Hasil untuk "Hazardous substances and their disposal"

Jibo Dou, Yichao Xu, Yunfei He et al.

Fe-doped carbon matrices have garnered significant attention as peroxymonosulfate (PMS) activators for the removal of refractory organic pollutants. However, the role of Fe dopant in PMS activation remains unclear, especially concerning the relative contributions and reaction pathways of high-valent iron oxidations. In this study, a series of Fe-doped carbon nitride (CN) catalysts with varying iron contents was synthesized and utilized for the catalytic degradation of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Results showed that Fe doping significantly enhanced PMS activation, with the highest TCDD degradation rate achieved by Fe-CN-3, exhibiting a 4.6-fold improvement. Quenching experiment revealed that increasing Fe content shifted the degradation mechanism from a radical-dominated process (76.1 %) to a FeV=O-driven nonradical pathway (55.8 %); consequently, the Fe-CN-3/PMS system exhibited significant resistance to common environmental interferents. Theoretical calculations further confirmed that the Fe(III)-PMS interaction weakened both the O2-O3 and O3-H bonds, thereby enabling their thermodynamically feasible cleavage and the subsequent generation of FeV=O via a coupled electron-proton transfer pathway, which ultimately enhanced the electrophilic σ-attack on TCDD. These findings highlight the criticality of the metal dopant's local coordination environment in steering persulfate activation pathways, guiding the rational design of catalysts for selective oxidant generation.

Brian E. Perron, Dragan Stoll, Bryan G. Victor et al.

Background: Recent studies have demonstrated that large language models (LLMs) can perform binary classification tasks on child welfare narratives, detecting the presence or absence of constructs such as substance-related problems, domestic violence, and firearms involvement. Whether smaller, locally deployable models can move beyond binary detection to classify specific substance types from these narratives remains untested. Objective: To validate a locally hosted LLM classifier for identifying specific substance types aligned with DSM-5 categories in child welfare investigation narratives. Methods: A locally hosted 20-billion-parameter LLM classified child maltreatment investigation narratives from a Midwestern U.S. state. Records previously identified as containing substance-related problems were passed to a second classification stage targeting seven DSM-5 substance categories. Expert human review of 900 stratified cases assessed classification precision, recall, and inter-method reliability (Cohen's kappa). Test-retest stability was evaluated using approximately 15,000 independently classified records. Results: Five substance categories achieved almost perfect inter-method agreement (kappa = 0.94-1.00): alcohol, cannabis, opioid, stimulant, and sedative/hypnotic/anxiolytic. Classification precision ranged from 92% to 100% for these categories. Two low-prevalence categories (hallucinogen, inhalant) performed poorly. Test-retest agreement ranged from 92.1% to 99.1% across the seven categories. Conclusions: A small, locally hosted LLM can reliably classify substance types from child welfare administrative text, extending prior work on binary classification to multi-label substance identification.

Кожевников, И.А., Торкунов, М.К., Чмутин, И.А. et al.

Повсеместное распространение источников сверхвысокочастотного (СВЧ) излучения создает потенциальную угрозу для биологических объектов и работы чувствительной электроники. В связи с этим актуальной задачей является разработка эффективных средств защиты. В данной работе были получены тонкие пленки из поливинилбутираля (ПВБ), наполненные одностенными (ОУНТ) и многостенными углеродными нанотрубками (МУНТ), восстановленным оксидом графена (ВОГ) и различными видами сажи. Была изучена морфология поверхности пленок с помощью сканирующего электронного микроскопа, что позволило выявить общие закономерности распределения углеродных наноматериалов в полимерной матрице. Также были определены коэффициенты поглощения, пропускания и отражения электромагнитных волн в СВЧ диапазоне. Установлено, что пленки с добавлением ОУНТ имеют высокие значения коэффициентов отражения волн и являются отражающими экранами. Эта работа показала перспективность дальнейших исследований гибких поглощающих покрытий на основе полимерных композитных материалов.

Yao Cheng, Zhong Lv, Erika Schreder et al.

Quaternary ammonium compounds (QACs) are used as antimicrobials, preservatives, and antistatic agents in cleaning, disinfecting and personal care products, and textiles. High levels of QACs have been found in indoor dust in residential homes; however, there is limited information on QAC exposure in non-residential environments serving sensitive populations, such as childcare centers. In this study, we investigated the occurrence of QACs, including benzylalkyldimethyl ammonium compounds (BACs), dialkyldimethylammonium compounds (DADMACs), and alkyltrimethylammonium compounds (ATMACs), in dust from childcare centers and estimated daily intake of QACs by toddlers in chidlcare via dust ingestion. Nineteen QACs were detected in dust with a median total QAC concentration (ΣQAC) of 150 μg/g. BACs were the most abundant QAC group found at concentrations ranging from 2.67 to 1370 μg/g (median 90.4 μg/g) and constituted 64 % of the ΣQAC concentrations. The QAC levels in dust from childcare centers were significantly higher than concentrations previosly reported in homes. The EDIs for BACs, DADMACs, and ATMACs via dust ingestion calculated based on the 95th percentile concentrations in childcare dust were up to 30 times higher than those for toddlers in residential homes. These findings demonstrate high QAC exposure in childcares, posing significant early-life exposure for toddlers.

Довгань, С.В., Арефьева, О.Д., Ковехова, А.В. et al.

В работе исследована сорбция фенола из водных растворов кремний- и углеродсодержащими материалами, полученными в различных условиях из рисовой шелухи и соломы. Показано, что сорбционная активность по фенолу для образов, полученных пиролизом рисовой шелухи в промышленных условиях, составила 39%. Кремний- и углеродсодержащие материалы, синтезированные окислительным обжигом в лабораторных условиях, не поглощают фенол. Продукты пиролиза имеют сложное строение и находятся в аморфном и аморфно-кристаллическом состоянии. Кинетические исследования показали, что сорбция фенолалимитируется двумя стадиями – с одной стороны внутренней и внешней диффузией, с другой – химическим взаимодействием с поверхностными активными группами образцов. Термодинамические параметры указывают на то, что процесс является самопроизвольным (ΔG298 < 0) и экзотермическим (ΔH < 0), подвижность молекулы фенола уменьшается при адсорбции (ΔS < 0). Обработка изотерм адсорбции фенола по моделям Фрейндлиха, Дубинина-Астахова, Ленгмюра и БЭТ показала, что сорбция определяется нековалентными взаимодействиями адсорбат-адсорбент. Установлено, что при увеличении pH поглотительная способность снижается, что обусловлено формой нахождения фенола (в виде молекулы или фенолят-иона). Низкая степень десорбции фенола дистиллированной водой и раствором гидроксида натрия подтверждает сильное взаимодействие фенола с поверхностью материалов, которое зависит от времени контакта и среды раствора.

Гусева, Е.В., Фесик, Е.В.

В обзоре проведен всесторонний анализ современных данных, представленных в различных литературных источниках, касательно процессов выделения, концентрирования и разделения благородных металлов. Подчеркнута значимость цепочки этапов, включающей прогнозирование добычи, переработку и ценообразование с последующим сбытом. Рассмотрены различные типы химических соединений и методы, обеспечивающие наиболее эффективное выделение, концентрирование и разделение благородных металлов из сложных смесей. Проанализированы преимущества и недостатки таких методов, как осаждение, SX (Solvent Extraction), IX (Ion Exchange) и MRT™ (Molecular Recognition Technology). Особое внимание уделено системам молекулярного распознавания (MRT™), которые позволяют соблюдать принципы «зеленой химии» и «зеленой инженерии». На основе проведенных авторами статьи исследований представлены результаты применения соединений родия и платины с O-, N-, P-содержащими краун-эфирами и каликс[4]резорцинами в неводных средах. Оценены выходы твердых целевых продуктов, что может служить основой для разработки эффективных технологий выделения, концентрирования и разделения благородных металлов.

Christian Adi Pratama, Avnish Nitin Mistry, Saowaluk Krainara et al.

Polylactic acid (PLA) ranks among the most extensively produced bioplastics, raising waste management concerns globally. This study explored dual pretreatment strategies, specifically photolysis by Ultraviolet-C (UVC) and hydrolysis by subcritical water extraction (SWE), for PLA waste treatment. UVC irradiation reduced the mechanical strength and viscosity average molecular weight (Mv) of PLA, but the effects were more significant in thin PLA films than in thick PLA beverage cups. Thus, the PLA cups were selected for SWE treatment, which reduced their Mv from 190,000 to 5300. The pulverized SWE-treated PLA was later used for inducing protease and esterase in PLA-degrading bacterial consortium EAc. This active inoculum was applied as a bioaugmentation agent in a food composter for degrading UVC-treated PLA cups with food waste. PLA weight loss in the bioaugmented food composter (42 %) after 56 days was greater than the naturally attenuated composter (1 %). The residual PLA in the final bioaugmented compost had a relatively smaller size and lower Mv with prominent surface erosion. In addition, the 16S rRNA gene amplicon sequencing revealed that the bioaugmentation promoted bacterial diversity and community interactions. Conclusively, PLA waste can be treated by UVC irradiation followed by composting with food waste using an active EAc inoculum.

Phong K. Thai, Jeffrey T. McDonough, Trent A. Key et al.

Ameneh Marzban

Economic inequality is one of the most significant social and health challenges faced by communities around the world. Its direct and indirect impact on social health is a subject that has garnered considerable attention (1). With the widening economic gap between individuals and social groups, access to healthcare and social welfare services for low-income populations has become significantly restricted, leading to negative effects on quality of life and social cohesion (2). This letter explores the connection between economic inequality and social health and proposes solutions to bridge this gap.

Hubert Dirven, Aleksandra Bogusz, Hans Bouwmeester et al.

A recent study has suggested that plastics may contain more than 16,000 chemicals, including additives, processing aids, starting substances, intermediates and Non-Intentionally Added Substances. Plastic chemicals are released throughout the plastic life cycle, from production, use, disposal and recycling. Most of these chemicals have not been studied for potential hazardous properties for humans and in the environment. To refine the risk assessment of these leachable chemicals, additional hazard data are needed. The PlasticLeach project within the EU co-funded Partnership for the Assessment of Risks from Chemicals (PARC) aims to address this data gap by screening several plastic products in daily use. Leachates will be prepared from a number of these plastic items, and these chemical mixtures will be further tested using several test guideline compliant assays and New Approach Methodologies covering both human health and environmental endpoints. The most toxic leachates will be characterized using a non-targeted analysis pipeline to identify chemicals in the leachate. When single chemicals of concern are identified, these will be further tested to determine hazardous properties and identify the respective potency factors to better understand their specific hazard profiles. A tiered approach for hazard testing will be followed. The experimental work will be complemented by in silico toxicological profiling, using publicly available toxicity databases and tools, including Artificial Intelligence tools that cover both human and environmental endpoints. A comprehensive array of endpoints, including cytotoxicity, endocrine disruption, genotoxicity, immunotoxicity, reproductive toxicity and effects related to ecotoxicity will be evaluated. In this paper, we outline the plastic products to be tested and the battery of assays that will be used to identify hazards relevant to both human health and the environment. Data generated from in silico, in vitro, and in vivo approaches will be reported using standardized formats, stored within a centralized repository, and harmonized to adhere to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable). This integrated strategy will not only advance our understanding of the risks associated with plastic-derived chemicals but will also provide critical support for regulatory decision-making and facilitate the development of safer, and more ecofriendly plastic materials in the future.

Ran Elgedawy, Sanjay Das, Ethan Seefried et al.

Operational safety at mission-critical work sites is a top priority given the complex and hazardous nature of daily tasks. This paper presents the Human-Agent Risk Navigation and Event Safety System (HARNESS), a modular AI framework designed to forecast hazardous events and analyze operational risks in U.S. Department of Energy (DOE) environments. HARNESS integrates Large Language Models (LLMs) with structured work data, historical event retrieval, and risk analysis to proactively identify potential hazards. A human-in-the-loop mechanism allows subject matter experts (SMEs) to refine predictions, creating an adaptive learning loop that enhances performance over time. By combining SME collaboration with iterative agentic reasoning, HARNESS improves the reliability and efficiency of predictive safety systems. Preliminary deployment shows promising results, with future work focusing on quantitative evaluation of accuracy, SME agreement, and decision latency reduction.

Jianfeng Zhu, Hailong Jiang, Yulan Wang et al.

Early substance use during adolescence increases the risk of later substance use disorders and mental health problems, yet the emotional and contextual factors driving these behaviors remain poorly understood. This study analyzed 23000 substance-use related posts and an equal number of non-substance posts from Reddit's r/teenagers community (2018-2022). Posts were annotated for six discrete emotions (sadness, anger, joy, guilt, fear, disgust) and contextual factors (family, peers, school) using large language models (LLMs). Statistical analyses compared group differences, and interpretable machine learning (SHAP) identified key predictors of substance-use discussions. LLM-assisted thematic coding further revealed latent psychosocial themes linking emotions with contexts. Negative emotions, especially sadness, guilt, fear, and disgust, were significantly more common in substance-use posts, while joy dominated non-substance discussions. Guilt and shame diverged in function: guilt often reflected regret and self-reflection, whereas shame reinforced risky behaviors through peer performance. Peer influence emerged as the strongest contextual factor, closely tied to sadness, fear, and guilt. Family and school environments acted as both risk and protective factors depending on relational quality and stress levels. Overall, adolescent substance-use discussions reflected a dynamic interplay of emotion, social context, and coping behavior. By integrating statistical analysis, interpretable models, and LLM-based thematic exploration, this study demonstrates the value of mixed computational approaches for uncovering the emotional and contextual mechanisms underlying adolescent risk behavior.

T. E. Bikbaev, M. Yu. Khlopov, A. G. Mayorov

The hypothesis of composite $XHe$ dark atoms may provide solution to the long-standing problem of direct searches for dark matter particles. The main problem of the $XHe$ dark atom is its ability to strongly interact with the nucleus of substance, arising from the unshielded nuclear attraction between the helium nucleus and the nucleus of matter. It is assumed that in order to prevent the destruction of the bound structure of dark atom, the effective potential of interaction between $XHe$ and the nucleus of substance must have dipole Coulomb barrier that prevents the fusion of dark matter atom particles with the nucleus of substance. The problem in describing the interaction between dark atom and substance nucleus is the three-body problem, for which an exact analytical solution is not available. Consequently, to assess the physical meaning of the proposed scenario, it is essential to develop a numerical approach. Our approach involves consistently developing an accurate quantum mechanical description of this three-body system, comprising bound dark atom and the external nucleus of substance. We incorporate the necessary effects and interactions to enhance the precision of the results, which helps to elucidate the most significant aspects of the proposed dark atom scenario.

Shuang Chen, Yifeng He, Barry Lennox et al.

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots capture images from different positions, which must be processed simultaneously to create a spatio-temporal model of the facility. In this paper, we propose a novel approach that integrates data simulation, a multi-modal deep learning network for coordinate prediction, and image reassembly to address the challenges posed by environmental disturbances causing drift and rotation in the robots' positions and orientations. Our approach enhances the precision of alignment in noisy environments by integrating visual information from snapshots, global positional context from masks, and noisy coordinates. We validate our method through extensive experiments using synthetic data that simulate real-world robotic operations in underwater settings. The results demonstrate very high coordinate prediction accuracy and plausible image assembly, indicating the real-world applicability of our approach. The assembled images provide clear and coherent views of the underwater environment for effective monitoring and inspection, showcasing the potential for broader use in extreme settings, further contributing to improved safety, efficiency, and cost reduction in hazardous field monitoring. Code is available on https://github.com/ChrisChen1023/Micro-Robot-Swarm.

Zizhe Xia

I use a novel geometric approach to compare information in moral hazard problems. I study three nested geometric orders on information, namely the column space, the conic span, and the zonotope orders. The orders are defined by the inclusion of the column space, the conic span, and the zonotope of the matrices representing the experiments. For each order, I establish four equivalent characterizations of the orders, (i) inclusion of feasible state-dependent utility sets, (ii) matrix factorizations, (iii) posterior belief distributions, and (iv) improved incentives in certain moral hazard problems. The column space order characterizes the comparisons of implementability in all moral hazard problems. The conic span order characterizes the comparisons of costs in all moral hazard problems with a risk neutral agent and limited liability. The zonotope order characterizes the comparisons of costs in all moral hazard problems when the agent can have any utility exhibiting risk aversion.

Kaichao Yang, Ibrahim M. Abu-Reesh, Zhen He

Electrochemical chlorination is promising for direct potable reuse (DPR) with zero-chemical-input and was investigated in this study for its application potential in reverse osmosis (RO)-based and non-RO-based DPR processes. Treatment of the simulated reclaimed water from non-RO-based trains showed satisfactory chlorine evolution compared to that from the RO-based trains. Under an applied current of 100 mA, a desired free chlorine concentration of 2 mg Cl2 L−1 was obtained within a short reaction time of 3.5 s. Consistent chlorine evolution performance was achieved in continuous experiments for 500 cycles, and the effluent pH was within the range of potable water guidelines (6.5 – 8.5). Electrochemical chlorination showed competitive disinfection performance compared to conventional chemical chlorination (7 log inactivation of E. coli within 60 s) at a low energy demand of ∼ 0.05 kWh m−3. The results of this preliminary investigation encourage the further exploration of electrochemical chlorination for DPR through the use of noble-metal-free anodes, utilization of renewable energy sources, removing persistent organic contaminants, and examining the synergy with RO-based DPR.

Fuhar Dixit, Edmund H. Antell, Katharine A. Faber et al.

Multiple poly- and perfluoroalkyl substances (PFASs) are present in aqueous film-forming foams (AFFF) used for firefighting activities. Currently, no single analytical technique provides a complete accounting of total PFASs or total organofluorine content in AFFF-contaminated samples. To provide insight into the performance of existing methods, we compared ten previously described PFAS measurement techniques. In AFFF-amended tap water, US EPA Methods 533 and 1633, adsorbable organic fluorine with particle induced gamma emission spectroscopy (AOF-PIGE) and fluorine-19 nuclear magnetic resonance (19F NMR) provided similar estimates of total fluorine. The total oxidizable precursor (TOP) assay, suspect screening, and adsorbable organic fluorine with combustion ion chromatography (AOF-CIC) yielded estimates of total organic fluorine that were about two to three times higher than the other techniques. Proximate to AFFF sources, suspect screening and modified EPA Method 1633 yielded higher results, while the TOP assay results were between the other two sets of analyses. Further from sources, suspect screening, modified EPA Method 1633, and the TOP assay yielded similar results that were 4-fold higher than results from targeted quantification methods, such as EPA Method 1633. These results are consistent with expectations about PFAS behavior and inform the selection of analytical techniques used for PFAS contamination characterization efforts.

Afifa Abdullahi Warsame, Sakarie Mustafe Hidig

No Abstract

Daisuke Inoue, Ryugo Nishimine, Shinpei Fujiwara et al.

Bioremediation is a promising approach for mitigating commingled contaminations of chlorinated ethenes (CEs) and 1,4-dioxane (DX). However, aerobic bioremediation to simultaneously remove CEs and DX remains challenging. This study aimed to explore the ability of Pseudonocardia sp. D17 (D17) to aerobically degrade CEs and its applicability for concurrent removal of CEs and DX. Aerobic degradation experiments of individual CEs revealed that D17 could degrade trichloroethene (TCE), three isomers of dichloroethene (DCE), and vinyl chloride (VC), and the trend of its degradation ability was cis-1,2-DCE (cDCE) > VC > TCE > trans-1,2-DCE > 1,1-DCE. Notably, the CE-degrading activity of D17 was expressed even without any auxiliary substrates. Further, when TCE, cDCE, or VC was co-present with DX (each at 1 mg/L), D17 could degrade both compounds without any significant inhibition (for TCE and cDCE) or with only a transient and reversible suspension of its DX degradation ability (for VC). These findings indicated that D17 is a promising agent for the aerobic bioremediation of CEs and DX co-contamination and provide novel insights into the future development of efficient aerobic bioremediation strategies.

Yuwei Wu, Yuezhan Tao, Peihan Li et al.

Real-time multi-robot coordination in hazardous and adversarial environments requires fast, reliable adaptation to dynamic threats. While Large Language Models (LLMs) offer strong high-level reasoning capabilities, the lack of safety guarantees limits their direct use in critical decision-making. In this paper, we propose a hierarchical optimization framework that integrates LLMs into the decision loop for multi-robot target tracking in dynamic and hazardous environments. Rather than generating control actions directly, LLMs are used to generate task configuration and adjust parameters in a bi-level task allocation and planning problem. We formulate multi-robot coordination for tracking tasks as a bi-level optimization problem, with LLMs to reason about potential hazards in the environment and the status of the robot team and modify both the inner and outer levels of the optimization. This hierarchical approach enables real-time adjustments to the robots' behavior. Additionally, a human supervisor can offer broad guidance and assessments to address unexpected dangers, model mismatches, and performance issues arising from local minima. We validate our proposed framework in both simulation and real-world experiments with comprehensive evaluations, demonstrating its effectiveness and showcasing its capability for safe LLM integration for multi-robot systems.