Comprehensive Experimental Design Research Articles

Electrochemical C−O and C−N Arylation using Alternating Polarity in flow for Compound Libraries

AbstractEtherification and amination of aryl halide scaffolds are commonly used reactions in parallel medicinal chemistry to rapidly scan structure–activity relationships with abundant building blocks. Electrochemical methods for aryl etherification and amination demonstrate broad functional group tolerance and extended nucleophile scope compared to traditional methods. Nevertheless, there is a need for robust and scale‐transferable workflows for electrochemical compound library synthesis. Herein we describe a platform for automated electrochemical synthesis of C−X arylation (X=NH, OH) in flow to access compound libraries. A comprehensive Design of Experiment (DoE) study identifies an optimal protocol which generates high yields across>30 aryl halide scaffolds, diverse amines (including electron‐deficient sulfonamides, sulfoximines, amides, and anilines) and alcohols (including serine residues within peptides). Reaction sequences are automated on commercially available equipment to generate libraries of anilines and aryl ethers. The unprecedented application of potentiostatic alternating polarity in flow is essential to avoid accumulating electrode passivation. Moreover, it enables reactions to be performed in air, without supporting electrolyte and with high reproducibility over consecutive runs. Our method represents a powerful means to rapidly generate nucleophile independent C−X arylation compound libraries using flow electrochemistry.

Comparative Analysis of Machine Learning Algorithms for Heart Attack Prediction

This study aims to develop a robust and efficient prediction model for heart failure detection by using state-of-the-art machine learning and artificial intelligence techniques. The research analyses various patient data that includes demographic and clinical variables to identify patterns and risk factors associated with heart failure. Parameters such as age, gender, cardiovascular indicators, lipid levels, blood glucose and exercise-induced responses were examined to capture the complex interactions that contribute to heart health outcomes. A comprehensive experimental design was used for the study, which included data preprocessing, feature correlation analysis, model training and performance evaluation of a series of machine learning algorithms. The models tested include random forest, gradient boosting (XGBoost), logistic regression, support vector machines (SVM) and neural networks, with a focus on balancing predictive accuracy and computational efficiency. The results show that ensemble methods such as XGBoost and Random Forest provide superior prediction accuracy while being computationally feasible, making them particularly suitable for clinical applications. Neural network architectures, in particular RNN and FNN (post-SHAP), also achieved high accuracy but were associated with significantly higher computational costs. Simpler models such as decision trees and logistic regression were computationally efficient but delivered lower performance metrics than the ensemble methods. These results underscore the potential of XGBoost and Random Forest as optimal models for integrating AI-assisted heart failure diagnostics into real-time medical decision-making processes and offer a compelling balance between precision and practicality in a clinical context.

Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization

This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.

Electrochemical C-O and C-N Arylation using Alternating Polarity in flow for Compound Libraries.

Etherification and amination of aryl halide scaffolds are commonly used reactions in parallel medicinal chemistry to rapidly scan structure-activity relationships with abundant building blocks. Electrochemical methods for aryl etherification and amination demonstrate broad functional group tolerance and extended nucleophile scope compared to traditional methods. Nevertheless, there is a need for robust and scale-transferable workflows for electrochemical compound library synthesis. Herein we describe a platform for automated electrochemical synthesis of C-X arylation (X=NH, OH) in flow to access compound libraries. A comprehensive Design of Experiment (DoE) study identifies an optimal protocol which generates high yields across>30 aryl halide scaffolds, diverse amines (including electron-deficient sulfonamides, sulfoximines, amides, and anilines) and alcohols (including serine residues within peptides). Reaction sequences are automated on commercially available equipment to generate libraries of anilines and aryl ethers. The unprecedented application of potentiostatic alternating polarity in flow is essential to avoid accumulating electrode passivation. Moreover, it enables reactions to be performed in air, without supporting electrolyte and with high reproducibility over consecutive runs. Our method represents a powerful means to rapidly generate nucleophile independent C-X arylation compound libraries using flow electrochemistry.

Investigation of experimental parameters in the electric field and mechanical vibration integrated AFM-based nanopatterning on PEDOT

Atomic force microscope (AFM)-based nanomanufacturing offers an affordable and easily deployable method for fabricating high-resolution nanopatterns. This study employs a comprehensive design of experiment (DOE) approach to investigate the effects of various parameters, such as voltage, speed, and vibration axis, on the width and depth of lithography patterns using electrical field and vibration-assisted lithography on PEDOT: PSS films. The DOE explores the effect of voltage and speed on the process of electrical field and vibration-assisted AFM-based nanopatterning in two vibration trajectories: a circular trajectory employing X and Y axis vibration and a reciprocating trajectory employing Y axis vibration. The results indicate that using circular XY-vibration with a low stiffness contact probe and optimized speed and voltage factors results in higher depth and width of the lithography patterns compared to Y-vibration alone at the same parameters as expected. In both cases, pattern width was dominantly controlled by the voltage. Regarding depth, in XY-vibration, the speed of the tip is the most significant factor, while for Y-vibration, voltage plays the most significant role. It is noteworthy that there is a minimum threshold of speed that can produce a pattern; for example, the high-speed level that produced patterns in the circular trajectory (XY-vibration) did not produce patterns in reciprocating motion (Y-vibration). In conclusion, the study demonstrates the significant impact of voltage, speed, and axis on the width and depth of the lithography patterns. These findings can be instrumental in developing and understanding AFM-based high-resolution nanofabrication techniques.

Effects of soil amendments and coverings on the kenaf yield and soil physicochemical properties in saline-alkali land

In order to investigate the effects of various treatments on the growth of kenaf in saline soil, this study employed a comprehensive experimental design incorporating a dual approach of soil amendment and mulching techniques. Soil treatment of saline and alkaline land, as well as biological material mulching treatment after sowing kenaf, were conducted. The growth dynamics of kenaf were assessed before the mid-term investigation, and the harvest period was examined. Soil physicochemical properties were measured and analysed at different periods. The results show that in the analysis of red sesame yield traits, the differences mainly appeared between soil treatments. There is no significant difference between the different mulching treatments within each soil treatment. In the mid-term study, significant differences were observed in kenaf plant height. Plant height was the most affected by soil conditioners, while other yield traits did not show significant variations. During harvest, only stem thickness and dry weight of a single plant did not show significant differences, whereas other yield traits did. The application of soil conditioners resulted in a 58.15% increase in dry hull yield compared to organic fertilizers and a 22.66% increase compared to regular fertilizers. Soil conditioners also led to higher levels of effective phosphorus, quick-acting potassium, organic matter, and total nitrogen throughout the kenaf growth period compared to organic and regular fertilizers. This is beneficial for enhancing soil quality and reducing soil alkalinity. Combined with the results of kenaf yield and soil physical and chemical properties analysis, it has been proven that the application of soil conditioner has the best effect, followed by the application of compound fertilizer, with organic fertilizer treatment being the least effective.

A systematic review of the Metaverse in formal education

PurposeIn light of the burgeoning interest in the Metaverse within educational contexts, this study provides a comprehensive review to address the knowledge gap prevalent among K-12 and higher education teachers and educators. The increasing integration of the Metaverse into classroom settings necessitates a systematic exploration of its impact on subject-specific pedagogy, assessment methods, research methodologies and overall learning outcomes.Design/methodology/approachThe research design involves a systematic review of 34 selected studies published between 2009 and 2023. The inclusion criteria prioritize investigations into Metaverse applications in classroom teaching, with a focus on subject disciplines, pedagogical approaches, measurement metrics and research methodologies. The selected studies undergo a detailed analysis and synthesis to extract meaningful patterns and trends.FindingsThe analysis reveals a predominant concentration on higher education, encompassing both science, technology, engineering, and mathematics (STEM) and non-STEM disciplines. However, few studies adopt comprehensive experimental designs. This suggests a gap in the methodological rigor of current research on Metaverse integration in education. While the Metaverse's impact on higher education is evident, the study underscores the need for more robust experimental designs and comprehensive research methodologies.Research limitations/implicationsThe limited exploration of students' experiences with the Metaverse in educational settings highlights a nascent research area that warrants further investigation. Future research should prioritize measuring students' perceptions and performance through indicators such as test grades to enhance our understanding of the Metaverse's efficacy in educational contexts.Originality/valueThe study aims to offer insights into the current state of Metaverse integration in education and identify areas for further research and development.

Exploring Conventional and Green Extraction Methods for Enhancing the Polyphenol Yield and Antioxidant Activity of Hyssopus officinalis Extracts.

Hyssopus officinalis L. (HO) is, as one of the most prevalently utilized plants, used in traditional medicine to cure various diseases as well as the in food and cosmetic industries. Moreover, HO is a rich source of polyphenols with potent antioxidant properties. However, the studies on the extraction of such compounds from HO are scanty and sparse. This study aims to optimize the extraction of polyphenols and maximize the antioxidant activity in HO extracts. A comprehensive experimental design was employed, encompassing varied extraction parameters to determine the most effective ones. Alongside conventional stirring (ST), two green approaches, the ultrasonic treatment (US) and the pulsed electric field (PEF), were explored, either alone or in combination. The extracted polyphenolic compounds were identified with a high-performance liquid chromatography-diode array detector (HPLC-DAD). According to the results, the employment of ST along with an ethanolic solvent at 80 °C for 150 min seems beneficial in maximizing the extraction of polyphenols from HO, resulting in extracts with enhanced antioxidant activity. The total polyphenol was noted at 70.65 ± 2.76 mg gallic acid equivalents (GAE)/g dry weight (dw) using the aforementioned techniques, and the antioxidant activity was noted as 582.23 ± 16.88 μmol ascorbic acid equivalents (AAE)/g dw (with FRAP method) and 343.75 ± 15.61 μmol AAE/g dw (with the DPPH method). The as-prepared extracts can be utilized in the food and cosmetics industries to bestow or enhance the antioxidant properties of commercial products.

Fatigue-Healing Performance Analysis of Warm-Mix Rubber Asphalt Mastic Using the Simplified Viscoelastic Continuum Damage Theory

As a green and low-carbon road material, warm-mix rubber asphalt (WMRA) has received extensive attention from scholars for its road performance. In the in-depth study of its properties, the fatigue characteristics of WMRA are particularly critical. However, in current studies on asphalt fatigue performance, its self-healing ability is often underestimated or neglected. Furthermore, the simplified viscoelastic continuum damage theory (S-VECD), with its accuracy, speed, and convenience, provides a powerful tool for analyzing asphalt fatigue performance. Therefore, to analyze the fatigue and self-healing performances of WMRA in practical applications, four sample materials were selected in this study: virgin asphalt mastic (VAM), rubber asphalt mastic (RAM), Sasobit rubber asphalt mastic (SRAM), and Evotherm rubber asphalt mastic (ERAM). Subsequently, the samples were subjected to a comprehensive experimental design with frequency sweep tests, linear amplitude sweep tests, and multiple intermittent loading time sweep tests under different aging conditions. The fatigue and self-healing performances of different aging degrees and different types of WMRA were evaluated based on the S-VECD theory. The results show that aging reduces the fatigue and self-healing performances of asphalt mastic to a certain extent, and at a 7% strain, the fatigue life of SRAM after long-term aging is only 30.7% of the life of the unaged sample. The greater the aging degree, the more pronounced the effect. Under different aging levels, the warm-mix agent can significantly improve the fatigue and self-healing performances of rubber asphalt mastic. After undergoing ten fatigue intermittent loading tests, the recovery rate of the complex shear modulus for the long-term aged VAM was 0.65, which is lower than that of SRAM under the same conditions, and the warm mix can further extend the fatigue life of rubber asphalt by improving the self-healing properties of the asphalt. The role of Sasobit in enhancing the fatigue and self-healing performances of rubber asphalt mastic is more significant. This study can provide a theoretical basis for the promotion and application of WMRA pavements and contribute to the sustainable development of road construction.

Evaluation of the turning parameters of AISI 5115 steel in dry and MQL cutting environments with the use of a coated carbide cutting insert: An Experimental Study

This study investigates the effects of cutting parameters on turning AISI 5115 steel in both dry and MQL environments using a coated carbide insert. The cutting parameters are determined using a full factorial design. A comprehensive full factorial experimental design was executed in order to investigate the effect of cutting parameters, including cutting speed, feed rate, and depth of cut, on surface roughness, cutting force and cutting temperature. Following the completion of the turning trials, surface roughness measurements were meticulously recorded. Also cutting force and cutting temperature were measured. The results of the study indicated that the most significant influence on surface roughness is exerted by the feed rate. Moreover, the impact of the depth of cut became more significant as the cutting speed decreased. While the surface roughness increased by 23% in the dry environment due to the increased feed rate at low cutting speed, the increase in the MQL environment was 32%. The cutting temperature is influenced by a number of factors, including the cutting parameters and the material properties. The maximum temperature for turning in the MQL environment was 381°C compared with an average cutting temperature of 430°C in dry machining conditions. The application of high-speed cutting in a dry cutting environment was found to result in a 10% increase in cutting temperature. The influence of cutting speed on the outcome was less pronounced in the MQL environment. At high cutting speeds and low parameter values in the MQL environment, the cutting force decreased by 75% in contrast to the low cutting speeds and high cutting parameters in the dry environment. The optimal cutting conditions for minimising cutting force were identified in the MQL environment, characterised by high cutting speeds and low feed rates.

Sustainable coatings for green solar photovoltaic cells: performance and environmental impact of recyclable biomass digestate polymers

The underutilization of digestate-derived polymers presents a pressing environmental concern as these valuable materials, derived from anaerobic digestion processes, remain largely unused, contributing to pollution and environmental degradation when left unutilized. This study explores the recovery and utilization of biodegradable polymers from biomass anaerobic digestate to enhance the performance of solar photovoltaic (PV) cells while promoting environmental sustainability. The anaerobic digestion process generates organic residues rich in biodegradable materials, often considered waste. However, this research investigates the potential of repurposing these materials by recovering and transforming them into high-quality coatings or encapsulants for PV cells. The recovered biodegradable polymers not only improve the efficiency and lifespan of PV cells but also align with sustainability objectives by reducing the carbon footprint associated with PV cell production and mitigating environmental harm. The study involves a comprehensive experimental design, varying coating thickness, direct normal irradiance (DNI) (A), dry bulb temperature (DBT) (B), and relative humidity (C) levels to analyze how different types of recovered biodegradable polymers interact with diverse environmental conditions. Optimization showed that better result was achieved at A = 8 W/m2, B = 40 °C and C = 70% for both the coated material studied. Comparative study showed that for enhanced cell efficiency and cost effectiveness, EcoPolyBlend coated material is more suited however for improving durability and reducing environmental impact NanoBioCelluSynth coated material is preferable choice. Results show that these materials offer promising improvements in PV cell performance and significantly lower environmental impact, providing a sustainable solution for renewable energy production. This research contributes to advancing both the utilization of biomass waste and the development of eco-friendly PV cell technologies, with implications for a more sustainable and greener energy future. This study underscores the pivotal role of exploring anaerobic digestate-derived polymers in advancing the sustainability and performance of solar photovoltaic cells, addressing critical environmental and energy challenges of our time.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 7 Given name: [Ashok] Last name [Kumar Yadav]. Also, kindly confirm the details in the metadata are correct.correct

Formulation and optimization of mesoporous silica loaded gel containing extract of Rosmarinus officinalis for treatment of acute wound healing

This study investigate the development and optimization of a mesoporous silica-loaded gel formulated with Rosmarinus officinalis extract, aiming to amplify wound healing capabilities by harnessing the plant's known therapeutic properties alongside the sophisticated drug delivery advantages of mesoporous silica. The formulation process entailed encapsulating the Rosmarinus officinalis extract within mesoporous silica particles before their incorporation into a gel matrix, with particular attention paid to the variable concentrations of Poloxamer 407 and HPMC K15 M to evaluate their effect on the gel's Spreadability and drug release profile. Employing a comprehensive full factorial experimental design analyzed through Design-Expert software, inclusive of ANOVA and 3D surface plotting, the investigation identified batch RF7 as the prime formulation, composed of 14 % w/v Poloxamer 407 and 1 % w/v HPMC K15 M. RF7 precisely matched the predicted Spreadability with an experimental value of 13.6 gm.cm/sec and showcased a promising Cumulative % drug release at 10 h of 87.93 %, closely aligning with a predicted 89.94 %, marking a minimal percentage error of −2.01 %. The study further validated RF7 efficacy through stability and in-vitro antibacterial activity assessments, where it demonstrated significant zones of inhibition against E. coli (22.5 ± 0.87 mm) and S. Aureus (23.4 ± 1.15 mm), nearly paralleling or outperforming the Rosmarinus officinalis extract itself and a marketed standard, Candiderma Plus. These findings, supported by rigorous statistical validation, underscore RF7 enhanced wound healing potential, marrying natural therapeutic virtues with cutting-edge drug delivery technology for a promising advancement in wound care solutions.

Predictive Modeling and Optimization of Plywood Drying: An Artificial Neural Network Approach

Introduction: This investigation delves into the optimization of the plywood drying process through the development of predictive models for output moisture content (MC_Out) and waviness. It focuses on bridging the gap in current methodologies by employing artificial neural networks (ANNs), optimized with genetic algorithms, to enhance prediction accuracy and process efficiency. Materials and Methods: A comprehensive experimental design was employed, analyzing the effects of three wood types (Doncel, Tamburo, and Zapote), two thickness levels, and three drying speeds on MC_Out and waviness. Data collected were subjected to both traditional statistical analysis and ANNs. The ANNs were fine-tuned through genetic algorithms, exploring different network architectures to achieve optimal predictive performance. Results: Statistical models revealed the significant influence of wood type, thickness, and drying speed on MC_Out and waviness, explaining 95.9% and 84.3% of the variations, respectively. The optimized ANN models, however, demonstrated superior accuracy, with the MC_Out model achieving fitted R-squared values of 0.940 and 0.757 for training and validation sets, respectively, thus outperforming traditional models in predicting drying outcomes. Discussion: The study underscores the effectiveness of ANNs in capturing complex nonlinear relationships within the plywood drying data, which traditional statistical models might not fully elucidate. The successful optimization of ANN architecture via genetic algorithms further highlights the potential of machine learning approaches in industrial applications, offering a more precise and reliable method for predicting drying process outcomes. Conclusion: The integration of artificial neural networks, optimized through genetic algorithms, represents a significant advancement in the predictive modeling of plywood drying processes. This approach not only offers enhanced prediction accuracy for key variables such as MC_Out and waviness but also paves the way for more efficient and controlled drying operations, ultimately contributing to the production of higher-quality plywood.

Internet of things-based study on online monitoring system of building equipment energy saving optimization control using building information modeling.

Smart building equipment monitoring is a well-established field focused on enhancing contemporary building comfort. The proliferation of Internet connectivity, facilitated by the internet of things (IoT), has transformed buildings from static structures into interactive environments. IoT has witnessed substantial growth across various aspects of daily life, from monitoring environmental conditions to managing building systems and storing data in the cloud. One critical application is the intelligent monitoring and control of building equipment, such as air conditioners, to optimize energy efficiency-a matter of increasing concern for building owners, design experts, and system integrators. Achieving comprehensive energy savings demands a meticulous approach to energy-efficient design and control. This paper's primary objective is to explore and analyze IoT-based energy-saving optimization techniques for intelligent building equipment, integrating building information modeling (BIM) technology. It particularly delves into the energy conservation control algorithm for air-conditioning systems. The research presents a challenge rooted in energy-saving optimization, established upon specific objective functions, followed by a detailed explanation of the energy-saving control algorithm. To validate their approach, the paper outlines a comprehensive experimental design. Over three sessions in August, they conducted control experiments in two distinct areas. Area 1 implemented the energy-saving control methodology discussed in the paper, utilizing virtual parameter enhancement mechanisms, while Area 2 adhered to conventional control methods. The results were enlightening. Area 1 demonstrated superior energy efficiency, consuming 735 kWh compared to Area 2's 819 kWh, signifying an impressive 11.43% reduction in energy consumption thanks to the optimized control strategy. This research underscores the practicality and significance of implementing IoT-based energy-saving strategies, with a focus on smart thermostats, HVAC controllers, and daylight sensors, in intelligent building equipment management to achieve substantial energy conservation gains.

A review of animal models for burning mouth syndrome: Mechanistic insights and knowledge gaps.

The underlying mechanisms of burning mouth syndrome (BMS) remain unclear leading to challenges and unsatisfactory management. Current treatments focus primarily on symptom relief, with few consistently achieving a 50% reduction in pain. This review aims to explore animal models of BMS to gain a better understanding of the underlying mechanisms and to discuss potential and existing knowledge gaps. A comprehensive review of PubMed®, Google Scholar, and Scopus was performed to assess advances and significant gaps of existing rodent models that mimic BMS-related symptoms. Rodent models of BMS involve reproduction of dry-tongue, chorda tympani transection, or overexpression of artemin protein. Existing preclinical models tend to highlight one specific etiopathogenesis and often overlook sex- and hormone-specific factors. Combining aspects from various BMS models could prove beneficial in developing comprehensive experimental designs and outcomes encompassing the multifaceted nature of BMS.

Prediction of colour strength in environmentally‐friendly dyeing of polyester fabric with madder using supercritical carbon dioxide

AbstractThe textile industry is one of the significant reasons for global water pollution, with dyeing processes being particularly environmentally detrimental. Researchers have explored alternative approaches to address this issue, such as using natural dyes, supercritical fluids and so forth. In addition to environment‐friendly approaches, reducing the number of experiments in studies, accurate production straightaway and using artificial intelligence (AI), one of the technologies of the present and the future that will provide significant support. Reaching clearer results with AI technology will not necessarily contribute to environment‐friendly technologies. However, AI techniques, including artificial neural networks (ANNs) and adaptive neuro fuzzy interface system (ANFIS) were employed to predict the colour strength (K/S) of the dyed fabric based on process parameters. A comprehensive experimental design involving pressure, temperature, and time variations was conducted, and the results were analysed using multi‐factor analysis of variance (MANOVA). The study demonstrates that supercritical carbon dioxide (scCO2) dyeing with madder on polyester fabric is a promising and environmentally friendly approach. Additionally, the optimised ANN and ANFIS models, aided by genetic algorithms (GAs), exhibit high predictive accuracy (less than 3%), providing insights into the impact of process parameters on colour strength. This research underscores the potential of AI‐driven automation in textile dyeing, offering solutions for dye formula prediction, colour matching, and defect detection, reducing the need for human intervention in these processes.

Sustainable chemometric methods boosted by Latin hypercube technique for quantifying the recently FDA-approved combination of bupivacaine and meloxicam in the presence of bupivacaine carcinogenic impurity: Comprehensive greenness, blueness, and whiteness assessments

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

A study of abrasive water jet performance on hybrid metal fiber laminate

The hybrid metal fiber laminate (MFL) composed of Titanium, Kevlar, and Jute. The MFL was manufactured using the compression molding technique. The machining of MFL is essential for achieving the required geometric shape and size to use in various industries. This is crucial for validating its functionality in different impact protection scenarios. The study systematically explores the influence of abrasive water jet pressure (AWJP), traverse rate (TR), abrasive quantity rate (AQR), and nozzle distance (ND) on the Erosion rate (ER). Through a comprehensive experimental design, the interactions and impact of these parameters on the cutting process are examined. The objective is to optimize the abrasive water jet cutting parameters to enhance the Erosion rate while preserving the structural integrity of prepared MFL. The analysis of variance (ANOVA) exploration unveiled that AWJP stood out as the most influential parameter on ER. The AWJP, TR, AQR, and ND are direct proportional with ER.

Novel functional insights into the microbiome inhabiting marine plastic debris: critical considerations to counteract the challenges of thin biofilms using multi-omics and comparative metaproteomics

BackgroundMicrobial functioning on marine plastic surfaces has been poorly documented, especially within cold climates where temperature likely impacts microbial activity and the presence of hydrocarbonoclastic microorganisms. To date, only two studies have used metaproteomics to unravel microbial genotype–phenotype linkages in the marine ‘plastisphere’, and these have revealed the dominance of photosynthetic microorganisms within warm climates. Advancing the functional representation of the marine plastisphere is vital for the development of specific databases cataloging the functional diversity of the associated microorganisms and their peptide and protein sequences, to fuel biotechnological discoveries. Here, we provide a comprehensive assessment for plastisphere metaproteomics, using multi-omics and data mining on thin plastic biofilms to provide unique insights into plastisphere metabolism. Our robust experimental design assessed DNA/protein co-extraction and cell lysis strategies, proteomics workflows, and diverse protein search databases, to resolve the active plastisphere taxa and their expressed functions from an understudied cold environment.ResultsFor the first time, we demonstrate the predominance and activity of hydrocarbonoclastic genera (Psychrobacter, Flavobacterium, Pseudomonas) within a primarily heterotrophic plastisphere. Correspondingly, oxidative phosphorylation, the citrate cycle, and carbohydrate metabolism were the dominant pathways expressed. Quorum sensing and toxin-associated proteins of Streptomyces were indicative of inter-community interactions. Stress response proteins expressed by Psychrobacter, Planococcus, and Pseudoalteromonas and proteins mediating xenobiotics degradation in Psychrobacter and Pseudoalteromonas suggested phenotypic adaptations to the toxic chemical microenvironment of the plastisphere. Interestingly, a targeted search strategy identified plastic biodegradation enzymes, including polyamidase, hydrolase, and depolymerase, expressed by rare taxa. The expression of virulence factors and mechanisms of antimicrobial resistance suggested pathogenic genera were active, despite representing a minor component of the plastisphere community.ConclusionOur study addresses a critical gap in understanding the functioning of the marine plastisphere, contributing new insights into the function and ecology of an emerging and important microbial niche. Our comprehensive multi-omics and comparative metaproteomics experimental design enhances biological interpretations to provide new perspectives on microorganisms of potential biotechnological significance beyond biodegradation and to improve the assessment of the risks associated with microorganisms colonizing marine plastic pollution.EMsdtEfK7hXyizwjh4GBBGVideo

Reliability Analysis of Compressive Fatigue Life of Cement Concrete under Temperature Differential Cycling

Cement concrete, as an extensively used engineering material, is omnipresent in various infrastructure projects such as bridges and roads. However, these structures often need to operate for extended periods under varying and harsh environmental conditions, facing not only complex vehicular loads but also the effects of temperature differential cycling. Consequently, understanding how temperature differential cycling impacts the compressive fatigue life of cement concrete has become a pivotal research topic. In this study, through a comprehensive experimental design, the fatigue life of cement concrete under typical temperature difference conditions (20–60°C) and different number of temperature differential cycling (60, 120, 180, 240, 300) was tested at three stress levels (0.70, 0.75, 0.85). Statistical analysis was conducted to obtain the Weibull distribution parameters of the compressive fatigue life of cement concrete. The Pf–S–N relationship of concrete considering reliability was analyzed, and a fatigue life prediction model under different reliability probabilities was established. The results show that the fatigue life of concrete subjected to temperature differential cycling follows a two-parameter Weibull distribution well. From the Pf–N curve, it can be seen that, regardless of the stress level, the calculated fatigue life under the same reliability probability decreases with the increase of temperature differential cycling times. At a 95% reliability probability, the decrease can reach 77.5%–87.5%. Based on the exponential function, a concrete fatigue life prediction model based on different reliability levels was established. Using this model, the S–lgN curve was plotted, and it was found that, regardless of the temperature differential cycling, an increase in reliability probability could lead to a 7.3%–14.4% reduction in logarithmic fatigue life (lgN). Additionally, this study also defined a fatigue life safety factor related to the number of temperature differential cycling and reliability probability, aiming to provide a theoretical basis for the design of cement concrete materials under the coupled environment of temperature differential cycling and fatigue loading.