jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vitória M. S. Freitas | Waldemir J. Paschoalino | Luis C. S. Vieira | Jussara M. Silva | Bruno C. Couto | Ângelo L. Gobbi | Renato S. Lima
Source: ACS Omega (article, 2024-08-15)
Field: Materials Science — Biomaterials

Sensitive Monitoring of the Minimum Inhibitor Concentration under Real Inorganic Scaling Scenarios Flow assurance is a long-term challenge for oil and gas exploration as it plays a key role in designing safe and efficient operation techniques to ensure the uninterrupted transport of reservoir fluids. In this regard, the sensitive monitoring of the scale formation process is important by providing an accurate assessment of the minimum inhibitor concentration (MIC) of antiscale products. The optimum dosage of antiscale inputs is of pivotal relevance as their application at concentrations both lower and higher than MIC can imply pipeline blockages, critically hindering the entire supply chain of oil-related inputs and products to society. Using a simple and low-cost impedimetric platform, we here address the monitoring of the scale formation on stainless-steel capillaries from its early stages under real topside (ambient pressure and 60 °C) and subsea (1000 psi and 80 °C) sceneries of the oil industry. The method could continuously gauge the scale formation with a sensitivity higher than the conventional approach, i.e., the tube blocking test (TBT), which proved to be mandatory for avoiding misleading inferences on the MIC. In fact, whereas our sensor could entail accurate MICs, as confirmed by scanning electron microscopy, TBT suffered from negative deviations, with the predicted MICs being lower than the real values. Importantly, the impedance measurements were performed through a hand-held, user-friendly workstation. In this way, our method is envisioned to deliver an attractive and readily deployable platform to combat the scale formation issues because it can continuously monitor the salt precipitation from its early stages and yield the accurate determination of MIC. Flow assurance ; Subsea ; Process engineering ; Scale (ratio) ; Microfluidics ; Computer science ; Petroleum engineering ; Sensitivity (control systems) ; Environmental science ; Biochemical engineering ; Engineering ; Nanotechnology ; Chemistry ; Marine engineering ; Materials science Calcium Carbonate Crystallization and Inhibition ; Minerals Flotation and Separation Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Qihui Lian | Yao Ying | Feng Jiang | Yinjiao Fu | Xin Luo | Lang Wu
Source: Journal of the American Ceramic Society (article, 2024-04-22)
Field: Materials Science — Ceramics and Composites

In situ study of microstructural evolution of borosilicate glass–sulfate mixture at high temperatures Abstract Microstructural evolution of borosilicate glass–sulfate (BaSO 4 or Na 2 SO 4 ) mixture was observed in situ using a high‐temperature confocal scanning laser microscope. The phase compositions and sulfate retention of the mixture at different temperatures were also examined. The results indicate that for the glass–BaSO 4 sample (abbreviated as BS), the borosilicate glass begins to melt at about 900°C, and the triangular BaSO 4 crystals grow obviously between 900°C and 1100°C. At temperatures above 1100°C, the BaSO 4 crystals gradually melt and decompose. For the glass–Na 2 SO 4 sample (abbreviated as NS), strip‐shaped BaSO 4 crystals begin to form at about 820°C after the borosilicate glass melting, and then gradually melt with further increasing temperature. When the temperature increases to 1000°C, the upper layer of the NS sample possesses Na 2 SO 4 , LiNaSO 4 , and BaSO 4 phases, while the lower layer possesses an amorphous phase. Moreover, the SO 3 retention in the BS sample remains almost unchanged with temperature increases from 900°C to 1100°C, and then decreases with further increasing temperature. The SO 3 retention retained in the NS sample decreases rapidly at temperatures above 900°C. These findings can provide a guidance for vitrifying sulfur‐rich high‐level liquid waste. Borosilicate glass ; Materials science ; Amorphous solid ; Vitrification ; Phase (matter) ; Analytical Chemistry (journal) ; Sulfate ; Scanning electron microscope ; Mineralogy ; Chemical engineering ; Composite material ; Crystallography ; Chemistry ; Metallurgy ; Chromatography Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production ; Building materials and conservation
Authors: Harsimran Kaur Mann | Mainak Mondal | Vivek Sah | Kenji Watanabe | Takashi Taniguchi | Akshay Singh | Aveek Bid
Source: npj 2D Materials and Applications (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Optical control of multiple resistance levels in graphene for memristic applications Abstract Neuromorphic computing has emphasized the need for memristors with non-volatile, multiple conductance levels. This paper demonstrates the potential of hexagonal boron nitride (hBN)/graphene heterostructures to act as memristors with multiple resistance states that can be optically tuned using visible light. The number of resistance levels in graphene can be controlled by modulating doping levels, achieved by varying the electric field strength or adjusting the duration of optical illumination. Our measurements show that this photodoping of graphene results from the optical excitation of charge carriers from the nitrogen-vacancy levels of hBN to its conduction band, with these carriers then being transferred to graphene by the gate-induced electric field. We develop a qualitative model to describe our observations. Additionally, utilizing our device architecture, we propose a memristive crossbar array for vector-matrix multiplications. Graphene ; Resistance (ecology) ; Control (management) ; Materials science ; Computer science ; Nanotechnology ; Biology ; Artificial intelligence ; Agronomy Graphene research and applications ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Bo Wen | William Stafford Noble
Source: Scientific Data (article, 2024-11-08)
Field: Chemistry — Spectroscopy

A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from nine different species. The dataset is based on a previously described benchmark but has been re-processed to ensure consistent data quality and enforce separation of training and test peptides. Benchmark (surveying) ; Computer science ; Mass spectrometry ; Proteomics ; Machine learning ; Artificial intelligence ; Chemistry ; Chromatography ; Geography Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: N Sagar | Suresh Kp | Ramanji RS | M. Bharath | Naveesh YB | Ravichandra | Archana CA
Source: International Journal of Advanced Biochemistry Research (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Revolutionizing potato crop management: Deep learning-driven potato disease detection with convolutional neural networks Potato cultivation is essential for ensuring global food security, yet it faces significant threats from diseases such as early blight and late blight, resulting in substantial yield losses. To address this challenge, integrated disease management strategies are pivotal, encompassing cultural practices, disease-resistant varieties, and timely fungicide applications. Recent advancements in artificial intelligence (AI), particularly in deep learning, hold promise for revolutionizing disease detection in agriculture. This study aims to contribute to these efforts by developing a highly accurate and efficient system for early disease detection in potato crops using deep learning techniques. Through the utilization of convolutional neural networks (CNNs), transfer learning, and data augmentation, the proposed model showcases significant potential in automating the identification and classification of potato leaf diseases. Rigorous experimentation and evaluation demonstrate that the proposed CNN model achieved an impressive accuracy of 97.82% in classifying potato early and late blight diseases. These findings underscore the efficacy of CNNs in agricultural disease management and highlight the transformative role of AI technologies in bolstering global food security efforts. Convolutional neural network ; Crop ; Deep learning ; Crop management ; Agronomy ; Disease management ; Agricultural engineering ; Computer science ; Artificial intelligence ; Environmental science ; Agroforestry ; Biology ; Disease ; Medicine ; Engineering ; Pathology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Cemre Avşar
Source: Kemija u industriji (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Recent Advances in Wastewater Treatment by Utilisation of Bio-waste-derived Hydroxyapatite Hydroxyapatite (HAp) is basically calcium phosphate and the main inorganic component in natural bone and teeth structure. Animal bones and eggshells are mass-generated bio-wastes, and their calcium-rich structure makes them suitable precursors for bioactive HAp synthesis through biomimetic mineralisation. Industrial wastewater discharge is a worldwide alarming problem due to high-capacity industrial-scale processes, and their adverse effects on human health and environment will be encountered long term. Adsorption is the most widely used separation process in wastewater treatment, in which HAp particles can be used as bio-adsorbents for wastewater treatment processes due to their bioactive nature. Utilisation of bio-waste-derived HAp particles for wastewater treatment applications would be a step towards sustainable development and circular economy concepts. It promotes the reuse and recycle rate of bio-wastes as bio-adsorbents through an effective waste management strategy and increases the circularity of the material in the production chain. This study offers a brief overview of articles published in 2023 on the use of bio-waste-derived HAp particles for wastewater treatment. Waste management ; Wastewater ; Sewage treatment ; Waste treatment ; Environmental science ; Engineering Advanced Nanomaterials in Catalysis ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Francesco Calise | Francesco Liberato Cappiello | Luca Cimmino | Massimo Dentice d’Accadia | Maria Vicidomini
Source: Energies (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

A Novel Layout for Combined Heat and Power Production for a Hospital Based on a Solid Oxide Fuel Cell This paper addresses the problem of the reduction in the huge energy demand of hospitals and health care facilities. The sharp increase in the natural gas price, due to the Ukrainian–Russian war, has significantly reduced economic savings achieved by combined heat and power (CHP) units, especially for hospitals. In this framework, this research proposes a novel system based on the integration of a reversible CHP solid oxide fuel cell (SOFC) and a photovoltaic field (PV). The PV power is mainly used for balancing the hospital load. The excess power production is exploited to produce renewable hydrogen. The SOFC operates in electrical tracking mode. The cogenerative heat produced by the SOFC is exploited to partially meet the thermal load of the hospital. The SOFC is driven by the renewable hydrogen produced by the plant. When this hydrogen is not available, the SOFC is driven by natural gas. In fact, the SOFC is coupled with an external reformer. The simulation model of the whole plant, including the reversible SOFC, PV, and hospital, is developed in the TRNSYS18 environment and MATLAB. The model of the hospital is calibrated by means of measured data. The proposed system achieves very interesting results, with a primary energy-saving index of 33% and a payback period of 6.7 years. Therefore, this energy measure results in a promising solution for reducing the environmental impact of hospital and health care facilities. Renewable energy ; Photovoltaic system ; Solid oxide fuel cell ; Natural gas ; Process engineering ; Payback period ; Automotive engineering ; Thermal power station ; Environmental science ; Power to gas ; Electricity generation ; Waste management ; Computer science ; Production (economics) ; Power (physics) ; Electrical engineering ; Engineering ; Economics ; Chemistry Advancements in Solid Oxide Fuel Cells ; Building Energy and Comfort Optimization ; Geothermal Energy Systems and Applications
Authors: Samson Damilola Fabiyi | Paul Murray | Jaime Zabalza | Christos Tachtatzis | Hai Vu | Trung‐Kien Dao
Source: IEEE Transactions on AgriFood Electronics (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification Hyperspectral imaging (HSI) has been reported to produce promising results in the classification of rice seeds. However, HSI data often require the use of dimensionality reduction techniques for the removal of redundant data. Folded linear discriminant analysis (F-LDA) is an extension of linear discriminant analysis (LDA, a commonly used technique for dimensionality reduction), and was recently proposed to address the limitations of LDA, particularly its poor performance when dealing with a small number of training samples which is a usual scenario in HSI applications. This article presents an improved version of F-LDA, exploring the feasibility of hybridizing a genetic algorithm (GA) and F-LDA for effective dimensionality reduction in HSI-based rice seeds classification. The proposed approach, inspired by the previous combination of GA with principle component analysis, is evaluated on rice seed datasets containing 256 spectral bands. Experimental results show that, in addition to attaining promising classification accuracies of up to 96.21%, this novel combination of GA and F-LDA (GA + F-LDA) can further reduce the computational complexity and memory requirement in the standalone F-LDA. It is worth noting that these benefits are not without a slight reduction in classification accuracy when evaluated against those reported for the standard F-LDA (up to 96.99%). Hyperspectral imaging ; Dimensionality reduction ; Linear discriminant analysis ; Pattern recognition (psychology) ; Reduction (mathematics) ; Principal component analysis ; Artificial intelligence ; Algorithm ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Thinesh Sharma Balakrishnan | Mohamed Thariq Hameed Sultan | Farah Syazwani Shahar | Suhas Yeshwant Nayak | Ain Umaira Md Shah | Tamer A. Sebaey | Adi Azriff Basri
Source: Iranian Polymer Journal (article, 2024-05-06)
Field: Materials Science — Polymers and Plastics

Hybridization of woven kenaf and unidirectional glass fibre roving for unsaturated polyester composite Abstract This is a study on the mechanical properties of kenaf/glass-reinforced polyester composites intended for use in structural profiles with a wall thickness by max. 6 mm. Mechanical properties such as tensile, compression, bending and interlaminar shear stress were investigated by comparing the hybrid variants with the pure fibreglass variant. According to the study, woven kenaf/unidirectional glass roving (WK/UG) alternate recorded the highest tensile properties among hybrid samples. It demonstrated a decrement of about 8.2% of the tensile strength (404.54 MPa) and 10.7% of tensile modulus (24.54 GPa) compared to conventional fibreglass samples. Alternating WK/UG samples demonstrated higher compressive strength (417.15 MPa) compared to other hybrid specimens, recording a slight decrease at 6.09% compared to pure fibreglass composites. The highest bending properties were also observed in hybrid alternate WK/UG samples among other hybrid laminates with only a decrement of 4.13% in modulus of rupture (456.33 MPa) and 1.9% in modulus of elasticity (14.49 GPa) when compared to the control specimen. The ILSS of hybrid composites 2WK/3UG/2WK (30.97 MPa) and WK/UG alternate (34.90 MPa) showed good agreement with the pure fibreglass (42.33 MPa) composites. Using SEM images, tensile fractured specimens were examined to comprehend composites’ failure mechanism and interfacial adhesion. Overall, woven kenaf/unidirectional glass roving alternate sequence is chosen as a potential alternative in developing structural profiles for moderate load-bearing structural applications. In contrast, 3WK/UG/3WK with a higher kenaf to glass ratio demonstrate potential in low load-bearing structural profile applications. Graphical abstract Kenaf ; Materials science ; Composite material ; Ultimate tensile strength ; Composite number ; Bending ; Glass fiber ; Young's modulus ; Polyester ; Compression (physics) ; Modulus ; Fiber Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Structural Load-Bearing Analysis
Authors: Joseph Melville | Vishal Yadav | Lin Yang | Amanda R. Krause | Michael Tonks | Joel B. Harley
Source: Computational Materials Science (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Anisotropic physics-regularized interpretable machine learning of microstructure evolution Misorientation ; Grain growth ; Anisotropy ; Isotropy ; Triple junction ; Abnormal grain growth ; Grain boundary ; Microstructure ; Materials science ; Potts model ; Statistical physics ; Monte Carlo method ; Work (physics) ; Condensed matter physics ; Artificial intelligence ; Geometry ; Physics ; Mathematics ; Computer science ; Optics ; Metallurgy ; Thermodynamics ; Statistics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: Dominik Geier | Markus Mailänder | Iain Whitehead | Thomas Becker
Source: Sensors (article, 2024-09-27)
Field: Chemistry — Analytical Chemistry

Non-Invasive Characterization of Different Saccharomyces Suspensions with Ultrasound In fermentation processes, changes in yeast cell count and substrate concentration are indicators of yeast performance. Therefore, monitoring the composition of the biological suspension, particularly the dispersed solid phase (i.e., yeast cells) and the continuous liquid phase (i.e., medium), is a prerequisite to ensure favorable process conditions. However, the available monitoring methods are often invasive or restricted by detection limits, sampling requirements, or susceptibility to masking effects from interfering signals. In contrast, ultrasound measurements are non-invasive and provide real-time data. In this study, the suitability to characterize the dispersed and the liquid phase of yeast suspensions with ultrasound was investigated. The ultrasound signals collected from three commercially available <i>Saccharomyces</i> yeast were evaluated and compared. For all three yeasts, the attenuation coefficient and speed of sound increased linearly with increasing yeast concentrations (0.0-1.0 wt%) and cell counts (R<sup>2</sup> > 0.95). Further characterization of the dispersed phase revealed that cell diameter and volume density influence the attenuation of the ultrasound signal, whereas changes in the speed of sound were partially attributed to compositional variations in the liquid phase. This demonstrates the ability of ultrasound to monitor industrial fermentations and the feasibility of developing targeted control strategies. Yeast ; Ultrasound ; Attenuation ; Saccharomyces cerevisiae ; Materials science ; Ultrasonic sensor ; Fermentation ; Phase (matter) ; Suspension (topology) ; Biomedical engineering ; Biological system ; Chemistry ; Biology ; Food science ; Medicine ; Biochemistry ; Mathematics ; Radiology ; Optics Spectroscopy and Chemometric Analyses ; Microbial Inactivation Methods ; Advanced Chemical Sensor Technologies
Authors: Altin Mele | Viviana Scognamiglio | Valeria Nocerino | Luca De Stefano | Arben Memo | Roberta G. Toro | Manuela Rossi | Francesco Baldassarre | Francesco Capitelli
Source: Crystals (article, 2024-10-18)
Field: Materials Science — Biomaterials

In-Depth Characterization of Natural Clays from Southeast Albania Clays have been exploited in the manufacture of diverse products from ceramics to paints, pharmaceuticals, plastics, cosmetics, and more. Thus, they can be used in many industrial applications, showing good adsorbent ability thanks to their lamellar structure, high cation exchange capacity, pore size distribution, and large surface area. For this reason, considerable attention has been paid to their in-depth characterization, for further integration in sectors such as biomedicine, construction, remediation, aerospace, and nanotechnology. For this aim, two samples of natural clays, ALO1 and PRE4, from the southeast part of Albania, were subject to a multi-methodological characterization, with the aim of addressing the use of such geomaterials in possible sensing applications. X-ray fluorescence analysis, morphological characterization of the samples, and energy-dispersive system spectroscopy pointed to an extreme mineralogical variety, with kaolinite in AL01 and montmorillonite in PRE4 as the most abundant phases. This fact was further confirmed by powder X-ray diffraction, showing a quartz content of 20%, a kaolinite content of 64%, and a muscovite content of 16% for ALO1; meanwhile, for PRE4, we found a content of quartz of 45%, a content of montmorillonite of 34.9%, and a content of clinochlore of 20%. Infrared spectroscopy and thermal analyses confirmed the presence of hydroxyl groups in both samples, suggesting a higher content in ALO1. Measurement of N2 adsorption isotherms on the clay samples yields specific surface areas of 87 m2/g for PRE4 and 32 m2/g for ALO1, pore volumes of 0.721 cm3/g for PRE4 and 0.637 cm3/g for ALO1, and similar pore sizes in the range of 6–12 nm. Electrochemical analysis highlighted a good conductivity of ALO1 and PRE4 when used for the modification of commercial carbon-based screen-printed electrodes. In detail, higher currents were registered by differential pulse voltammetry for the electrodes modified with the clays with respect to bare electrodes, as well as good repeatability of the measurements. In addition, a comparative study with nanomaterials, known for their good conductivity, was achieved, using carbon black and gold nanoparticles as a reference, showing that the conductivity of the clays was lower than but not so different from those of the reference materials. Natural (archaeology) ; Geology ; Characterization (materials science) ; Geochemistry ; Mining engineering ; Materials science ; Paleontology ; Nanotechnology Clay minerals and soil interactions ; Soil and Unsaturated Flow ; Building materials and conservation