jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hery Tri Waloyo | Agus Mujianto | Richie Feriyanto
Source: Journal of Energy Mechanical Material and Manufacturing Engineering (article, 2024-11-21)
Field: Materials Science — Polymers and Plastics

Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network As the leading global producer of palm oil, Indonesia encounters substantial environmental challenges arising from the waste generated by empty palm oil fruit bunches (EPOFB). This research aims to develop an accurate Artificial Neural Network (ANN) model to predict the tensile strength of EPOFB fiber-reinforced composites. The method involves two types of ANN, namely Radial Basis Function (RBF) and Backpropagation, with testing using variations in immersion time, volume fraction, and length of EPOFB fibers. The research results show that both ANN models can predict tensile strength with a Mean Absolute Error (MAE) below 10%. However, the Backpropagation ANN shows superior performance with a training MAE of 0.0078 and a testing MAE of 0.45, compared to the RBF ANN, which has a training MAE of 0.371 and a testing MAE of 0.53. In conclusion, ANN Backpropagation is superior in prediction accuracy and characterization efficiency of EFB fiber-reinforced composites, offering an economical solution and supporting sustainable palm oil waste management. Ultimate tensile strength ; Composite number ; Palm oil ; Fiber ; Composite material ; Materials science ; Artificial neural network ; Palm ; Computer science ; Machine learning ; Food science ; Chemistry ; Physics Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Textile materials and evaluations
Authors: Deepti Bhusal | Shakya Wije Munige | Zongkai Peng | Zhibo Yang
Source: ChemRxiv (preprint, 2024-12-20)
Field: Chemistry — Spectroscopy

Exploring Single-probe Single-Cell Mass Spectrometry: Current Trends and Future Directions The Single-probe is a multifunctional device that can be coupled to mass spectrometry (MS) for molecular analysis of microscale samples, such as single cells, tissue slices, and multicellular spheroids, under ambient conditions. In Single-probe single cell MS (SCMS) studies, this technique leverages direct sampling and nanoelectrospray ionization (nanoESI), enabling the analysis of cells in their native environments without the need for extensive sample preparation. The Single-probe SCMS techniques have been used for a variety of different studies at the single-cell level, including semiquantitative profiling of metabolites, quantification of target molecules, cell-cell interactions, and incorporated chemical reactions for extended molecular analysis. Due to its miniaturized design and versatility, the Single-probe has been utilized for MS imaging (MSI) of tissue sections and investigation of extracellular molecules in live single spheroids. This review summarizes recent advancements in Single-probe-based techniques and their applications, with a focus on SCMS studies, emphasizing their potential utility in advancing MS methodologies in microscale bioanalysis. Mass spectrometry ; Current (fluid) ; Single-cell analysis ; Single use ; Chemistry ; Engineering ; Cell ; Chromatography ; Process engineering ; Electrical engineering Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Advanced Proteomics Techniques and Applications
Authors: Atajanova Zubayda Suleymanovna | Tanirbergenov Bazarbay | Tanirbergenova Zaripa
Source: American Journal Of Applied Science And Technology (article, 2024-03-01)
Field: Chemistry — Physical and Theoretical Chemistry

THE USE OF NEW PEDAGOGICAL TECHNOLOGIES IN TEACHING THE TOPIC OF AMINES IN HIGHER EDUCATIONAL INSTITUTIONS This article explores the use of new pedagogical technologies in higher education to teach the topic of amines. It examines the benefits of incorporating these technologies and provides practical guidance for educators looking to integrate them into their curriculum. Additionally, the article discusses the potential of technology to revolutionize chemistry education. In embracing innovative teaching methods, educators can empower students to develop a profound understanding of amines and prepare them for success in their academic and professional pursuits. Curriculum ; Engineering ethics ; Emerging technologies ; Sociology ; Pedagogy ; Mathematics education ; Engineering ; Psychology ; Nanotechnology ; Materials science Various Chemistry Research Topics
Authors: Rashid Khan | Kavita Srivastava
Source: Journal for Research in Applied Sciences and Biotechnology (article, 2024-07-31)
Field: Materials Science — Polymers and Plastics

Nanocomposite Materials Based on High-Performance Polymers The use of high-performance polymers (HPPs) is a crucial factor in the modern field of engineering as a result of its high thermal stability, resistance to chemicals, and mechanical strength. But, their disadvantaged characteristics of low electrical/thermal conductivity, high melt viscosity, low barrier properties and difficulties in processing limit their use in the industrial application. The study focuses on the fabrication, production, and characterisation of nanocomposite systems using HPP matrices with nanoscale fillers. The graphene nanoplatelets, carbon nanotubes, and nano-clays were melt-mixed and solution-casted to evaluate the impact of the quality of dispersion, the interface bonds and the percentage loading of the filler on the enhancement of multi-domain results. It has been shown through experimental measurements that there are great improvements in tensile strength, modulus, thermal stability, electrical conductivity, and gas-barrier behaviour at an optimal loading of about 3 wt%. The characterisation through mechanical means showed that modulus and strength grew significantly and then fell off at higher loadings as a result of agglomeration. The thermal analysis demonstrated high degradation temperatures and crystal high crystallinity, whereas electrical studies demonstrated an improvement of conductivity by maximum three orders of magnitude. Tortuous diffusion pathways created by the fillers that were well dispersed reduced permeability significantly. The rule of mixtures, Halpin-Tsai and Maxwell-Eucken models modelling strategies were used to prove the trend and explain interphase behaviour. All in all, this paper emphasizes that scalable and cost-effective HPP-based nanocomposites require strategic filler selection, controlled dispersion, and interface engineering. The results provide a firm foundation to the development of multi-purpose materials that would be applicable in the aerospace, electronics, oil and gas and next-generation structural applications. Nanocomposite ; Thermal conductivity ; Polymer nanocomposite ; Ultimate tensile strength ; Interphase ; Polymer ; Modulus ; Filler (materials) ; Graphene Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Thermal properties of materials
Authors: Margaret Mars Brisbin | Matthew M. McIlvin | Damien B. Wilburn | Jaclyn K. Saunders | Natalie R. Cohen | Maya P. Bhatia | Elizabeth B. Kujawinski | Brian C. Searle | Mak A. Saito
Source:  (preprint, 2024-11-21)
Field: Chemistry — Spectroscopy

Exploration of Machine Learning-Generated Spectral Libraries for Data Independent Acquisition in Complex Ocean Metaproteomic Analyses Ocean metaproteomics provides valuable insights into the structure and function of marine microbial communities. Yet, ocean samples are challenging due to their extensive biological diversity that results in a very large number of peptides with a larg Metaproteomics ; Computer science ; False positive paradox ; Metagenomics ; Function (biology) ; Range (aeronautics) ; Artificial intelligence ; Biology ; Engineering Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Microbial Community Ecology and Physiology
Authors: Ingvars Vitenburgs | Niels R. Walet
Source: arXiv (Cornell University) (preprint, 2024-05-17)
Field: Materials Science — Materials Chemistry

Extended Coupled Cluster approach to Twisted Graphene Layers A study of correlation effects in twisted bilayer graphene, using the extended coupled cluster method, is presented. This approach considers both self-consistent mean-field and beyond mean-field contributions, and can describe phase transitions in such strongly correlated systems, without further inputs or assumptions. Detailed expressions and a suitable implementation for the method are developed. Combining modern tensor contraction techniques with singular value decomposition, the correlation effects are successfully described in a qualitative manner, including contributions from the short-range and long-range parts of the Coulomb interaction. The superconducting gap is found to be maximal at a twist angle of $θ_c = 1.00 °$ with a roughly equal combination of s-wave and f-wave components. Using BCS theory, the size of the gap corresponds to a critical temperature value of $T_\text{c}^\text{BCS} = 0.5$K. This matches qualitatively with experimental data. Within the limitation of the numerical truncations used, a novel candidate for the mechanism behind superconductive phases in twisted bilayer graphene is proposed. Graphene ; Cluster (spacecraft) ; Materials science ; Nanotechnology ; Computer science ; Computer network Graphene research and applications
Authors: Osman Mamun | Markus Bause | Bhuiyan Shameem Mahmud Ebna Hai
Source: arXiv (Cornell University) (preprint, 2024-01-11)
Field: Materials Science — Materials Chemistry

Accelerated Development of Multicomponent Alloys in Discrete Design Space Using Bayesian Multi-Objective Optimisation Bayesian optimization (BO) protocol based on Active Learning (AL) principles has garnered significant attention due to its ability to optimize black-box objective functions efficiently. This capability is a prerequisite for guiding autonomous and high-throughput materials design and discovery processes. However, its application in materials science, particularly for novel alloy designs with multiple targeted properties, remains limited. This limitation is due to the computational complexity and the lack of reliable and robust acquisition functions for multiobjective optimization. In recent years, expected hypervolume-based geometrical acquisition functions have demonstrated superior performance and speed compared to other multiobjective optimization algorithms, such as Thompson Sampling Efficient Multiobjective Optimization (TSEMO), Pareto Efficient Global Optimization (parEGO), etc. This work compares several state-of-the-art multiobjective BO acquisition functions, i.e., parallel expected hypervolume improvement (qEHVI), noisy parallel expected hypervolume improvement (qNEHVI), parallel Pareto efficient global optimization (parEGO), and parallel noisy Pareto efficient global optimization (qNparEGO) for the multiobjective optimization of physical properties in multi-component alloys. We demonstrate the impressive performance of the qEHVI acquisition function in finding the optimum Pareto front in 1-, 2-, and 3-objective Aluminium alloy optimization problems within a limited evaluation budget and reasonable computational cost. In addition, we discuss the role of different surrogate model optimization methods from a computational cost and efficiency perspective. Bayesian optimization ; Computer science ; Throughput ; Multi-objective optimization ; Pareto principle ; Subspace topology ; Mathematical optimization ; Function (biology) ; Machine learning ; Artificial intelligence ; Mathematics ; Wireless Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Fault Detection and Control Systems
Authors: Bohan Huang | Long Xue | Chaoyang Yin | Jing Li | Muhua Liu
Source: Journal of Food Process Engineering (article, 2024-08-28)
Field: Chemistry — Analytical Chemistry

Feature area size prediction method of spherical fruit based on projection transformation Abstract With the development of machine vision and spectral detection technology, online sorting of fruit internal and external quality has been developed rapidly. However, for spherical fruits, it is difficult to obtain full surface images during sorting, so it is difficult to accurately calculate the size of the surface defects and the ratio of defects to the full surface. In this paper, a full surface line scanning image acquisition device for spherical fruit is proposed. Based on this device, the line scanning hyperspectral image of spherical fruit is collected, and the original image is extracted by feature extraction and background removal. Next, the isometric projection image and the equivalent projection image of the feature image is obtained through cartography projection transformation; The number of feature pixels in the original feature image, the isometric projection image, the equivalent projection image, and the width of the original feature image are used as input parameters to predict the actual defect area with the help of the shallow neural network. In this paper, the equipment and method are verified using three test balls with different diameters and pasting different sizes of identification blocks at different positions on their surfaces. The experimental results show that the prediction accuracy R of the test set of the model is 0.9937, and the RMSE is 0.3391 cm 2 . It can be seen that the method has good prediction accuracy, which can provide a reference for the hyperspectral on‐line sorting method of external quality of spherical fruit. Practical application This method provides an effective solution for the quality sorting production line of spherical fruits. In addition to agricultural product quality testing and food quality testing, similar to the detection of industrial products such as ball balls, the scheme provided in this manuscript can also be used as one of the options. The method proposed in this manuscript is suitable for all kinds of line scanning equipment, including hyperspectral imager and laser profilometer. Transformation (genetics) ; Feature (linguistics) ; Projection (relational algebra) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Computer vision ; Mathematics ; Algorithm ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Ella Gale | Leo Lobski | Fabio Zanasi
Source: arXiv (Cornell University) (preprint, 2024-10-02)
Field: Chemistry — Physical and Theoretical Chemistry

Disconnection Rules are Complete for Chemical Reactions We provide a category theoretical framework capturing two approaches to graph-based models of chemistry: formal reactions and disconnection rules. We model a translation from the latter to the former as a functor, which is faithful, and full up to isomorphism. This allows us to state, as our main result, that the disconnection rules are sound, complete and universal with respect to the reactions. Concretely, this means that every reaction can be decomposed into a sequence of disconnection rules in an essentially unique way. This provides a uniform way to store reaction data, and gives an algorithmic interface between (forward) reaction prediction and (backward) reaction search or retrosynthesis. Disconnection ; Computer science ; Psychology ; Chemistry ; Philosophy ; Theology History and advancements in chemistry
Authors: A. A. Akinola | I. O. Nwaze | R. U. Owolabi
Source: Journal of Engineering Research (review, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Evaluating the Potential of Refractance WindowTM Drying Technology in Food Preservation: A Review of Current Research and Future Directions One significant issue facing developing nations is the preservation of agricultural products, especially during harvesting. Dehydration remains critical in food preservation as dried products are cost-effective, lightweight, and have an extended storage duration. However, traditional drying techniques are either time-consuming, energy-intensive, or degrading the product. In the quest for efficient and sustainable technologies to preserve and enhance the quality of agricultural produce, Refractance Window Drying (RWD) has emerged as a promising technique with vast potential that remains largely unexplored. This review introduces this novel technique, including its principles, unique advantages, efficacy compared to traditional drying methods, and potential application in dehydrating agro-products. By discussing the current challenges and future research directions of this innovative drying technique, this review contributes to the growing knowledge of advanced dehydration technologies and empowers the audience with practical insights for their adoption in the food industry. Current (fluid) ; Environmental science ; Biochemical engineering ; Engineering ; Electrical engineering Spectroscopy and Chemometric Analyses
Authors: Masahiro Kato
Source: arXiv (Cornell University) (preprint, 2024-06-13)
Field: Materials Science — Materials Chemistry

General Bayesian Predictive Synthesis This study investigates Bayesian ensemble learning for improving the quality of decision-making. We consider a decision-maker who selects an action from a set of candidates based on a policy trained using observations. In our setting, we assume the existence of experts who provide predictive distributions based on their own policies. Our goal is to integrate these predictive distributions within the Bayesian framework. Our proposed method, which we refer to as General Bayesian Predictive Synthesis (GBPS), is characterized by a loss minimization framework and does not rely on parameter estimation, unlike existing studies. Inspired by Bayesian predictive synthesis and general Bayes frameworks, we evaluate the performance of our proposed method through simulation studies. Bayesian probability ; Computer science ; Econometrics ; Artificial intelligence ; Economics Machine Learning in Materials Science ; History and advancements in chemistry
Authors: Alexandru Sover | Vasile Ermolai | A Mitchell | U Lafont | M Hoyska | C Semprimoschnig | G Scalet | S Valvez | P Reis | L Susmel | F Berto | A Ameen | A Takhakh | A Abdal-Hay | S Leist | D Gao | R Chiou | J Zhou | B Goo | C Hong | K Park | M Rafiee | R Farahani | D Therriault | A Kafle | E Luis | R Silwal | H Pan | P Shrestha | A Bastola | K Mustapha | K Metwalli | R Suhail | J Chen | G Amato | D Mccrum | S Kumar | A Arohi | I Sen | M Carlson | Y Li | N Sorimpuk | W Choong | B Chua | V Ermolai | A Sover | G Nag | A Irimia | T Liu | L Liu | C Zeng | Y Liu | J Leng | M Khalid | Z Arif | R Noroozi | A Zolfagharian | M Bodaghi | Q Ding | W Yan | K Yan | A Kyme | S Cheng | M Razzaq | J Gonzalez-Gutierrez | G Mertz | D Ruch | D Schmidt | S Westermann | D Costa | P Costa | M Gomes | A Chandrakar | P Wieringa | L Moroni | J Mano | S Hosseini | A Farrokhabadi | D Chronopoulos | K Martin | T Thornell | H Hanna | C Weiss | Z Mcclelland | A Sharma | A Singholi | J Carrell | G Gruss | E Gomez | A Lang | A Rai | A Nazir | O Gokcekaya | K Billah | O Ertugrul | J Jiang
Source: International Journal of Mechatronics and Applied Mechanics (article, 2024-03-27)
Field: Materials Science — Polymers and Plastics

MULTI-MATERIAL SHAPE MEMORY POLYMERS: DESIGN CONSIDERATIONS AND IMPLEMENTATION Shape memory polymers (SMP) are material systems capable of changing shape in response to external stimuli. By integrating multiple materials in the same system, the shape SMPs' can produce multiple shape/geometrical changes proportional to the number of constituents. Multi-material Fused Filament Fabrication (FFF) introduced new development possibilities regarding SMPs. However, materials integration is limited due to the affinity of polymeric materials. This research proposes a design framework adapted for multi-material printing for lowcompatibility polymers to solve some of these limitations. This way, using some design principles for FFF and multi-material printing can integrate two or more materials into the same component. In the end, the principles were used to print and test eight TPU/PLA-based SMSs. Shape-memory polymer ; Polymer ; Materials science ; Material Design ; Process engineering ; Computer science ; Composite material ; Engineering Polymer composites and self-healing
Authors: Farag M. Mosallam | Rana Elshimy
Source: Research Square (preprint, 2024-01-08)
Field: Materials Science — Materials Chemistry

RETRACTED: Eradication of Klebsiella pneumoniae pulmonary infection by silver Oxytetracycline Nano-structure Preprint ; Klebsiella pneumoniae ; Oxytetracycline ; Microbiology ; Square (algebra) ; Medicine ; Virology ; Antibiotics ; Biology ; Computer science ; Mathematics ; Escherichia coli ; World Wide Web ; Genetics Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Hüseyin Özkal Özsoy
Source: DergiPark (Istanbul University) (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Yapay Zekâ Destekli Araçların Kullanımıyla Endüstriyel Tasarım Sürecinde Sağlanan Disiplinler Arası Gelişmeler The integration of artificial intelligence (AI) into industrial design is revolutionizing traditional methods, particularly in areas such as rapid prototyping, sustainable material selection, and predictive analytics. AI-driven tools accelerate prototyping cycles, reduce reliance on physical models, and enable designers to predict product performance under real-world conditions. Additionally, AI enhances sustainability by analyzing extensive material databases, guiding decisions that balance durability, cost, and environmental impact. Its role in data-driven personalization allows mass production to meet individual user needs efficiently while minimizing waste. Moreover, AI fosters collaboration among interdisciplinary teams by providing real-time feedback and facilitating iterative improvements throughout the design process, ultimately empowering designers to achieve greater innovation and productivity. By seamlessly integrating these capabilities, AI is transforming design workflows across industries, improving both their speed and quality. This article delves into current academic literature to evaluate AI’s current role as a powerful tool in shaping the future of industrial design and offers suggestions for its effective application, alongside proposed areas for further research. Workflow ; Personalization ; Mass customization ; Sustainability ; Product (mathematics) ; Flexibility (engineering) ; Rapid prototyping ; Production (economics) Machine Learning in Materials Science ; Design Education and Practice ; Digital Transformation in Industry