Hasil untuk "Chemical technology"

J. C. Serrano-Ruiz, R. Luque, A. Sepúlveda-Escribano

M. Zakrzewska, Ewa Bogel-Łukasik, Rafał Bogel-Łukasik

W. Fenical, P. Jensen

D. Rackemann, William O. S. Doherty

E. A. Quadrelli, G. Centi, J. Duplan et al.

R. Judson, K. Houck, R. Kavlock et al.

Background Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use, and the thousands of environmental chemicals lacking toxicity data. The U.S. Environmental Protection Agency’s ToxCast program aims to address these concerns by screening and prioritizing chemicals for potential human toxicity using in vitro assays and in silico approaches. Objectives This project aims to evaluate the use of in vitro assays for understanding the types of molecular and pathway perturbations caused by environmental chemicals and to build initial prioritization models of in vivo toxicity. Methods We tested 309 mostly pesticide active chemicals in 467 assays across nine technologies, including high-throughput cell-free assays and cell-based assays, in multiple human primary cells and cell lines plus rat primary hepatocytes. Both individual and composite scores for effects on genes and pathways were analyzed. Results Chemicals displayed a broad spectrum of activity at the molecular and pathway levels. We saw many expected interactions, including endocrine and xenobiotic metabolism enzyme activity. Chemicals ranged in promiscuity across pathways, from no activity to affecting dozens of pathways. We found a statistically significant inverse association between the number of pathways perturbed by a chemical at low in vitro concentrations and the lowest in vivo dose at which a chemical causes toxicity. We also found associations between a small set of in vitro assays and rodent liver lesion formation. Conclusions This approach promises to provide meaningful data on the thousands of untested environmental chemicals and to guide targeted testing of environmental contaminants.

Dongyue Zhao, Xiuli Xu, Wei Wu et al.

Magnetic covalent organic framework nanocomposite denoted as Fe3O4@TPBD-BTA with core-shell structure was fabricated via a simple template-mediated precipitation polymerization method at mild conditions. The polyimine network shell was created through the polymerization of N,N,N′,N′-tetrakis(p-aminophenyl)-p-phenylenediamine (TPBD) and biphenyl-3,3′,5,5′-tetracarbaldehyde (BTA) in tetrahydrofuran (THF) by the Schiff-base reaction. Featuring with large good solution dispersibility, and high stability, the obtained Fe3O4@TPBD-BTA exhibited high adsorption capacities and fast adsorption for zearalenone and its Aflatoxin (AFT). The adsorption isotherms showed multilayer adsorption dominated at low concentration and monolayer adsorption at high concentration between the interface of AFs and Fe3O4@TPBD-BTA. With the Fe3O4@TPBD-BTA as sorbent, a magnetic solid-phase extraction-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was established for simultaneous adsorption and detection of five ZEAs in complex samples. The proposed method displayed favorable linearity, low limits of detection (0.006 5 − 0.008 2 μg/kg). The developed method has been applied for real sample analysis, with recoveries of 81.0%−109.6%. These results showed that Fe3O4@TPBD-BTA has a good application potential for the adsorption of AFs in milk and dairy products.

Juro Živičnjak, Antoneta Tomljenović, Igor Zjakić

During use, the surface of textile fabrics is prone to wear, which can cause changes such as pilling. Pilling (entanglement of fibers) is primarily assessed using the standard visual method EN ISO 12945-4:2020, but it can also be quantitatively measured by instrumental methods with image analysis software. Due to non-uniform digital imaging conditions, such as variations in magnification and analyzed surface area, the assessed area is often inconsistent. As a result, the total percentage of the fabric specimen surface area covered with pills is often omitted. To ensure uniform digital imaging, an innovative apparatus was designed and constructed in this research and applied to woven fabrics made from 100% cotton, wool, viscose, polyamide 6.6, polyester, and acrylic fiber. Pilling in the fabric specimens was induced by rubbing with the Martindale pilling tester (EN ISO 12945-2:2020) using two different abradant materials, through predefined pilling rubs ranging from 125 to 30,000. Pilling assessment was conducted using both the visual method and the improved instrumental method, following established grading classes based on the total percentage of the fabric specimen surface area covered with pills. The research results highlight the importance of uniform digital imaging and digital grading, as these demonstrate the high comparability of pilling grades assigned by the standard visual method while providing better distinction between consecutive grades.

Yanbiao Liu, Guandao Gao, C. Vecitis

ConspectusRapid population growth and industrialization have driven the emergence of advanced electrochemical and membrane technologies for environmental and energy applications. Electrochemical processes have potential for chemical transformations, chloralkali disinfection, and energy storage. Membrane separations have potential for gas, fluid, and chemical purification. Electrochemical and membrane technologies are often used additively in the same unit process, e.g., the chloroalkali process where a membrane is used to separate cathodic and anodic products from scavenging each other. However, to access the maximal potential requires intimate hybridization of the two technologies into an electroactive membrane. The combination of the two discrete technologies results in a range of synergisms such as reduced footprint, increased processing kinetics, reduced fouling, and increased energy efficiency.Due to their high specific surface area, excellent electric conductivity, and desirable robustness, 1D carbon nanotubes (CNTs) hold promise for many applications over a range of industry sectors such as a base material for electrodes and membranes. Importantly, CNT morphology and surface chemistry can be rationally modified and fine-tuning of these CNT physicochemical properties can enhance their functionality toward practical applications. The CNT 1D form allows assembly of a stable thin-film fibrous network by a variety of facile techniques. These CNT networks have pore sizes in the range of 10-500 nm (dpore ∼ 6-8dCNT) and thicknesses of 10-200 μm, both similar to those of classical polymer membranes, thus allowing for straightforward incorporation into commercial membrane devices modified for electroactivity inclusion.In this Account, CNTs and their composites are used as model electroactive porous materials to exemplify the design strategies and environmental applications of emerging electroactive membrane technology. The Account begins with a brief summary of the electroactive membrane design principles and flow processes developed by our groups. After the methodology section, a detailed discussion is provided on the underlying physical-chemical mechanisms that govern the electroactive membrane technology. Then we summarize our findings on the rational design of several flow-through electrochemical CNT filtration systems focused on either anodic oxidation reactions or cathodic reduction reactions. Subsequently, we discuss a recently discovered electrochemical valence-state-regulation strategy that is capable to detoxify and sequester heavy metal ions. Finally, we conclude the Account with our perspectives toward future development of the electroactive membrane technology.

Imad Ali Shah, Jiarong Li, Roshan George et al.

Hyperspectral imaging (HSI) offers a transformative sensing modality for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD) applications, enabling material-level scene understanding through fine spectral resolution beyond the capabilities of traditional RGB imaging. This paper presents the first comprehensive review of HSI for automotive applications, examining the strengths, limitations, and suitability of current HSI technologies in the context of ADAS/AD. In addition to this qualitative review, we analyze 216 commercially available HSI and multispectral imaging cameras, benchmarking them against key automotive criteria: frame rate, spatial resolution, spectral dimensionality, and compliance with AEC-Q100 temperature standards. Our analysis reveals a significant gap between HSI's demonstrated research potential and its commercial readiness. Only four cameras meet the defined performance thresholds, and none comply with AEC-Q100 requirements. In addition, the paper reviews recent HSI datasets and applications, including semantic segmentation for road surface classification, pedestrian separability, and adverse weather perception. Our review shows that current HSI datasets are limited in terms of scale, spectral consistency, the number of spectral channels, and environmental diversity, posing challenges for the development of perception algorithms and the adequate validation of HSI's true potential in ADAS/AD applications. This review paper establishes the current state of HSI in automotive contexts as of 2025 and outlines key research directions toward practical integration of spectral imaging in ADAS and autonomous systems.

Bo Wu, Ruihu Lu, Chao Wu et al.

Abstract Employing electrochemistry for the selective functionalization of liquid alkanes allows for sustainable and efficient production of high-value chemicals. However, the large potentials required for C(sp 3)-H bond functionalization and low water solubility of such alkanes make it challenging. Here we discover that a Pt/IrO x electrocatalyst with optimized Cl binding energy enables selective generation of Cl free radicals for C-H chlorination of alkanes. For instance, we achieve monochlorination of cyclohexane with a current up to 5 A, Faradaic efficiency (FE) up to 95% and stable performance over 100 h in aqueous KCl electrolyte. We further demonstrate that our system can directly utilize concentrated seawater derived from a solar evaporation reverse osmosis process, achieving a FE of 93.8% towards chlorocyclohexane at a current of 1 A. By coupling to a photovoltaic module, we showcase solar-driven production of chlorocyclohexane using concentrated seawater in a membrane electrode assembly cell without any external bias. Our findings constitute a sustainable pathway towards renewable energy driven chemicals manufacture using abundant feedstock at industrially relevant rates.

Fengyuan Zhao, Xinrui Fu, Jiahao Zhang et al.

Massive irreparable rotator cuff tears are difficult to restore when the tendon quality is poor, and the tendon retraction prevents complete repair. In such cases, tendon allograft bridging can restore continuity but cannot replicate the native tendon–bone interface. In this study, we evaluated an Achilles-tendon–bone block allograft (BTA) for anatomic tendon–bone interface reconstruction in a rabbit model of chronic massive rotator cuff tear. Thirty-six rabbits underwent bilateral infraspinatus tendon detachment, followed by repair after 3 weeks using direct suture (DS), tendon allograft without bone block (TA), or BTA. At 8 and 16 weeks, we assessed the magnetic-resonance-imaging-based tendon maturation (signal-to-noise quotient (SNQ)), micro-computed-tomography-based bone volume fraction (BV/TV) and histology, immunohistochemistry (COL I, II, X), and biomechanical-testing-based healing. The BTA group showed superior tendon continuity, significantly lower SNQ, and higher BV/TV than the DS and TA groups (p < 0.05) at both timepoints. The histological examination demonstrated denser collagen fibers, greater fibrocartilage formation, and complete bone–bone fusion in BTA. The immunohistochemical assessment revealed higher COL II and COL X expression, indicating advanced fibrocartilage maturation and mineralization. At 16 weeks, the BTA group achieved the highest ultimate load to failure (113.45 ± 14.45 N) and stiffness (19.65 ± 3.41 N/mm) values, exceeding those of the TA and DS groups (p < 0.05). These results indicate that the Achilles-tendon–bone block allograft bridge effectively reconstructs the layered tendon–bone interface, promotes osteointegration and fibrocartilage regeneration, and enhances biomechanical strength, all of which support its potential as a translational option for functional enthesis reconstruction in massive rotator cuff tear repair.

Witchakorn Charusiri, Naphat Phowan, Tharapong Vitidsant

M. Tunesi, R. Soomro, Xi Han et al.

MXenes have recently been recognized as potential materials based on their unique physical and chemical characteristics. The widely growing family of MXenes is rapidly expanding their application domains since their first usage as energy materials was reported in 2011. The inherent chemical nature, high hydrophilicity, and robust electrochemistry regard MXenes as a promising avenue for environment-remediation technologies such as adsorption, membrane separation, photocatalysis and the electrocatalytic sensor designed for pollutant detection. As the performance of MXenes in these technologies is on a continuous path to improvement, this review intends to cumulatively discuss the diversity and chemical abilities of MXenes and their hybrid composites in the fields mentioned above with a focus on MXenes improving surface-characteristics. The review is expected to promote the diversity of MXenes and their hybrid configuration for advanced technologies widely applied for environmental remediation.

H. Chow, Yue Zhang, Eilidh J. Matheson et al.

Cyclic peptides have been attracting a lot of attention in recent decades, especially in the area of drug discovery, as more and more naturally occurring cyclic peptides with diverse biological activities have been discovered. Chemical synthesis of cyclic peptides is essential when studying their structure-activity relationships. Conventional peptide cyclization methods via direct coupling have inherent limitations, like the susceptibility to epimerization at the C-terminus, poor solubility of fully protected peptide precursors, and low yield caused by oligomerization. In this regard, chemoselective ligation-mediated cyclization methods have emerged as effective strategies for cyclic peptide synthesis. The toolbox for cyclic peptide synthesis has been expanded substantially in the past two decades, allowing more efficient synthesis of cyclic peptides with various scaffolds and modifications. This Review will explore different chemoselective ligation technologies used for cyclic peptide synthesis that generate both native and unnatural peptide linkages. The practical issues and limitations of different methods will be discussed. The advance in cyclic peptide synthesis will benefit the biological and medicinal study of cyclic peptides, an important class of macrocycles with potentials in numerous fields, notably in therapeutics.

Sindhana Pannir-Sivajothi, Joel Yuen-Zhou

An important question in polariton chemistry is whether reacting molecules are in thermal equilibrium with their surroundings. If not, can experimental changes observed in reaction rates of molecules in a cavity (even without optical pumping) be attributed to a higher/lower temperature inside the cavity? In this work, we address this question by computing temperature differences between reacting molecules inside a cavity and the air outside. We find this temperature difference to be negligible for most reactions. On the other hand, for phase transitions inside cavities, as the temperature of the material is actively maintained by a heating/cooling source in experiments, we show cavities can modify observed transition temperatures when mirrors and cavity windows are ideal (non-absorbing); however, this modification vanishes when real mirrors and windows are used. Finally, we find substantial differences in blackbody spectral energy density between free space and infrared cavities, which reveal resonance effects and could potentially play a role in explaining changes in chemical reactivity in the dark.

Sanggyu Chong, Filippo Bigi, Federico Grasselli et al.

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.

A.A. Rotkovich, D.I. Tishkevich, I.U. Razanau et al.

Composite materials based on a polymer matrix of linear low-density polyethylene (LLDPE) and W were produced by thermal pressing. The content of W in the samples varied from 0 to 70 %. The recycling properties of LLDPE are demonstrated in this study, which significantly helped to reduce the defects. The microstructure of the composites consists of well-defined W grains covered by elastic LLDPE fibers. A homogeneous distribution of tungsten in the polymer matrix was observed for sample W70. The EDX analysis showed the presence of tungsten and carbon (from the polymer component). The XRD analysis confirms the increase in W content in the samples. The FTIR spectra of the composites showed an increase in the content of terminal methyl groups, a decrease in the molecular weight, and a decrease in the degree of crystallinity of the polyethylene matrix with an increase in the W content in the composite. The sample with 70 % W content has the highest effective density (2.61 g/cm3). The sample relative density ranges from 93.3 to 97.7 %. The porosity of LLDPE-W composites does not exceed 7 %. Gamma radiation shielding efficiency parameters such as LAC, HVL, and MFP were calculated using Phy-X/PSD. The radiation source was Co60, with an emission range of 0.8–2.5 MeV. As the gamma energy increases, it is observed that the values of all the parameters deteriorate. However, the sample with a maximum W content of 70 % has the best values of LAC, HVL, and MFP among the other samples.

Saumya Gopalkrishnan, Ph.D

Xinyue Li, Zhankun Xiong, Wen Zhang et al.

Abstract The prediction of drug‐drug interactions (DDIs) is a crucial task for drug safety research, and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy. Traditional wet chemical experiments for DDI are cumbersome and time‐consuming, and are too small in scale, limiting the efficiency of DDI predictions. Therefore, it is particularly crucial to develop improved computational methods for detecting drug interactions. With the development of deep learning, several computational models based on deep learning have been proposed for DDI prediction. In this review, we summarized the high‐quality DDI prediction methods based on deep learning in recent years, and divided them into four categories: neural network‐based methods, graph neural network‐based methods, knowledge graph‐based methods, and multimodal‐based methods. Furthermore, we discuss the challenges of existing methods and future potential perspectives. This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning. Deep learning models can scale to large‐scale datasets and accept multiple data types as input, thus making DDI predictions more efficient and accurate.