Material Production Research Articles

The development of RBL - STEM learning materials to improve students' computational thinking skills in solving convolutional neural network problems

In 21st century learning, computational thinking skills have become one of the essential competencies that need to be emphasised in the development of knowledge. To enhance computational thinking skills, research-based learning (RBL) with a science, technology, engineering and mathematics (STEM) approach, known as RBL-STEM, can be used. This study aims to explore RBL-STEM activities, describe the process and outcomes of developing RBL-STEM materials, and analyse data. In this research, the RBL-STEM framework is used to improve students' computational thinking skills in applying Convolutional Neural Network (CNN) to identify coffee plant diseases using a quadcopter drone and its flight path with resolving dominating set. The research method used is Research and Development (R&D). This research develops RBL-STEM materials and produces learning material products in the form of semester study plans, student assignment designs, student worksheets and learning outcome tests. The results of the development of the materials show validity with a validity criterion of 92%. Implementation using RBL-STEM materials was found to be practical with a practicality criterion of 96.25% and effective with an effectiveness criterion of 94.32%. In addition, students were highly engaged and provided very positive feedback on the learning experience. Pre-test and post-test analysis showed an improvement in students' computational thinking skills when solving CNN problems.

Palladium N-Heterocyclic Carbene-Catalyzed Aminations: An Outline.

Amination reactions play a pivotal role in synthetic organic chemistry, facilitating the generation of nitrogen-containing scaffolds with broad applications in drug synthesis, material production, polymer formation, and the generation of amino acids and peptides. Amination offers the potential to fine tune the properties of natural products and produce functional materials for various applications. Palladium N-heterocyclic carbene (Pd-NHC) emerges as an innovative and highly effective catalyst in this context. Under favorable reaction conditions, this robust and simple catalyst efficiently facilitates the synthesis of a diverse range of compounds with varying complexity and utility. Pd-NHC complexes exhibit significant σ-electron donating potential, enhancing the ease of the oxidative addition process in their mechanistic pathway. Their steric topography further contributes to a rapid reductive elimination. These complexes demonstrate remarkable stability, a result of the strong Pd-ligand bond. The wide variety of Pd-NHC complexes has proven highly efficient in catalyzing reactions across a spectrum of complexities, from simple to intricate. The domain of aminations catalyzed by Pd-NHC has undergone significant diversification, presenting new opportunities, particularly in the realms of material chemistry and natural product synthesis. This review outlines the advancements in Pd-NHC-catalyzed amination reactions, covering literature up to date.

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products.

The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.

Modeling and formulation/process parameters design methodological approaches for improving the performance of biocomposite materials for building, construction, and automotive applications: A state-of-the-art review

In today’s manufacturing sector, high-quality materials that satisfy customers’ needs at a reduced cost are drawing attention in the global market. Also, as new applications are emerging, high-performance biocomposite products that complement them are required. The production of such high-performance materials requires suitable optimization techniques in the formulation/process design, not simply mixing natural fibre/filler, additives, and plastics and characterization of the resulting biocomposites. However, a comprehensive review of the optimization strategies in biocomposite production intended for infrastructural applications is lacking. This study, therefore, presents a detailed discussion of the various optimization approaches, their strengths, and weaknesses in the formulation/process parameters design of biocomposite manufacturing. The report explores the recent progress in optimization techniques in biocomposite material production to provide baseline information to researchers and industrialists in this field. Therefore, this review consolidates prior studies to explore new areas.

Acid insoluble lignin material production by chemical activation of olive endocarps for an efficient furfural adsorption-removal from aqueous solutions

The present work describes a protocol of chemical activation, with acid catalyst, of olive endocarps to obtain acid insoluble lignin-rich materials with high capacities for the adsorption of furfural present in aqueous media. During biomass activation, factors such as acid concentration, reaction time and temperature, solid/liquid ratio and the presence of water extractives strongly affected both the surface characteristics of the treated bioadsorbents and their capacities for furfural retention (percentage increase close to 600% with respect to the crude biomass). Once a treated solid with good adsorbent properties was obtained, the optimal conditions for adsorption were found: stirring speed 80 rpm, temperature 303 K and adsorbent load 7.5 g solid/50 cm3. Kinetic study indicated the pseudo-second order model provided the best fit of the experimental data. At 303 K, the equilibrium adsorption capacities values ranged from 2.27 mg g−1 to 29.29 mg g−1, for initial furfural concentrations between 0.49 g dm−3 and 12.88 g dm−3. Freundlich model presented the best isotherm (R2 = 0.996 and SE = 4.7%) providing KF and n values of 0.115 (mg g−1) (mg dm−3)−n and 0.610, respectively. Since physical interactions predominate in the adsorption of furfural on chemically activated olive endocarps, the furfural removal process could have occurred reversibly on the heterogeneous surface of the bioadsorbents.

Carbon emissions analysis of office building renovation: a case study in China’s hot summer and cold winter zone

Abstract Building renovations in climates characterized by a hot summer and cold winter (HSWC) pose significant challenges in managing carbon emissions due to increased energy demands and material use. Despite advances in sustainable design, the carbon footprint of renovation activities remains a critical concern, particularly regarding the balance between initial emissions from construction and long-term operational efficiency. This study addresses these pressing issues through analyzing the carbon emissions associated with a building complex renovation in an HSWC zone of China. The research examines both the construction stage, where material production and transportation contribute significantly to emissions, and the operational stage, where energy consumption and equipment degradation influence long-term carbon output. By evaluating the effectiveness of renovation strategies and integrating building-integrated photovoltaic systems, this study demonstrates a reduction in annual carbon emissions from 1176.28 tCO2 to 1007.25 tCO2, a reduction of approximately 14.4%. The findings showed that the renovation process would achieve a carbon payback period of 7.46 years and a 24.9%–37.8% reduction in carbon emission intensity. This work underscores the potential significant environmental benefits of innovative materials and efficient design, offering valuable insights for future low-carbon building renovation projects.

Carbon emission measurement of the envelope of a university teaching building in Hefei City

Abstract In response to the challenges posed by global warming, China has formulated dual carbon goals. In this context, the construction industry, a key carbon emitter, is vital for implementing energy-saving and emission-reducing strategies. This study zeroes in on the pivotal role of building envelope structures in carbon emissions, with a focus on a university building in Hefei. Commencing with onsite measurements to gather initial data, the study leverages simulation software to conduct an in-depth analysis. Employing orthogonal experimental design and variance analysis, it meticulously assesses the carbon emissions associated with different materials used in exterior walls, roofs and windows, considering both the material production and building operation phases. The research scrutinizes the impact of these materials on carbon emissions, with a special focus on the performance of seven distinct building retrofit schemes. Key findings of the study underscore that the type and thickness of exterior wall materials substantially influence carbon emissions during the production phase. Conversely, the choice of window materials emerges as more critical in reducing emissions during the building operation phase. The implementation of the various retrofit schemes demonstrates a tangible reduction in overall building carbon emissions. Specifically, these schemes yield a yearly reduction in carbon emissions of 2.96–3.62 tons during operation and a substantial decrease of 30.36–165.97 tons in the production phase, compared to the original structure of the case study building. These insights not only offer practical and viable strategies for the construction industry’s low-carbon development but also provide theoretical underpinnings and references for future building designs and retrofits.

Carbon footprint of expressway bridges based on LCA

Abstract Based on the whole life cycle theory, the research stage of an expressway bridge is divided into six stages: preliminary preparation design stage, production and processing stage of bridge structural materials, transportation stage of materials, on-site construction stage, operation stage and waste recycling stage, and the calculation model of expressway bridge carbon footprint is constructed. Two bridges along the Pingxiang Expressway in Jiangxi Province were chosen as case studies. Their carbon emissions across the entire life cycle were computed using the bill of quantities. Findings indicate that the material production stage accounts for the highest proportion of carbon emissions throughout the life cycle, whereas the construction stage registers the lowest proportion. Lastly, the results are compared with existing research to validate the feasibility and accuracy of the calculation model.

The evaluation of potential toxic metal levels of various drugs used in children

Aims: Medicines have been widely used in recent years to support the immune system. Exposure to toxic metals can occur at different stages, such as raw material procurement, production, and packaging of drugs designed to support the immune system. This exposure can lead to serious health problems. In this study, the aim was to determine the levels of toxic metals in drugs used to support the immune system in children. Methods: Ten drug samples, available in pharmacies and intended for strengthening the immune system in children, were collected. Preliminary processes were conducted for the ICP-MS analysis of these collected drugs. Following the initial preparation, levels of Arsenic (As), Copper (Cu), Zinc (Zn), Manganese (Mn), Selenium (Se), Chromium (Cr), Mercury (Hg), Lead (Pb), Cadmium (Cd), Tin (Sn), Cobalt (Co), Aluminum (Al), Molybdenum (Mo), Antimony (Sb), Nickel (Ni) were determined using an ICP-MS device. Results: In our study, we evaluated the levels of toxic metals in drugs used to support the immune system. The average levels of Cr, Zn, As, Se, Cd, and Sn in the samples were found to exceed the limits set by international organizations. However, the average levels of Al, Cu, and Hg were very close to these limit levels. On the other hand, the levels of Mo, Sb, and Pb in the analyzed products were well below the established limits. Conclusion: The levels of toxic metals in immune-supporting drugs can lead to toxicity when the results of the study are evaluated. To inform consumers and safeguard their health, it would be appropriate for manufacturers to include information about the daily intake limits set by international organizations for toxic metals and trace elements. Conducting toxicological tests, especially during these inspections, will greatly contribute to protecting the health of children and fostering the development of healthy generations.

Assessment of Storage Practices in Warehousing Companies of Groundnut Export in North Kordofan State with Reference to GSP (WHO, 2019)

This study was conducted in all groundnut export warehousing Companies namely: (A, B, C, D, E and F) in EL Obied Town of North Kordofan State during the season 2023. The objective of the study to assessment of storage practices of export Groundnut (Arachis hypogaea) and compare with reference to Good Storage Practices (WHO, 2019). The data were collected by audit checklist prepared by WHO, 2019. The data were statistically analyzed by using Likert- five scale points and the significant different between means obtained by LSD at level of significant 0.05%. The result revealed that the general arithmetic mean of the three categories; (organization and management), (Quality system & traceability of food products) and (Vehicles and equipment) were score higher than 4.2 points in warehousing Companies A, B, D, and F these result refilled that full compliance, while the rest Companies were scored ranged between 3.4.- 4.1 points incomes minor deficiency. Also The result revealed that the general arithmetic mean of the five categories; (Personnel), (Shipping containers and container labeling), (Dispatch and receipt), (Documentation) and (Self-inspections) were score higher than 4.2 points in all warehousing Companies these result approved full compliance. Moreover, the result revealed that the general arithmetic mean of the two categories; (Complaints) and (Recalls) were scoring 4 points in all warehousing Companies these result agreed full compliance. The result concluded that the obtained total score (out of 345 points) of storage practices for warehousing Companies A, B, C, and E were 261,260, 221 and 249 points respectively these result finding indicting classified unsatisfactory due to (≤ 80 %). while the warehousing Company D was score 276 points these outcome indicting classified standard due to (≥ 80 ≤ 84%) and the warehousing Company F was score 297 points these conclusion indicting classified good due to (≥ 85 ≤ 89% ) according to Global Food Safety(GFS,2013). The study recommended that the warehousing companies require to application Total Quality Management system (TQM) to ensure food safety and food quality of their raw material and end products as well as increase the power competitive on global level

Compositions of enzyme preparations for targeted modification of multicomponent bran

Microbial enzyme preparations (EPs) of the cellulolytic, proteolytic and phytase action, as well as their compositions, allow obtaining quite a wide spectrum of products of hydrolysis of proteins, non-starch polysaccharides and phytin by means of targeted biocatalysis upon action on grain bran. The use of EPs and multienzyme compositions (MECs) is inherently a modern tool for intensification of biochemical processes underlying one or another food technology. At the same time, EPs allow modifying products of processing plant raw materials, in particular cereal, leguminous and oil-bearing crops (deep processing) and obtain valuable feed and food ingredients. Two multienzyme compositions based on domestic and foreign EPs have been developed and scientifically substantiated. Optimal conditions have been determined for conducting enzymatic reactions when using wheat-lentil-flax bran as a substrate. High effectiveness of the developed MECs has been shown: the proportion of reducing substances (RS) in the hydrolysates under study increased compared to the mass fraction of the autolysates of wheat and three-component bran by 2.8 and 2.3 times (MEC‑1) and by 3.5 and 2.7 times (MEC‑2), respectively. The content of soluble protein increased by 4.7 and 3.0 times (MEC‑1) and by 6.4 and 4.2 times (MEC‑2). The proportion of soluble phosphorus increased on average by 3.0–3.5 times when using MEC‑1 and MEC‑2. It has been found by gel-electrophoresis that the fractions containing low-molecular-weight peptides and free amino acids (MW < 1000 Da) in the hydrolysates obtained using MECs exceeded by 3–4 times the corresponding fraction in the hydrolysates obtained under the action of endogenous enzymes (autolysis). With that, according to the HPLC results, the concentration of amino acids that are most valuable in terms of nitrogenous nutrition of yeasts (aspartic acid, arginine) increased on average by 2.5–3.0 times, the concentration of valine by 5 times, histidine and isoleucine by 2.0–2.5 times in the experimental hydrolysates of three-component bran.

A comprehensive review on pilot plant scale up and platform technology

The pharmaceutical business uses pilot plant scale-up strategies to create reliable manufacturing procedures and turn lab-scale formulas into commercial products. The place called Pilot is where the five elements Material, Man, Method, and Machine are combined to manufacture things. A tiny, basic lab scale formula will be tested on a replica of the intended plant in the pilot plant before spending a significant amount of money on a production unit. Scaling up a pilot plant provides information on formula examination, reviewing the range of pertinent processing equipment, understanding raw material specifications, production rate, and physical space requirements. It can hold accurate documentation and reports for analysis in support of the GMP procedure. This review research discusses the factors for solids, liquids, and semisolids for scaling up pilot plants. The primary goal of a pilot plant is to "identify errors on a small scale and generate revenue on a large scale."

A Biomimetic Lignocellulose Aerogel-Based Membrane for Efficient Phenol Extraction from Water.

Rapid extraction and concentration systems based on green materials such as cellulose or lignin are promising. However, there is still a need to optimize the material properties and production processes. Unlike conventional cellulose or lignin sorbent materials, aquatic reed root cells can concentrate external organic pollutants in the water and accumulate them in the plant. Inspired by this, a new nanocellulose-lignin aerogel (NLAG) was designed, in which nanocellulose was used as a substrate and lignin and polyamide epoxy chloropropane were used to crosslink cellulose in order to enhance the strength of the NLGA, resulting in good mechanical stability and water-oil amphiphilic properties. In practical applications, the organic membrane on the NLAG can transport organic pollutants from water to the NLAG, where they are immobilized. This is evidenced by the fact that the aerogel can remove more than 93% of exogenous phenol within a few minutes, highly enriching it inside. In addition, the aerogel facilitates filtration and shape recovery for reuse. This work establishes a novel biopolymer-aerogel-based extraction system with the advantages of sustainability, high efficiency, stability, and easy detachability, which are hard for the traditional adsorbent materials to attain.

PatchRLNet: A Framework Combining a Vision Transformer and Reinforcement Learning for The Separation of a PTFE Emulsion and Paraffin

During the production of a PolyTetraFluoroEthylene(PTFE) emulsion, it is crucial to detect the separation between the PTFE emulsion and liquid paraffin in order to purify the PTFE emulsion and facilitate subsequent polymerization. However, the current practice heavily relies on visual inspections conducted by on-site personnel, resulting in not only low efficiency and accuracy, but also posing potential threats to personnel safety. The incorporation of artificial intelligence for the automated detection of paraffin separation holds the promise of significantly improving detection accuracy and mitigating potential risks to personnel. Thus, we propose an automated detection framework named PatchRLNet, which leverages a combination of a vision transformer and reinforcement learning. Reinforcement learning is integrated into the embedding layer of the vision transformer in PatchRLNet, providing attention scores for each patch. This strategic integration compels the model to allocate greater attention to the essential features of the target, effectively filtering out ambient environmental factors and background noise. Building upon this foundation, we introduce a multimodal integration mechanism to further enhance the prediction accuracy of the model. To validate the efficacy of our proposed framework, we conducted performance testing using authentic data from China’s largest PTFE material production base. The results are compelling, demonstrating that the framework achieved an impressive accuracy rate of over 99% on the test set. This underscores its significant practical application value. To the best of our knowledge, this represents the first instance of automated detection applied to the separation of the PTFE emulsion and paraffin.

Effect of Hexagonal Boron Nitride on Morphology, crystallinity and internal Exfoliation properties of Polypropylene melt and solid phases

ABSTRACT Polypropylene (PP) is one of the most important polymers widely used in different industries due to its many superior properties. Hexagonal boron nitride (hBN) is generally used in the electronic packaging industry and insulator material production due to its high thermal conductivity and low electrical conductivity. However, the lubrication properties have not yet been examined in the polymer matrix. The main purpose of this study is to examine the lubricating properties of hBN in the PP matrix and to expand its application areas. In this research, hBN/PP composites have been produced and characterized by scanning electron microscopy with an energy dispersive X-ray detector (SEM-EDX) and the differential scanning calorimeter (DSC), respectively. The linear viscoelastic behaviors (LVBs) and rheological properties of the hBN/PP composites have been investigated at 190°C by hybrid rheometer which had high temperature plate-plate geometry system; furthermore, the SEM has been used for searching hBN particles deformation, which was mechanical deformation and distribution into the PP. The oscillation sweeps tests have shown that the critical strain values and the gelation point of composites decreased with the increasing of the hBN content into the composites. Over the 10%, hBN addition into the PP elevated internal lubrication by the exfoliation of hBN plaques. The Williamson model and the discrete retardation spectrum (DRS) equation has been applied on the hBN loaded PP composites and the PP.

Editorial: Nanoscale insight into the hydration products of cementitious materials: from theory to engineering

Editorial: Nanoscale insight into the hydration products of cementitious materials: from theory to engineering

Life cycle assessment of atmospheric environmental impact on the large-scale promotion of electric vehicles in China

Decarbonizing the transportation sector emerges as a pivotal step in addressing climate change. In recent years, rapid growth in China’s new energy automotive industry has significantly contributed to transportation decarbonization. However, environmental challenges in producing and recycling electric vehicles (EVs) may limit emission reduction benefits. In this study, we establish a comprehensive life cycle assessment model for vehicles to analyze the gap in air pollutant and greenhouse gas emissions between electric vehicles and internal combustion engine vehicles (ICEVs). Based on this model, the environmental benefits of further promoting electric vehicles in China are evaluated. Results reveal that, compared to ICEVs, EVs reduce life cycle emissions of CO2 by 12%, NOx by 69%, and VOCs by 9%. Primary constraints on EVs in emission reduction are traced to raw material and component production, notably lithium batteries. By 2025, under the low carbon EVs policy scenario, widespread EV production and sales could cut lifecycle emissions by 3.55 million tons of CO2, 3,6289 tons of NOx, and 4315 tons of VOCs. During the driving stage, these indicators contribute 495%, 124%, and 253%, respectively, to total emission reduction throughout the lifecycle. This study conducts a comprehensive lifecycle analysis of greenhouse gases and various air pollutants for Chinese EVs. It integrates the latest market trends, application progress, and policy guidelines into scenario design, identifying key sources and indicators of atmospheric pollution in the EV production chain. The findings offer valuable policy insights into China’s role in the global emission reduction process.

OPTIMASI PERSEDIAAN BAHAN BAKU DAN PRODUKSI USAHA GANEPO PUTRI YOSE DENGAN MENGGUNAKAN ALGORITMA GREY WOLF OPTIMIZER

Putri Yose's Ganepo business, located in Padang Kandi seven koto talago, is one of the Rubik Ganepo producers that has grown to be one of the main suppliers in the Bukittingi and Riau areas. Interviews with the business owner revealed problems in inventory management that affect the profitability of this business. This study aims to assess and improve inventory management with a focus on raw material procurement and production cycle time. The model used in this study is Integrated Procurement Production (IPP) with the Grey Wolf Optimizer (GWO) optimization algorithm to determine the decision variables, namely the frequency of raw material procurement and production cycle time. GWO algorithm is chosen because it is a metaheuristic optimization method that is able to overcome real-world problems, avoid stagnation in solutions of local, and has a wide search space. The results showed that the IPP model with GWO was able to provide a new inventory policy, including the optimal procurement quantities of salt, cooking oil, garlic, turmeric, and production cycle time. The implementation of the policy increased revenue by 0.309203%.

The Development of RBL-STEM Learning Materials to Improve Students' Information Literacy in Solving Rainbow Antimagic Coloring Problem for ETLE Technology

Students often struggle to solve complex mathematical problems in real-world contexts due to low information literacy skills. To improve information literacy skills, an effective learning approach can be RBL-STEM, which provides research-based learning and can be practically applied in the real world. This study aims to investigate RBL-STEM activities, describe the process and results of developing RBL-STEM materials, and analyze data from the results of developing these materials to improve students’ information literacy skills. The method of research used is research and development (R&D). The purpose of this research is to develop RBL-STEM materials and produce learning material products in the form of semester learning plans, student assignment designs, student worksheets, and learning outcomes tests. The results of the materials development showed validity with a validity criterion of 92.75%. The trial was conducted with 40 students, and the implementation using the RBL-STEM materials was found to be practical with a practical criterion of 98.75% and effective with an effectiveness criterion of 94%. In addition, the students were highly engaged and responded very positively to the learning experience. Pretest and posttest analysis showed an increase in students’ information literacy in solving the rainbow antimagic coloring problem. The study also identified three levels of information literacy skills: high, medium, and low. Statistical analysis, phase portrait, NVivo, and word cloud confirmed the research findings and showed an increase in students’ information literacy skills. Thus, RBL-STEM has the potential to improve students’ information literacy in real-world contexts, such as the application of ETLE using graph neural network techniques.

Application of machine learning and grey Taguchi technique for the development and optimization of a natural fiber hybrid reinforced polymer composite for aircraft body manufacture

Abstract The rapid expansion of the air transport industry raises significant sustainability concerns due to its substantial carbon emissions and contribution to global climate change. These emissions are closely linked to fuel consumption, which in turn is influenced by the weight of materials used in aircraft systems. This study extensively applied machine learning tools for the optimization of natural fiber-reinforced composite material production parameters for aircraft body application. The Taguchi optimization technique was used to study the effect of sisal fibers, glass fibers, fiber length, and NaOH treatment concentration on the performance of the materials. Multi-objective optimization methods like the grey relational analysis and genetic algorithm (using the MATLAB programming interface) were employed to obtain the best combination of the studied factors for low fuel consumption (low carbon emission) and high-reliability structural applications of aircraft. The models developed from regressional analysis had high accuracy of prediction, with R-Square values all >80%. Optimization of the grey relational analysis of the developed composite using the genetic algorithm showed the best process parameter to achieve low weight material for aircraft application to be 40% sisal, 5% glass fiber at 35 mm fiber length, and 5% NaOH concentration with grey relational analysis at the highest possible level, which is unity.