jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Surya Prakash Tiwari | Robert L. Thompson | Wei Shi | Nicholas Siefert | David Hopkinson | Janice A. Steckel
Source: ChemRxiv (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

Foaming Prediction in Pure Liquids from Dimensionless Numbers Inspired by the Theory of Fluid Behavior for Drops Foaming prediction is essential for the rational selection of materials and design processes in diverse industries ranging from bioprocesses to gas processing facilities. Existing foaming prediction models lack the generality needed for a wide range of materials and overlook the foaming in pure liquids. This work introduced a novel approach to predict foaming in pure liquids based on their density, surface tension, and viscosity, calculated through Reynolds (Re) and Ohnesorge (Oh) numbers. A foaming prediction map, representing a plot between these numbers, was constructed by leveraging the theory governing the behavior of fluid drops. This map delineated distinct non-foaming regions alongside a foaming region, serving as a binary classifier for foaming predictions. The map was fitted and validated with shake test experiments on 47 liquids, demonstrating effective predictions, except for a specific region characterized by small Oh and large Re numbers. This region corresponded to relatively low foam stability and high turbulence, making predictions challenging for liquids in this category. Dimensionless quantity ; Surface tension ; Materials science ; Weber number ; Binary number ; Viscosity ; Reynolds number ; Generality ; Thermodynamics ; Mechanics ; Turbulence ; Mathematics ; Composite material ; Physics Pickering emulsions and particle stabilization ; Heat and Mass Transfer in Porous Media ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ben Zhong Tang | Ping Shen | Yu Xiong | Tianjing Wu | Gang Wang | Yucheng Wang | Liping Zhang | Han Yang | Wei He | Jian Du | Xuedong Wei | Siwei Zhang | Zijie Qiu | Weijie Zhang | Zheng Zhao
Source: ChemRxiv (preprint, 2023-03-02)
Field: Materials Science — Materials Chemistry

Bright, photostable, and long-circulating NIR-II nanoparticles for whole-process monitoring and evaluation of renal transplantation Kidney transplantation is the gold standard for the treatment of end-stage renal disease (ESRD). However, the scarcity of kidneys has caused more and more ESRD patients being stuck on the waiting-list for surgery. Improving the survival rate of kidney grafts as much as possible is not only responsible for patients, but also an alternative solution to kidney shortage. Thus, rapid diagnosis and timely management of surgical complications are of vital importance to the success of renal transplantation but lack efficient methods and technologies. Herein, a fluorescence technology based on bright, photostable, and long-circulating aggregation-induced emission (AIE) active NIR-II nano contrast agent for the whole-process monitoring and evaluation of renal transplantation has been reported. The outstanding optical property and long-circulating characteristic of the AIE NPs help to achieve renal angiography in the kidney retrieval surgery, donor kidney quality evaluation before transplantation, diagnosing vascular complications, and assessment of renal graft reperfusion after revascularization, which considerably outperforms the clinically approved indocyanine green (ICG). Medicine ; Transplantation ; Economic shortage ; Kidney transplantation ; Kidney ; Revascularization ; Kidney disease ; Surgery ; Intensive care medicine ; Urology ; Internal medicine Nanocluster Synthesis and Applications ; Neurological Complications and Syndromes ; Organ Donation and Transplantation
Authors: Ünsal Aybek | Lütfü Namlı | Mustafa Özbey | Bekir Doğan
Source: Thermal Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Artificial neural network assisted multi-objective optimization of a methane-fed DIR-SOFC system with waste heat recovery The main purpose of this study is to enhance the performance of solid oxide fuel cell systems. For this purpose, a mathematical model of a direct internal reforming (DIR) methane-fed solid oxide fuel cell system with waste heat recovery was designed in the engineering equation solver program. We optimised the performance of the solid oxide fuel cell using a genetic algorithm and TOPSIS technique considering exergy, power, and environmental analyzes. An ANN working with the Levenberg-Marquardt training function was designed in the MATLprogram to create the decision matrix to which the TOPSIS method will be applied. According to the power optimization, 786 kW net power was obtained from the system. In exergetic optimization, the exergy efficiency was found to be 57.6%. In environmental optimization, the environmental impact was determined as 330.6 kgCO2/MWh. According to the multi-objective optimization results, the exergy efficiency, the net power of the solid oxide fuel cell system, and the environmental impact were 504.1 kW, 40.08%, and 475.4 kgCO2/MWh. Exergy ; Solid oxide fuel cell ; Process engineering ; TOPSIS ; Exergy efficiency ; Multi-objective optimization ; Cogeneration ; Genetic algorithm ; Electricity generation ; Environmental science ; Computer science ; Syngas ; Power (physics) ; Waste management ; Engineering ; Thermodynamics ; Chemistry Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Mary Otuosorochukwu NNYİA | Abiodun Oladipo | Ebube Victoria Anyaebosim | Onome Ejeromedoghene
Source: Journal of the Turkish Chemical Society Section A Chemistry (article, 2023-02-24)
Field: Chemistry — Inorganic Chemistry

The Preparation and Physicochemical Analysis of Local Black Soap from Coconut Oil and Plantain Peel Biochar Local black soap, produced from coconut oil and plantain peel biochar (alkaline source) was presented in this study since the agricultural raw materials gain significance in environmentally benign feedstocks for saponification reactions. The physicochemical analysis of the coconut oil and plantain peel biochar shows remarkable free fatty acid (FFA) and alkalinity contents respectively suitable for soap making. The physicochemical properties of the as-prepared local black soap demonstrated a good moisture content (15.1 %) showing that it can be stored for long periods without any water-associated deteriorations. The percentage of matter insoluble in water (4.4 %), matter insoluble in alcohol (12.3 %), FFA content (2.1 %), and pH (9) were found to be higher than the acceptable limit according to the Nigerian Industry Standards (NIS). Also, the total fatty matter (TFM) was higher (67.6 %) than the minimum acceptable level with a good foam height of 2.5 cm. The overall results show good soap properties and are suitable for domestic purposes. Saponification ; Raw material ; Biochar ; Coconut oil ; Pulp and paper industry ; Chemistry ; Organic matter ; Alkalinity ; Saponification value ; Moisture ; Water content ; Iodine value ; Food science ; Organic chemistry ; Pyrolysis Coconut Research and Applications ; African Botany and Ecology Studies ; Food Chemistry and Fat Analysis
Authors: Yuri Belotti | Doorgesh Sharma Jokhun | V. L. M. Valerio | Tie Chong | Chwee Teck Lim
Source: AIP Advances (article, 2023-05-01)
Field: Chemistry — Spectroscopy

Deep convolutional neural network accurately classifies different types of bladder cancer cells based on their pH fingerprints and morphology Non-invasive identification of different populations of cells at the single-cell level carries significant biomedical implications. We recently developed a novel fast and cost-effective method that, combining pH imaging and machine learning, enabled us to classify normal and cancer cells based on their different intracellular acidity. Here, we sought to capture and utilize intracellular structural features, known to undergo changes during cancer progression, in addition to intracellular pH patterns in order to make robust predictions. Leveraging both the biophysical and biochemical markers acquired via pH imaging with deep learning allowed us to classify cancer cells, at single-cell resolution, with very high accuracy. Specifically, the deep Convolutional Neural Network (CNN)-based strategy classified individual cells from the RT4 and J82 bladder cancer cell lines with an accuracy of 99.9%, compared to 94% achieved with our previously reported pH-based method. Convolutional neural network ; Deep learning ; Intracellular ; Artificial intelligence ; Cancer cell ; Computer science ; Bladder cancer ; Cancer ; Identification (biology) ; Cancer cell lines ; Artificial neural network ; Machine learning ; Pattern recognition (psychology) ; Chemistry ; Biology ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Leon Amadeus Varga | Hannah K. Frank | Andreas Zell
Source:  (article, 2023-05-25)
Field: Chemistry — Analytical Chemistry

Self-supervised Pretraining for Hyperspectral Classification of Fruit Ripeness The ripeness of fruit can be measured in a non-destructive way using hyperspectral imaging (HSI) and deep learning methods. However, the lack of labeled data samples limits hyperspectral image classification. This work explores self-supervised learning (SSL) as pretraining for HSI classification of fruit ripeness. Three state-of-the-art SSL methods, Sim-CLR, SimSiam, and Barlow Twins are implemented, and augmentation techniques for HSI are developed. A 3D-2D hybrid convolutional network is proposed to support the pretraining procedure. This model is evaluated against a ResNet-18 and a HS-CNN. The pretraining is evaluated on the fruit ripeness prediction task using the proposed second version of the DeepHS fruit data set. Besides comparing the classification performance of the pretrained models to only supervised training, the influence of the model architecture and size, pretraining method, and augmentations for SSL is investigated. This work shows that it is possible to transfer the ideas of SSL to HSI. It is possible to extract essential features in an unsupervised manner via this pretraining. Pretraining stabilizes classifier training and improves the classifier performance. Further, it can partially compensate for the need for large labeled data sets in HSI classification. Ripeness ; Artificial intelligence ; Hyperspectral imaging ; Pattern recognition (psychology) ; Classifier (UML) ; Computer science ; Convolutional neural network ; Contextual image classification ; Training set ; Transfer of learning ; Deep learning ; Machine learning ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Yangshuai Wang | Shashwat Patel | Christoph Ortner
Source: arXiv (Cornell University) (preprint, 2023-11-03)
Field: Materials Science — Materials Chemistry

A Theoretical Case Study of the Generalisation of Machine-learned Potentials Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challenge for their generalization to a broader range of systems outside the training set. Nevertheless, MLIPs have demonstrated impressive accuracy in predicting forces and energies in simulations involving intricate and complex structures. In this paper we aim to take steps towards rigorously explaining the excellent observed generalisation properties of MLIPs. Specifically, we offer a comprehensive theoretical and numerical investigation of the generalization of MLIPs in the context of dislocation simulations. We quantify precisely how the accuracy of such simulations is directly determined by a few key factors: the size of the training structures, the choice of training observations (e.g., energies, forces, virials), and the level of accuracy achieved in the fitting process. Notably, our study reveals the crucial role of fitting virials in ensuring the consistency of MLIPs for dislocation simulations. Our series of careful numerical experiments encompassing screw, edge, and mixed dislocations, supports existing best practices in the MLIPs literature but also provides new insights into the design of data sets and loss functions. Generalization ; Consistency (knowledge bases) ; Computer science ; Context (archaeology) ; Set (abstract data type) ; Process (computing) ; Range (aeronautics) ; Series (stratigraphy) ; Key (lock) ; Dislocation ; Algorithm ; Machine learning ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics ; Engineering ; Mathematical analysis ; Geology Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Ferroelectric and Negative Capacitance Devices
Authors: Ben Nengjun | Oleg Vytyaz | Xin Jin | R. S. Hrabovs’kyi | Б. В. Копей
Source: Nafta-Gaz (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Failure analysis during the operation of offshore oil and gas structures Failures caused by offshore oil and gas structures operations are investigated. This work is based on the description and analysis of real case studies of accidents on offshore stationary and floating platforms; it combines foundational knowledge and current research on the latest developments in the field. It was shown that strength characteristics of offshore reinforced concrete and steel elements change during operation and cause the accumulation of defects and damages. It was established that corrosive wear, corrosion-mechanical processes, and crack-like defects are the decisive causes of element failure. It was shown that up to 60–75% of all damages to and failures of offshore engineering facilities' steel structures occur due to the corrosion-mechanical influence of an aggressive environment and force loads. This means that issues of corrosion-mechanical failure of such structures have become an industrial-scale problem. It thus allows us to draw the following conclusions: improvement of steel offshore drilling platforms (ODPs) maintenance system involves the development of new models and methods of managing the operational reliability of these structures, aimed at making decisions that take into account the crack resistance and fatigue-corrosion strength of steel ODPs in contact with the corrosive-active environment. Only on the basis of such scientifically and economically grounded models can rational strategies be shaped for carrying out revisions of the ODPs technical condition, ensuring the necessary level of their reliability during the operation period. This investigation can be very helpful to improve the design and construction of more reliable and durable offshore stationary and floating platforms. Submarine pipeline ; Corrosion ; Damages ; Reliability (semiconductor) ; Corrosion fatigue ; Work (physics) ; Offshore oil and gas ; Fossil fuel ; Offshore drilling ; Engineering ; Forensic engineering ; Petroleum engineering ; Geotechnical engineering ; Materials science ; Mechanical engineering ; Law Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Engineering and Environmental Studies
Authors: Yusuf Syaril Alam | Pratiwi Pudjiastuti | Saipul Maulana | Nur Rahmayanti Afifah | Fahimah Martak | Arif Fadlan | Tutik Sri Wahyuni | Syukri Arief
Source: Indonesian Journal of Chemistry (article, 2023-08-15)
Field: Chemistry — Organic Chemistry

Synthesis and Antidiabetic Evaluation of &lt;i&gt;N&lt;/i&gt;’-Benzylidenebenzohydrazide Derivatives by &lt;i&gt;In Silico&lt;/i&gt; Studies Two new of N’-benzylidenebenzohydrazide (NBB) derivatives were successfully synthesized and yielded 50–58%. FTIR, ESI-MS, 1H-NMR and 13C-NMR were used to investigate the characteristic of NBB derivates. The structure and relationship of NBB derivatives into α-glucosidase and α-amylase as good targets for diabetes treatment were evaluated using in silico screening. Molecular Mechanics-Poisson Boltzmann/Generalized Born Surface Area (MM-PB/GBSA) was used to calculate the free binding energy (ΔGbind (MM-GBSA)) of NBB to α-glucosidase and α-amylase receptors showed that the results of −0.45 and −20.79 kcal/mol respectively. In the ortho position, NBB derivatives exhibited electron donating groups (EDG like -OCH3, -OH and -Cl with binding free energies of −21.94, −6.71 and 21.94, respectively, and acarbose, a native ligand energy of 32.62 kcal/mol. In addition, the binding free energy of N-2-(-OCH3, -OH and -Cl)-NBB to the α-amylase receptor showed the number of −39.33, −43.96, −42.81, respectively and −46.51 kcal/mol in comparing with a native ligand. As a result, it was found that all the NBB derivatives were able to interact with several amino acids in the α-glucosidase cavity as well as the native ones, including Ala281, Asp282, and Asp616. NBB and native ligand showed similar interaction between α-amylase with Gly110 amino acid residue. Chemistry ; Ligand (biochemistry) ; In silico ; Amino acid ; Stereochemistry ; Molecular mechanics ; Acarbose ; Medicinal chemistry ; Receptor ; Molecular dynamics ; Biochemistry ; Enzyme ; Computational chemistry Synthesis and biological activity ; Natural Antidiabetic Agents Studies ; Click Chemistry and Applications
Authors: Agus Arip Munawar | Hizir Hizir | Syaifullah Muhammad | Yusmanizar Yusmanizar
Source: Mathematical Modelling and Engineering Problems (article, 2023-06-29)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Patchouli Oil Adulteration Using Support Vector Machine Classification and Discriminant Analysis with Near-Infrared Spectroscopy Patchouli oil (PO) is a valuable commodity in the global market due to its numerous pharmaceutical properties.Consequently, adulteration of pure patchouli oil with other oil substances has become a prevalent issue.This study aims to develop rapid and nondestructive classification models for detecting patchouli oil fraudulence using support vector machine classification (SVMC) and discriminant analysis (DA) based on nearinfrared (NIR) spectroscopic data.Pure PO was adulterated with oleoresin oil in proportions of 75:25 and 50:50 to create the adulterated samples.A total of 40 samples were used for model calibration, and 9 samples were employed for validation.NIR spectra covering a wavelength range of 1000-2500 nm were collected for all samples and preprocessed using the multiplicative scatter correction algorithm.Classification models based on SVMC and DA were developed using principal component analysis.Results demonstrated that pure patchouli oil and oleoresin oil could be accurately classified, while adulterated PO with a 50:50 proportion was detected with a maximum accuracy of 87%, and the 75:25 proportion yielded a detection accuracy of 97%.These findings suggest that combining NIR spectroscopy with appropriate classification models can efficiently detect patchouli oil adulteration in a rapid and non-destructive manner, offering a promising method for quality control in the industry. Patchouli ; Linear discriminant analysis ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Infrared spectroscopy ; Quadratic classifier ; Principal component analysis ; Mathematics ; Chromatography ; Computer science ; Chemistry ; Essential oil ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ajith Kumar | Kulkarni Madhurima | Athi N. Naganathan | Pramodh Vallurupalli | Ashok Sekhar
Source: Methods (article, 2023-08-20)
Field: Chemistry — Spectroscopy

Probing excited state 1Hα chemical shifts in intrinsically disordered proteins with a triple resonance-based CEST experiment: Application to a disorder-to-order switch Intrinsically disordered proteins ; Chemistry ; Excited state ; Nuclear magnetic resonance spectroscopy ; Biomolecule ; Intermolecular force ; Chemical shift ; Magnetization transfer ; Nuclear magnetic resonance ; Chemical physics ; Molecule ; Physics ; Magnetic resonance imaging ; Stereochemistry ; Atomic physics ; Biochemistry Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies ; Protein Structure and Dynamics
Authors: Hasan M. Sayeed | Wade Smallwood | Sterling G. Baird | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-12-28)
Field: Materials Science — Materials Chemistry

NLP meets Materials Science: Quantifying the presentation of materials data in scientific literature The recent and sudden emergence of Large Language Models have profoundly changed the landscape around how we approach and interact with information. Materials Science, given its highly complex and multifaceted nature, is a space we intend for Natural Language Processing to absolutely flip the script related to progress, learning, and especially new materials discovery, as a result of enhanced data accessibility. We explore the underlying patterns and structures of data expression across a number of randomly selected materials science papers, annotating relevant data by type (category) and source (channel) as a starting point to future Materials Science specific information extraction and LLM development. Computer science ; Presentation (obstetrics) ; Point (geometry) ; Data science ; Information extraction ; Natural language processing ; Space (punctuation) ; Artificial intelligence ; Information retrieval Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Raí Felipe Pereira Junio | Douglas Santos Silva | Lucas de Mendonça Neuba | J.Ch. Fontes | Artur Camposo Pereira | Sérgio Neves Monteiro | Lúcio Fábio Cassiano Nascimento
Source: Journal of Engineering Research (review, 2023-11-28)
Field: Materials Science — Polymers and Plastics

PROPERTIES AND APPLICATIONS OF COMPOSITES REINFORCED WITH NATURAL FIBERS – A BRIEF REVIEW The current concern on the environmental appeal provides a search for the development of eco-friendly materials that are applied in the most diverse areas of knowledge. In this scenario, natural lignocellulosic fibers (NLFs) are highlighted, because in comparison to conventional synthetic fibers they have several advantages, such as lower density, biodegradability and abundant availability, cause low damage to equipment. Additionally, their wastes do not pollute the environment. These characteristics make them a promising alternative for use as reinforcement in composite materials. There is a growing interest on the part of the academic community in investigating the properties presented by composites reinforced with natural fibers (CRNFs), thus generating a high number of published works related to this topic. Various methodologies are investigated with the aim of causing a significant improvement in the properties presented by the CRNFs, such as fiber surface treatments, manufacturing processes or processing temperatures. The properties presented by these composites are directly dependent on the matrix, the fiber reinforcement and the interface region between these phases. CRNFs are growing in number and degree of applications, being observed in different areas such as civil construction, naval, military, medical, fashion, automobile and several others, where all of them need materials that exhibit exceptional properties allied to low weight. The present work intends to present an up dated brief review on CRNFs, where due emphasis are given to the manufacturing methods, properties and main applications for these materials. Composite material ; Materials science ; Natural (archaeology) ; Natural fiber ; Composite number ; Geology Natural Fiber Reinforced Composites