Processing Temperature Research Articles

Integrating Data Mining and Natural Language Processing to Construct a Melting Point Database for Organometallic Compounds.

As semiconductor devices are miniaturized, the importance of atomic layer deposition (ALD) technology is growing. When designing ALD precursors, it is important to consider the melting point, because the precursors should have melting points lower than the process temperature. However, obtaining melting point data is challenging due to experimental sensitivity and high computational costs. As a result, a comprehensive and well-organized database for the melting point of the OMCs has not been fully reported yet. Therefore, in this study, we constructed a database of melting points for 1,845 OMCs, including 58 metal and 6 metalloid elements. The database contains CAS numbers, molecular formulas, and structural information and was constructed through automatic extraction and systematic curation. The melting point information was extracted using two methods: 1) 1,434 materials from 11 chemical vendor databases and 2) 411 materials identified through natural language processing (NLP) techniques with an accuracy of 86.3%, based on 2,096 scientific papers published over the past 29 years. In our database, the OMCs contain up to around 250 atoms and have melting points that range from -170 to 1610 °C. The main source is the Chemsrc database, accounting for 607 materials (32.9%), and Fe is the most common central metal or metalloid element (15.0%), followed by Si (11.6%) and B (6.7%). To validate the utilization of the constructed database, a multimodal neural network model was developed integrating graph-based and feature-based information as descriptors to predict the melting points of the OMCs but moderate performance. We believe the current approach reduces the time and cost associated with hand-operated data collection and processing, contributing to effective screening of potentially promising ALD precursors and providing crucial information for the advancement of the semiconductor industry.

Development and characterization of carbon fiber reinforcement in Aluminium metal matrix composites

Abstract Carbon fibers (CF) possess exceptional mechanical properties and the highest degree of chemical stability. However, carbon reinforcement in metal matrix composites is extremely scarce due to production difficulties, particularly in obtaining a uniform distribution. Carbon fiber reinforced composites are typically made using high temperature processing processes. However, the fibers must be coated with Ni or Cu in order to achieve effective particle dispersion; otherwise, there is a larger likelihood of intermetallic compound formation, which reduces the chances for enhanced properties. In this work, the metallurgical, mechanical, and tribological characteristics of the carbon fiber reinforcement in AA 7050 are examined. Uncoated carbon fibers are reinforced into the Aluminium matrix using a low temperature processing technique known as powder metallurgy. The AA 7050 matrix reinforced with carbon fibers at various weight percentages between 0 and 1.5. The samples undergone mechanical and metallurgical testing in accordance with ASTM guidelines. The findings indicate that the 0.25 weight percent carbon fiber reinforcement in the matrix increased the material’s hardness by 30% over the monolithic alloy, making it an excellent alternative for structural applications.

Mutation of MeMinD increased amyloplast size with a changed starch granule morphologenesis and structures in cassava storage roots

Amyloplasts are the sites of starch synthesis and accumulation. Little is known about amyloplast division and its effects on the size, structure, and physicochemical properties of starch granules. In this study, we created mutants of plastid division-related gene MeMinD by CRISPR/Cas9 technology, leading to the disruption of normal division of amyloplasts in cassava storage roots. The memind mutants exhibited significantly enlarged amyloplasts with an increased number of starch granules, and broader range of granule sizes. The loss of MeMinD function led to transcriptional reprogramming of gene expressions related to starch-synthesizing enzymes, affecting the fine structure of starch. Starch in memind mutant storage roots showed a significantly decreased proportion of shorter amylopectin chains and an increased proportion of medium and long chains, which ultimately led to a significant increase in apparent amylose content (AAC) in memind mutants compared to that in WT. The changes in starch granule size and structure resulted in a significant increase in onset temperature (To), peak temperature (Tp), and conclusion temperature (Tc) of the gelatinization process, extending the time to reach peak temperature. These data suggest that regulating amyloplast division affects starch accumulation in cassava, presenting an effective strategy for developing novel cassava starch.

Reconstructing 169 years of historical atmospheric and hydrologic conditions in the American River Watershed through the Watershed Environmental Hydrology Hydro-Climate Model system

The risk of more frequent and intensified extreme events is expected to increase with climate change. In semi-arid regions, particularly, both increased flooding and prolonged droughts pose a threat as the water resources system must be prepared for both types of extremes. To understand how future extremes may be altered, however, past extremes must be understood. For this reason, this study employs three physically based models to simulate the atmospheric, snow, and hydrologic conditions in the American River Watershed: the Weather Research and Forecasting Model and the Watershed Environmental Hydrology Hydro-Climate Model with the addition of a SNOW Module. Each model is individually calibrated and validated against either gauge data or data provided by the Parameter-Elevation Regressions on Independent Slopes Model for two 10-year periods before a full 169-year daily reconstruction is performed for the period from 1852 through 2020. To analyze the model performance under hydrologic extremes, three flood years (1997, 2006, and 2017) and two drought periods (1987–1992 and 2012–2016) are further analyzed. The reconstruction outputs are found to successfully simulate historical conditions at both daily and monthly scales. Results highlight the importance of fully accounting for the role of temperature in snow processes and the subsequent flow conditions. By understanding how the models perform under historical extreme conditions, future extreme events can be compared, and necessary adaptation plans can be conceived to ensure that the watershed is prepared for future wet and dry conditions. Additionally, the created reconstruction dataset is comprehensive, simulating conditions across the hydroclimate, at a finer spatial and temporal scale than is typically available from observation data alone. The dataset can be further analyzed to assess changes in the trends and the potential trajectory of extreme events within the American River Watershed.

Comparison of conventional and green approaches to the synthesis of aromatic Schiff bases

Abstract The chemical industry is one of the key elements in improving the quality of human life. At the same time, it generates pollution influencing the ecosystem and our health. To limit or remove some of the reasons of pollution for two decades less harmful approaches to the synthesis of various organic compounds were developed. Among organic compounds, azomethines, also known as Schiff bases, are of particular interest in biochemistry, medicine, and pharmacy. For over twenty years many novel approaches to the synthesis of these compounds have been investigated. Toxic solvents (benzene, toluene) have been replaced with water, fruit juice, or white egg, and in many cases, temperature and time of process were significantly reduced. In the review, conventional and green approaches to the synthesis of Schiff bases are presented with a focus on the effectiveness of these methods, including advantages and disadvantages.

Experimental and simulation study on lean combustion characteristics of passive pre-combustion chamber ignition boosted GDI engine

In order to verify the potential application of passive pre-combustion chamber technology in commercial GDI engines, the efficiency characteristics, combustion characteristics (CA50, Knock Index and COV) and emission characteristics of a pre-combustion chamber ignition engine in a boosted environment were studied by three-stage injection strategy to form a lean burn environment (λ = 1.3). A 3D simulation model of pre-combustion chamber ignition engine is established. Combined with the engine bench test results, the process of intake, mixture formation, combustion and emission generation under different working conditions is simulated by simulation method. The development process of TKE (turbulent kinetic energy), air-fuel ratio, temperature and combustion emissions in the main/pre-combustor is studied. It is found that under high load conditions in boosted pre-combustion chamber engine, the average knock index can be controlled between 2 and 3.4 bar, and the COV can be controlled between 3% and 3.5%. The indicated thermal efficiency reaches over 40% within the load range of IMEP from 10 to 14 bar. When the load increases, NOx and soot emissions show a downward trend, the HC and CO emissions slight increase. The pre-combustion chamber engine exhibits a tumble intake pattern around the cylinder axis perpendicular to the cylinder axis. The TKE reaches its maximum value 20 CAD before the top dead center and then rapidly decreases. The use of a three-injection strategy can form a uniform stratified mixture at the ignition moment. The fuel air equivalence ratio in the ignition area of the pre-combustion chamber is 0.8–0.9, slightly higher than the average fuel air equivalence ratio of 0.75.

AFSD-Physics: Exploring the governing equations of temperature evolution during additive friction stir deposition by a human-AI teaming approach

This paper presents a modeling effort to explore the underlying physics of temperature evolution during additive friction stir deposition (AFSD) by a human-AI teaming approach. AFSD is an emerging solid-state additive manufacturing technology that deposits materials without melting. However, both process modeling and modeling of the AFSD tool are at an early stage. In this paper, a human-AI teaming approach is proposed to combine models based on first principles with AI. The resulting human-informed machine learning method, denoted as AFSD-Physics, can effectively learn the governing equations of temperature evolution at the tool and the build from in-process measurements. Experiments are designed and conducted to collect in-process measurements for the deposition of aluminum 7075 with a total of 30 layers. The acquired governing equations are physically interpretable models with low computational cost and high accuracy. Model predictions show good agreement with the measurements. Experimental validation with new process parameters demonstrates the model’s generalizability and potential for use in tool temperature control and process optimization.

Synthesis of Dumbbell-Like Heteronanostructures Encapsulated in Ferritin Protein: Towards Multifunctional Protein Based Opto-Magnetic Nanomaterials for Biomedical Theranostic

Dumbbell-like hetero nanostructures based on gold and iron oxides is a promising material for biomedical applications, useful as versatile theranostic agents due the synergistic effect of their optical and magnetic properties. However, achieving precise control on their morphology, size dispersion, colloidal stability, biocompatibility and cell targeting remains as a current challenge. In this study, we address this challenge by employing biomimetic routes, using ferritin protein nanocages as template for these nanoparticles’ synthesis. We present the development of an opto-magnetic nanostructures using the ferritin protein, wherein gold and iron oxide nanostructures were produced within its cavity. Initially, we investigated the synthesis of gold nanostructures within the protein, generating clusters and plasmonic nanoparticles. Subsequently, we optimized the conditions for the superparamagnetic nanoparticles synthesis through controlled iron oxidation, thereby enhancing the magnetic properties of the resulting system. Finally, we produce magnetic nanoparticles in the protein with gold clusters, achieving the coexistence of both nanostructures within a single protein molecule, a novel material unprecedented to date. We observed that factors such as temperature, metal/protein ratios, pH, dialysis, and purification processes all have an impact on protein recovery, loading efficiency, morphology, and nanoparticle size. Our findings highlight the development of ferritin-based nanomaterials as versatile platforms for potential biomedical use as multifunctional theranostic agents.

Research on automated verification method for temperature-related tests of electrical energy meters

Abstract Program of pattern evaluation of fixed electrical energy meters-alternating current and GB/T 17215 series of national standards for the examination of energy meter performance by the impact of temperature change, set up a variety of temperature-related test items. Systematic research is possible to conclude that temperature-related tests can be divided into two categories according to the test procedure: Hold at test temperature for a certain period of time, return to the reference temperature and then measure the relevant parameters; measurement of the relevant parameters immediately after a certain period of time at the test temperature. Relevant parameters include errors at different load points and daily timing error of the internal clock of the energy meter. Data processing is undertaken after the test mainly consists of deriving error shift and mean temperature coefficient. This automated calibration method allows you to freely set the test temperature, test duration, reference temperature, stabilization duration, detection parameters, test points and data processing according to the test requirements. The paper explains in detail the application of the automated calibration method to realize typical temperature dependence, test of time-keeping accuracy of the energy meter with temperature and climatic tests (high temperature test, low temperature test and durability test of accuracy) The method breaks the shackles of the traditional calibration method of fixed test temperature, fixed test points and fixed test duration. It can adapt to the development needs of different levels of pattern evaluation of energy meter and national standards, for the laboratory to realize automated testing to provide the basis for industry standards and product testing commissioned inspection.

A multi-scale method for the study of deep drying of ethylene with 3A zeolite

Coal-based ethanol to ethylene (ETE) is an emerging production pathway with the advantages of a short construction period, high product purity, and few by-products. N2 is commonly used in industry to regenerate zeolites during the ETE process, with the problems of excessive material consumption, energy consumption and environmental pollution. To solve this problem, this paper proposed a method of using the ethylene product as regeneration gas to regenerate the zeolite and recover the desorbed gas. In this work, we adopted a multi-scale simulation method combining microscale apparent diffusivity and macroscale mass transfer models. Reactive force field and molecular dynamics (ReaxFF-MD) were used to obtain an equation for the diffusion coefficient versus temperature. The start-up of the twin-tower temperature and pressure swing adsorption (TPSA) process was simulated using Computational Fluid Dynamics (CFD). The TPSA process achieved an outlet water content below 5 ppm, fulfilling the process specifications.

Effect of tempering process on the structure and properties of M51/X32 bimetal band saw blade

Abstract The mechanical properties of band saw have a great influence on the cutting life. In this paper, through the systematic study of M51/X32 bimetallic band saw blade with different tempering temperatures and times, the metallography of the bimetallic band saw blade under different heat treatment conditions was analyzed. The mechanical properties of the bimetallic band saw blade under different conditions were tested. The results showed that the carbide in the metallography of the tooth tip was uniform and tended to be spherical when tempering at 600°C for five times. The mechanical properties such as hardness, tensile strength, and fatigue properties are well matched, which can extend the service life of bimetallic band saw blades and ensure the cutting quality.

Jet Array Design for the Physical Tempering Process of Ultrathin Glass

Jet Array Design for the Physical Tempering Process of Ultrathin Glass

Effect of NaCl on the structure and digestive properties of heat-treated myofibrillar proteins

During meat processing, the quality of food and structure of meat proteins are affected by different processing technologies and addition of raw and auxiliary materials. Different meat products are treated with varying processing temperatures and NaCl content, changing the protein molecular structure. This study aimed to determine impact of heating temperature (40–115 °C) and NaCl concentration (0–0.8 M) on the oxidation, structure, and digestibility of beef myofibrillar proteins. The results revealed that high temperatures and NaCl concentration of 0.4–0.8 M caused the salting-out effect, leading to a decrease in solubility. The oxidative denaturation of proteins leads to increased protein aggregation. Consequently, structural changes of myofibrillar proteins, and the digestive enzymes are unable to recognize specific sites, which reduces the digestibility of the proteins. The findings of this study revealed that heating beef myofibrillar proteins at 85 °C and 0.4 M NaCl substantially improved its digestibility to 85.66 %.

Deposition, microstructure and hardness of AlCoCrFeNi2.1 eutectic high entropy alloy coatings by cold spray, HVOF, and plasma spray

Eutectic high entropy alloys (EHEAs) are reported to exhibit excellent mechanical properties which are useful for coating applications. In this study, we have produced AlCoCrFeNi2.1 EHEA coatings using the cold spray, high velocity oxygen-fuel (HVOF), and plasma spray techniques and compared their bonding characteristics, microstructure and hardness. We observe body-centered cubic (bcc) to face-centered cubic (fcc) phase transformations in all the types of coatings. The high processing temperatures in HVOF and plasma sprays lead to the segregation and depletion of Al from Ni-and Al-rich bcc/B2 regions and form Al2O3. In the cold sprayed coatings, we observe that a fraction of lamellar microstructure is preserved after cold spraying. Regarding the hardness, the cold sprayed coatings exhibit hardness in the range of 440–498 HV due to high work hardening and low oxygen content below 4 at.%; the HVOF coatings show the hardness in the range of 380–556 HV due to the effects of oxide dispersion strengthening with 13–28 at.% oxygen; The plasma sprayed coatings exhibit the hardness in the range 208–258 HV and 6.1–13.4 at.% oxygen. This study demonstrates the feasibility to produce thick and dense EHEA coatings using the above spray techniques, and the cold sprayed coatings exhibit relatively high hardness and low oxygen levels.

Sustainable lignocellulosic mediterranean biocomposites: Thermogravimetric analysis, mechanical performance, morphological and comparative study

Utilizing accessible agricultural waste to create sustainable materials is a potentially beneficial attempt toward developing better bio-products and a more sustainable socioeconomic environment. Accordingly, this work aims to investigate the mechanical performance enhancement and/or deteriorations as well as morphological and thermal characteristics of certain Mediterranean lignocellulosic fiber composites considering various reinforcement conditions. Several composites of polypropylene pomegranate peel powder (PG) and short corn leaves (CL) were designed and fabricated with various reinforcement conditions to demonstrate their potential characteristics. Moreover, the experimental results of this work were compared with commonly used fiber/PP composites worldwide to demonstrate their appropriateness for producing bio-based composites suitable for various applications. The investigations included determining the tensile strength, tensile modulus, elongations at break properties under various reinforcement conditions, as well as revealing the thermal properties of the fibers to expose their suitability for such bio-based composites to improve utilizing such available waste of fibers into useful materials for various mechanical components. Thermal gravimetric analysis (TGA) and derivative thermal gravimetric analysis (DTG) were also carried out to examine thermal stability and to distinguish the maximum degradation temperature (Tmax) of the cellulosic fiber compositions. The outcomes revealed that both types of fibers, pomegranate peel powder and short corn leaves, had positive impacts on the tensile strength and modulus of PP regardless of the weight percentage. The 40 wt% of corn leaves displayed the best improvement in the strength (+29 %) and modulus (+17 %). The 40 wt%, CL-PP reached tensile modulus of 1589.5 MPa, whereas the best modulus for the PG-PP was 1399.4 MPa at 30 wt%. When comparing the tensile strength and modulus of PG/PP and CL/PP composites with those reported in the literature, these composites demonstrated superior tensile properties relative to thirteen other types documented. Regarding the thermal analysis, PG and CL fibers could withstand the processing temperature without degradation, which enables them to be feasibly utilized in bio-based composites to enhance the development of green bio-products. When it comes to the morphological analysis, it was observed that there was a good adhesion between PP and CL, which interprets the enhancement tensile properties.

Including detailed chemistry features in the modeling of emerging low-temperature reactive flows: A review on the application to diluted and MILD combustion systems

Developing and optimizing new reactive systems with carbon-neutral fuels like biofuels, e-fuel, hydrogen, or ammonia is crucial for sustainable energy. This requires advanced technologies capable of fuel flexibility, high efficiency, and minimal pollutant emissions. However, these energy carriers still produce pollutants, especially NOx. To address this, engineers aim to lower combustion process temperatures by adopting different strategies such as burned gas recirculation, staging or increasing the air-to-fuel ratio. Yet, lean flames, though effective at emission reduction, are prone to instability and extinction, posing safety and mechanical risks. Emerging technologies like MILD Combustion, based on burned gas recirculation and reactant dilutions offer interesting solutions. The review article begins by synthesizing experimental studies and numerical simulations of MILD turbulent combustion. It then explores fundamental phenomena specific to diluted combustion (where MILD regimes are included as sub-sets), including autoignition and flame propagation. Using high-fidelity simulations and advanced experiments, it examines flow and mixing roles in reactive zones stabilization. Moving forward, the review paper addresses the inclusion of detailed chemical properties in modeling turbulent combustion systems. Scientific challenges revolve around modeling the intricate interactions between combustion chemistry and flow turbulence while maintaining computational efficiency compatible with industrial constraints. To address this, various simplified chemistry methods – such as reduced, tabulated, or optimized chemistry – have been developed. Additionally, turbulence/chemistry coupling modeling remains unresolved in simulations, with three main routes – geometrical, statistical, or reactor-based approaches – available for turbulent combustion modeling. The state-of-the-art in simplified chemistry and turbulent combustion modeling for low-temperature regimes is then focused on capturing MILD regimes, where there is a crucial impact of dilution by burnt gases, heat transfer, and turbulence mixing on the chemical flame structure. Recent advancements enabled by machine learning and deep learning algorithms are also highlighted. Lastly, the article underscores the critical need for data to validate models, emphasizing the importance of scale-bridging experiments.

Thermal decomposition temperature-dependent bonding performance of Ag nanostructures derived from metal–organic decomposition

In wide-bandgap semiconductor power device packaging, die bonding refers to attaching the die to substrate. Thereby, the process temperature of Ag sintering for the die bonding should be low to prevent damage to fragile dies. Herein, an organic-free strategy using Ag nanostructures derived from the thermal decomposition of metal–organic decomposition (MOD) was proposed to achieve low-temperature bonding. Significant effects on bonding performance were determined by the thermal decomposition temperature, which in turn determined the organic content and sintering degree of Ag nanostructures. At a low thermal decomposition temperature of 160 °C, incomplete decomposition resulted in high organic content in the Ag nanostructures, causing large pores inside the Ag joints owing to the generation of gaseous products. Owing to the Ag particles with naked surfaces and wide size distribution, the Ag nanostructure obtained at 180 °C showed an excellent bonding performance, resulting in a high shear strength of 31.1 MPa at a low bonding temperature of 160 °C. As the thermal decomposition temperature was 200 °C, sintering among Ag particles increased the particle size, resulting in a reduction of surface energy and driving force for sintering. We think that uncovering this underlying mechanism responsible for the bonding performance will promote the application of Ag MOD in the die bonding of WBG power devices.Graphical abstract

Co-fermentation of hot-pressed rapeseed meal with multiple strains and cellulase: Evaluating changes in protein quality and metabolite profiles

Hot-pressed rapeseed meal (RSM) is rich in resources, but high fiber content and low protein utilization result from high temperature processing. This study aims to obtain a high-quality protein source through the fermentation of hot-pressed RSM. We used Plackett-Burman and Box-Behnken designs to optimize parameters for solid state fermentation with Bacillus subtilis strain 5, Saccharomyces cerevisiae, and Aspergillus niger. Under the optimum conditions, the synergistic fermentation with multiple strains and cellulase significantly reduced anti-nutritional factors in hot-pressed RSM, while also increasing protein solubility, digestibility and extraction rate by 466.80%, 45.56% and 816.44%, respectively. 77 enzymes secreted by fermentation strains were identified in fermented hot-pressed RSM, which were mainly involved in the metabolism of proteins, peptides and amino acids (28.6%), energy (28.5%), carbohydrates (13.0%) and lipids (5.2%). Non-targeted metabonomics identified 1245 differential metabolites, with the top five being peptides and amino acids, lipids, heterocyclic compounds, alkaloids, and organic acids. These differential metabolites enhanced the nutritional, functional and flavor properties of fermented hot-pressed RSM. Among them, 702 differential metabolites were enriched into metabolic pathways, with the largest number of pathways associated with amino acid metabolism. These results hold important implications for understanding the quality of fermented hot pressed RSM.

Flavor properties of post-heated fermented milk revealed by a comprehensive analysis based on volatile and non-volatile metabolites and sensory evaluation

Existing research on the post-heating processing of fermented milk has primarily focused on single post-heating treatments and the texture, while research on how changes in metabolites during different post-heating treatments affect flavor and sensory properties is limited. This study investigates the changes in volatile metabolites in fermented milk treated at different post-heating temperatures to determine the characteristic aroma types and analyzes the changes in non-volatile metabolites associated with aroma-active compounds or their precursors to clarify the causes of the altered flavor and sensory properties. The results showed that in the 65°C and 75°C treatments, 63 volatile compounds were produced by Strecker degradation, lipid oxidation and esterification to produce ketones and aldehydes. Significantly higher odor activity values for 2,3-butanedione, hexanoic acid, and esters and significantly lower odor activity values for 2-heptanone enhanced the frankincense odors and creaminess of the post-heated fermented milk. With temperatures increasing to 95°C, the increased ketones were primarily 2-heptanone, 2-nonanone, and 2-undecanone that originated from the oxidative decomposition of unsaturated phospholipids at high temperatures. The Maillard reaction of dipeptides produces nitrogenous heterocycles that trigger a caramelized flavor, while organic acids interact with proteins to form complexes that produce astringent flavors. These increase the oxidative off-flavors and reduce the overall palatability. These findings provide a scientific basis for optimizing the post-heating temperature process of fermented milk.

EFFECTS OF HEAT TREATMENT ON THE IMPROVED WEAR RESISTANCE OF SHOT BLAST MACHINE BLADE MATERIAL

The blade is a component of the shot blast machine that functions as a thrower of small steel balls (steel shot). During its use, the blade undergoes impacts and friction with the steel shot, causing wear and a shortened lifespan. The material typically used for blades is white cast iron. Hence, the blade must possess good hardness and wear resistance. This study analyzed the causes of blade failure and proposed solutions in the form of hardening and tempering treatments. The tests conducted in this research included composition analysis, metallographic examination, hardness testing, and wear resistance testing. Based on the composition and hardness tests, the failure was attributed to material composition and hardness not meeting the ASTM A532 standards. The solution to this failure involves heat treatment to achieve hardness that complies with ASTM A532 standards, specifically hardening at 900°C for 40 minutes, followed by cooling with oil as the quenching medium. Then, the tempering process was carried out at 200°C, 250°C, and 300°C with holding times of 80 and 120 minutes. Post-heat treatment, the optimal hardness value, and the lowest wear rate were obtained at a tempering temperature of 300°C with a holding time of 120 minutes. The hardness value of the blade material under these conditions reached 63.8 HRC, and the wear rate was 1,21E-04 mm3.kg-1.m-1. A low wear rate indicates that the material has high wear resistance.