. This loss function enhances the accuracy and robustness of small object detection by integrating minimum point distance-based IoU (MPDIoU) and Alpha-IoU methods. The results demonstrate that MRA-YOLOv8 outperforms other mainstream methods in detection performance. On the photovoltaic electroluminescence anomaly detection (PVEL-AD) dataset, the proposed method achieves a <i>mAP</i>₅₀ of 91.7%, representing an improvement of 3.1% over YOLOv8 and 16.1% over detection transformer (DETR). On the SPDI dataset, our method achieves a <i>mAP</i>₅₀ of 69.3%, showing a 2.1% improvement over YOLOv8 and a 6.6% improvement over DETR. The proposed MRA-YOLOv8 also exhibits great deployment potential. It can be effectively integrated with drone-based inspection systems, allowing for efficient and accurate PV plant inspections. Moreover, to tackle the issue of data imbalance, we propose generating synthetic defect data via generative adversarial networks (GANs), which can supplement the limited defect samples and improve the model’s generalization ability.