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BACKGROUND: The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. OBJECTIVES: This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. DISCUSSION: Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end pointagnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001, Introduction Chemical hazard and risk assessments aim to estimate toxicological outcomes and evaluate the potential risks that chemicals may pose to human health and the environment. However, studying chemicals one-by-one [...]

Representation plays an important role in helping students understand chemistry learning. This is due to the many chemical concepts that require visualization. Representation is something that still needs to be studied. So this study aims to see the development of representation research in chemistry education. The articles used in this study are articles from 2018 to 2022. This Systematic Literature Review research used Publish or Perish software, then filtered and extracted data according to the criteria for the research objectives to create 30 relevant articles and combined using the Prisma Protocol. The results showed that research related to representation in chemistry learning is still frequently conducted in the last 5 years. This research often discusses related to cognitive ability, which is most associated with chemistry concepts and conceptual understanding. Students and teachers are commonly used samples in research. In addition, stated of matter is most widely used subject in chemistry research. [ABSTRACT FROM AUTHOR]

This document provides an overview of a student laboratory concept aimed at giving students insight into the work of researchers in chemistry. The concept includes various experiments and activities related to the properties and applications of deuterium, a heavy isotope of hydrogen. Students explore the behavior of frozen deuterium and water, investigate the freezing temperature of deuterium, and learn about the benefits of deuterium labeling in drug development. They also explore the use of photokatalysis for targeted deuterium substitution in molecules and conduct their own photokatalytic reactions. The document includes information about the researchers involved in the project and their areas of expertise. The concept is being evaluated through a questionnaire study to assess its impact on students' perceptions of chemical research and researchers. [Extracted from the article]

Coffee. Whether early in the morning or during a late afternoon break, its smell can evoke a sense of relaxation, invigoration, or camaraderie. But too much of a good thing [...]

This review provides a comprehensive overview of the distinguished academic career and scientific accomplishments of Prof. Noritatsu Tsubaki at the University of Toyama. For over 35 years, he has dedicated himself to the research field of one-carbon (C1) chemistry, including catalytic conversion of C1 molecules to valuable chemicals and superclean fuels, innovative catalyst and reactor development, and the design of new catalytic reactions and processes. Organized chronologically, this review highlights Prof. Tsubaki's academic contributions from 1990, when he studied and worked at The University of Tokyo, to his current role as a full professor at the University of Toyama. The academic section of this review is divided into three main parts, focusing on Prof. Tsubaki's pioneering work in C1 chemistry. We believe that this review will serve as a highly valuable reference for colleagues in the fields of C1 chemistry and catalysis, and inspire the development of more original and groundbreaking research. [ABSTRACT FROM AUTHOR]

Soil discrimination is one of the most critical forensic analyses aiming to establish a link between a suspect and a crime scene or victim. Preliminary studies have indicated that ultra-performance liquid chromatography (UPLC) is useful in acquiring organic profiles of soils. However, the resulting chromatograms are often imperfect but consist of fluctuated baseline and overlapped peaks. Hence, this work aims to compare the performances of two derivative algorithms, i.e., Gap-Segment (GS) and Savitzky-Golay (SG) algorithms, in improving the UPLC chromatograms of soils for forensic investigation purposes. A set of 45 chromatograms was prepared by analyzing 15 Malaysian soil samples in triplicate by using a UPLC-photodiode array detector system. The capability of the two algorithms in discriminating the chromatograms from five geographical origins was studied based on predictive capability of K-nearest neighbor algorithm. Results showed that the GS algorithm slightly outperformed the SG algorithm. In conclusion, lower polynomial and differentiation order are preferred for improving the quality of the UPLC chromatograms of soils. The study provides an understanding of the relative merits between Gap-Segment and Savitzky-Golay algorithms in preprocessing UPLC data., Author(s): Yaishnavee A/P Ravi [sup.1] , Nur Ain Najihah Binti Mohd Rosdi [sup.1] , Nadirah Abd Hamid [sup.1] , Hukil Sino [sup.1] , Loong Chuen Lee [sup.1] Author Affiliations: (1) [...]

The article talks about the high role of quantum chemical research methods in such a promising direction as the catalytic oxidation of saturated hydrocarbons, especially in the study of transition states of molecules. Theoretical analysis is based on research in catalysis and computational chemistry. Computational chemistry is an effective tool that allows one to calculate the structure and physical properties of catalysts, as well as the structure and energy of the activated complex. In this review, special attention is paid to identifying a sufficiently accurate and, at the same time, less energy-consuming method for determining the structure of the transition state. The most popular methods of quantum chemical calculations are presented, among which a special role is given to density functional theory (DFT), functionals B3LYP, OLYP, PBE and basis sets 6-31G, LANL2DZ. The use of machine learning by some authors, along with quantum chemical computing, to identify catalysts for activation and functionalization of CH bonds has been mentioned. The advantages and disadvantages of computational chemistry for heterogeneous catalysis are discussed. The results of studies of catalysts predicated on transition metal complexes are summarized. It is underlined that nanocomposites based on transition metals attract the attention of researchers due to their d-orbitals, which are responsible for the reactivity when activating C-H bonds. The review also indicates plans for conducting quantum chemical calculations to study the catalytic processes of selective oxidation of n-alkanes using transition metal nanocomposites as catalysts. This review will be useful for future applications of computational chemistry methods for the development of new catalysts and the study of chemical reaction mechanisms. [ABSTRACT FROM AUTHOR]

Adsorbents based on various substrates-silica and a copolymer of styrene with divinylbenzene-are developed for the determination of amino acids by hydrophilic interaction liquid chromatography-mass spectrometry. The optimal version of the structure of the functional layer in two series of the obtained stationary phases was chosen, which provides the best hydrophilization for each substrate. Retention mechanisms were studied and the conditions for the mass-spectrometric detection, separation, and determination of 16 amino acids were chosen. The applicability of the obtained adsorbents and a method for determining amino acids for the analysis of soil extracts were estimated., Author(s): N. Yu. Chikurova [sup.1] , A. V. Gorbovskaia [sup.1] , A. N. Stavrianidi [sup.1] [sup.2] , E. S. Fedorova [sup.2] , A. O. Shemyakina [sup.1] , A. K. Buryak [...]

In 2011, graduate students Kyle and Liz von Hasseln were using an old Z310 gypsum 3D printer to create architectural models in their home in Los Angeles. Then inspiration struck: [...]

The 2022's Youth Forum on Resources Chemicals and Materials was held on November 12-13, 2022, in Shenyang, Liaoning Province. Panel discussions focus on the cutting-edge researches on "Fine chemicals and advanced alloy materials" and "Utilization of fossil and renewable carbon resources". This perspective summarizes the major directions of scientific research and technical developments aligned in the discussions. Fine chemical industry tends to pursue green and low-carbon products, intelligent product design, and start manufacturing. In recent years, great efforts have been made for transformation of cellulose into advanced electronic as well as life-service biomaterials and to the high-selectivity extraction of bio-base aromatic chemicals from lignin. Concerning high-end alloy materials, regulating deformation mechanism of crystal to construct bimodal microstructure seems highly prospective in harmonizing precipitate hardening effect and plastic deformation capacity. As we know, utilization of fossil carbon resources constitutes the major anthropogenic carbon emissions, and the related innovations thus should be, for possibly a long period, on increasing energy production efficiency and low-carbon cascaded conversion of fossil fuels, especially of coal. [ABSTRACT FROM AUTHOR]

Competition in research has led to the emergence of new regional spaces and interorganizational arrangements, designed to improve their participants' visibility, at the crossroads of regional systems and global exchanges. This article investigates the determinants of the formation of interregional scientific alliances through the in-depth case study of a federation of research laboratories in green chemistry in western France. Working from bibliometric and qualitative data and using a network analysis approach to evidence interurban network failures in this scientific field, we examine the federation's geographical scope. We highlight the decisive role of (1) the spatial organization of the field (structure effect), (2) interpersonal ties between consortium members (network effect) and (3) national scientific guidelines and incentives (policy effect). We propose a novel mixed-method approach to understand interregional alliances and defend its potential to uncover the importance of interregional institutional arrangements in prompting interurban knowledge exchanges. [ABSTRACT FROM AUTHOR]

Abstract A graft copolymer is synthesized by grafting a mixture of 2-(dimethylamino) ethyl acrylate (DMAEA) and 2-acrylamido-2-methyl propane sulfonic acid (AMPS) monomers onto the dextrin, polysaccharide backbone via free radical [...]