jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Afsar Kamal | Nur Diyana Kamarudin | Khairol Amali Bin Ahmad | Syarifah Bahiyah Rahayu | Mohd Rizal Mohd Isa | Siti Noormiza Makhtar | Zulkifli Yaakub
Source: Machine Graphics and Vision (review, 2024-12-23)
Field: Chemistry — Analytical Chemistry

Machine vision for automated maturity grading of oil palm fruits: A systematic review The maturity of oil palm fruits is a very crucial factor for oil extraction industry in Indonesia, Malaysia, Thailand, and other countries to ensure the oil quality and increase productivity. This literature review examines the various machine learning techniques, especially the deep learning techniques used to automate the maturity grading process of oil palm fresh fruit bunches. The crucial advantages of using machine learning approaches were highlighted, and the limitations and prospects of each research article were discussed. This review describes the various image pre-processing techniques utilized to prepare images for model training. CNN is identified as the dominant over all classification techniques of machine learning to classify the oil palm fruits images based on maturity level, due to its ability of learning complex features. Palm oil ; Grading (engineering) ; Maturity (psychological) ; Palm ; Computer science ; Artificial intelligence ; Agricultural engineering ; Engineering ; Biology ; Food science ; Psychology ; Civil engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Mahendra Jain | Kinjal Parikh | Ganesh Shevalkar | Parth Thakkar | Rakhee Kapadia
Source: International Journal of Herbal Medicine (article, 2024-01-01)
Field: Materials Science — Biomaterials

Introduction to functional performance of bio-based emulsifiers, natural preservatives, lipids, and natural surfactants Bio-based products can be obtained from natural renewable resources or produced after suitable bioprocessing. Almost every industry/sector, including energy, nutraceuticals, packaging, plastics, and many more, is currently concentrating on making bio-based materials, as these materials are bio-renewable, degradable, and environmentally safe. Bio-based nutraceuticals have gained popularity in recent decades due to their beneficial effects on the population and their role in lowering the risk of different diseases. In the present review, we have focused on the functional performance of various bio-based emulsifiers, natural preservatives, lipids, and surfactants with in-depth detail on their classification, functional roles, microorganisms involved in their production, and future perspectives. Thus, for commercial production, the use of renewable resources over fossil-based resources can provide tremendous advantages like protection to the environment, decreased production cost, and compounds with equal or improved yield. Natural (archaeology) ; Preservative ; Chemistry ; Biochemical engineering ; Food science ; Biology ; Engineering biodegradable polymer synthesis and properties
Authors: Imroi El-Habib | Hassan Maatouk | Alex Lemarchand | Anne Roynette | Sarah Dine | Christine Mielcarek | Mamadou Traoré | Rabah Azouani
Source: arXiv (Cornell University) (preprint, 2024-04-25)
Field: Chemistry — Inorganic Chemistry

Antibacterial size effect of ZnO nanoparticles and their role as additives in emulsion waterborne paint Nosocomial infections (NIs) are prevalent in intensive care units due to antibiotic overuse. Metal oxide nanoparticles (NPs), like ZnO, offer potential solutions, yet understanding how NPs size impacts their antibacterial efficacy are lacking. This study focuses on the effect of nanoparticle size on kinetics of bacterial strains growth. NPs were synthesized using a sol-gel process with monoethanolamine (MEA) and water, characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), and Raman spectroscopy, confirming crystallization and size variations. ZnO NPs with mean size of 22, 35 and 66 nm were used against the most common nosocomial bacteria strains Escherichia coli (Gram-negative), Pseudomonas aeruginosa (Gram-negative), and Staphylococcus aureus (Gram-positive). The evaluation of NPs minimal inhibitory concentration (MIC) and bactericidal concentration (MBC) revealed superior antibacterial activity in smaller NPs. The bacterial population was monitored via optical absorbance, showing reduced specific growth rate, prolonged latency period, and increased inhibition percentage with smaller NPs, indicating a substantial deceleration in the growth of microorganisms. Pseudomonas aeruginosa exhibited the smallest sensitivity to ZnO NPs, attributed to its environmental stress resistance. Furthermore, the antibacterial efficacy of paint containing 1 wt% of 22 nm ZnO NPs was assessed and displayed activity against E. coli and S. aureus. Emulsion ; Materials science ; Nanoparticle ; Chemical engineering ; Nanotechnology Pigment Synthesis and Properties
Source:  (article, 2024-08-08)
Field: Materials Science — Polymers and Plastics

Advances in Fibre-reinforced Composite and Sandwich Structures Composite materials have become essential across various industries, including aerospace, automotive, construction, sports equipment, and electronics.These materials excel in strength-to-weight ratio, resistance to fatigue and corrosion, and low thermal expansion.The flexibility to customize composites by altering the type, size, and orientation of the reinforcement, along with the matrix Aerospace ; Composite number ; Specific strength ; Flexibility (engineering) ; Composite material ; Materials science ; Automotive industry ; Reinforcement ; Corrosion ; Sandwich-structured composite ; Electronics ; Structural engineering ; Engineering ; Aerospace engineering ; Electrical engineering Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Structural Analysis and Optimization
Authors: Shasha Li | Guo Tao | Yong Yan | Yimin A. Wu
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2024-04-19)
Field: Materials Science — Materials Chemistry

Emergence of Ferroelectricity in p‐Type 2D In<sub>1.75</sub>Sb<sub>0.25</sub>Se<sub>3</sub> p‐type 2D ferroelectric semiconductors (2D FeSs) play an increasingly essential role in the advanced nonvolatile and morphotropic beyond‐Moore electronic devices with high performance and low power consumption. But reliable p‐type 2D FeS with holes as majority carriers are still scarce. Herein, the first experimental realization of room‐temperature ferroelectricity in van der Waals layered β‐In 1.75 Sb 0.25 Se 3 down to few layer is reported. The origin of ferroelectricity in β‐In 1.75 Sb 0.25 Se 3 comes from aliovalent elemental substitution, antimony substituting to the indium sites (Sb In ), changing the local environment of the central‐layer Se atoms. Thanks to the intrinsic ferroelectric and semiconducting natures, FeS field‐effect transistor (FeSFET) devices based on β‐In 1.75 Sb 0.25 Se 3 exhibit reconfigurable, multilevel nonvolatile memory (NVM) states, which can be successively modulated by gate voltage stimuli. Furthermore, the inherent operation mechanism, due to the switchable polarization, indicates that a neuromorphic memory is also possible with 2D FeSFETs. These presented results facilitate the technological implementation of versatile 2D FeS devices for next‐generation logic‐in‐memory approach for Internet‐of‐Things entities. Ferroelectricity ; Non-volatile memory ; Materials science ; Optoelectronics ; Realization (probability) ; Indium ; Neuromorphic engineering ; Transistor ; Semiconductor ; Field-effect transistor ; Polarization (electrochemistry) ; Condensed matter physics ; Nanotechnology ; Voltage ; Computer science ; Electrical engineering ; Physics ; Chemistry ; Dielectric 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; MXene and MAX Phase Materials
Authors: Julie M. Butler | M. Hjorth‐Jensen | Justin G. Lietz
Source: arXiv (Cornell University) (preprint, 2024-03-07)
Field: Materials Science — Materials Chemistry

Accelerating the Convergence of Coupled Cluster Calculations of the Homogeneous Electron Gas Using Bayesian Ridge Regression The homogeneous electron gas is a system which has many applications in chemistry and physics. However, its infinite nature makes studies at the many-body level complicated due to long computational run times. Because it is size extensive, coupled cluster theory is capable of studying the homogeneous electron gas, but it still poses a large computational challenge as the time needed for precise calculations increases in a polynomial manner with the number of particles and single-particle states. Consequently, achieving convergence in energy calculations becomes challenging, if not prohibited, due to long computational run times and high computational resource requirements. This paper develops the sequential regression extrapolation (SRE) to predict the coupled cluster energies of the homogeneous electron gas in the complete basis limit using Bayesian ridge regression to make predictions from calculations at truncated basis sizes. Using the SRE method we were able to predict the coupled cluster doubles energies for the electron gas across a variety of values of N and $r_s$, for a total of 70 predictions, with an average error of 4.28x10$^{-4}$ Hartrees while saving 88.9 hours of computational time. The SRE method can accurately extrapolate electron gas energies to the complete basis limit, saving both computational time and resources. Additionally, the SRE is a general method that can be applied to a variety of systems, many-body methods, and extrapolations. Bayesian probability ; Homogeneous ; Cluster (spacecraft) ; Convergence (economics) ; Ridge ; Regression ; Electron ; Statistical physics ; Computer science ; Physics ; Mathematics ; Statistics ; Geology ; Economics ; Nuclear physics Machine Learning in Materials Science
Authors: Eugenio J. Llanos | Wilmer Leal | Andrés Bernal | Jürgen Jost | Peter F. Stadler
Source: Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2024-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

Are the chemical families still there? Formal structure of similarity of elements and its thermochemical domain The periodic table organizes chemical elements into families based on their similarity, now understood through Quantum Mechanics. However, these families were inferred from limited compounds in the nineteenth century. Since then, the number of compounds has exponentially grown, leading to the discovery of new types unknown to pioneers. This situation prompts the question of whether these families can still be discerned from the amassed data, or if their recognition is confined to specific thermochemical domains. To address this inquiry, we conducted a comprehensive exploration by comparing formulae (1771–2015) as a proxy for chemical similarity. Our findings reveal that stoichiometry not only captures a significant portion of the trends observed within families but also unveils other intriguing features of the formal structure of chemical similarity. These patterns approach equivalence classes independent of thermochemical context and demonstrate high resilience. Temporal analysis demonstrates that, since approximately 1980, similarity is diminishing due to an increasing production of unique formulae for nearly all elements. Nevertheless, chemical families endure over time and they stand out as the most robust similarity patterns. Our analysis offers compelling evidence that any study will reach the same conclusions, provided there is a sufficient diversity in input compound data. Similarity (geometry) ; Domain (mathematical analysis) ; Computer science ; Mathematics ; Artificial intelligence ; Mathematical analysis History and advancements in chemistry ; Chemical Thermodynamics and Molecular Structure ; Chemistry and Chemical Engineering
Authors: Matheus Esteves Ferreira | Mariana Del Grande | Felipe Lopes Oliveira | Rodrigo Neumann Barros Ferreira | Ademir Ferreira da Silva | PIETRA DIONISI DE CARVALHO | Gabriel Santos de Castro e Lima | Ado Jorio | M. Steiner
Source: arXiv (Cornell University) (preprint, 2024-11-22)
Field: Chemistry — Inorganic Chemistry

Mode-selective Raman imaging of metal-organic frameworks reveals surface heterogeneities of single HKUST-1 crystals Metal organic frameworks (MOFs) are nanoporous materials with high surface-to-volume ratio that have potential applications as gas sorbents. Sample quality is, however, often compromised and it is unclear how defects and surface contaminants affect the spectral properties of single MOF crystals. Raman micro-spectroscopy is a powerful tool for characterizing MOFs, yet spatial spectral heterogeneity distributions of single MOF crystals have not been reported so far. In this work, we use Raman micro-spectroscopy to characterize spatially isolated, single crystals of the MOF species HKUST-1. In a first step, we validate HKUST-1's Raman spectrum based on DFT simulations and we identify a previously unreported vibrational feature. In a second step, we acquire diffraction-limited, mode-selective Raman images of a single HKUST-1 crystals that reveal how the spectral variations are distributed across the crystal surface. In a third step, we statistically analyze the measured spectral peak positions and line widths for quantifying the variability occurring within the same crystal as well as between different crystals taken from the same batch. Finally, we explore how multivariate data analysis can aid feature identification in Raman images of single MOF crystals. For enabling validation and reuse, we have made the spectroscopic data and simulation code publicly available. Metal-organic framework ; Materials science ; Raman spectroscopy ; Metal ; Nanotechnology ; Optics ; Chemistry ; Metallurgy ; Physics ; Physical chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Garba Uba | Kabiru Musa Aujara | Abdulhamid Aliyu Omar | Muntari Bala | Zainab Abdu Mashi
Source: Journal of Environmental Bioremediation and Toxicology (article, 2024-12-26)
Field: Chemistry — Spectroscopy

Lead Ion Detection in Challawa Water: Development and Application of Response Surface Methodology Agricultural areas in Nigeria depend upon unpolluted water sources and hence need continuous monitoring. River Challawa is among the water sources for domestic and agricultural purposes. It is located in an industrialized area of Kano, Nigeria. A cucumber protease-based assay for the detection of lead metal has been developed to determine the efficiency of the monitoring method. Cucumber protease is assayed by using casein as a substrate with Coomassie brilliant blue to track the complete hydrolysis of the substrate. In the presence of inhibitors such as metal ions, casein is not hydrolyzed to completion, and the solution is dark blue. The cucumber protease assay was optimized using One-Factor-at-a-Time (OFAT) and Response Surface Methodology (RSM). The RSM-optimized method demonstrated superior sensitivity, reducing the IC50 value for lead detection from 0.078 mg/L (OFAT) to 0.028 mg/L. The findings show a marked improvement in concentration, causing 50% inhibition ( IC50) for lead metal in River Challawa. The results of the comparison between one factor at a time (OFAT) optimization and RSM provide an improvement of IC50 for lead ion from 0.078 (95% CI,0.057 to 0.27) to 0.028 (95% CI, 0.022 to 0.037). The LOD for lead Pb2+ is 0.015 and 0.003 for OFAT and RSM, respectively. The results of this study indicate that cucumber-protease assay has the potential efficiency of being used in the determination of lead metal in water samples. Ion ; Materials science ; Computer science ; Environmental science ; Chemistry Mass Spectrometry Techniques and Applications
Authors: Haotian Fan | Wangcheng Gu | Dongrui Zhou | Ge Song | Pengfeng Xiao | Zhongjie Fei
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-04-16)
Field: Materials Science — Biomaterials

Five-layered robot (F-robot) based on functionally graded multilayer foam antiviral low-carbon (F-MAX) system for Pathogenic Microorganism Elimination Abstract In this study, a revolutionary air filtration technology, the F-MAX multilayer composite plate, is introduced, offering high efficiency and environmental sustainability. This innovative system is designed to capture a wide range of pollutants, including harmful viruses and bacteria, enhancing air quality significantly. The F-MAX combines multiple layers, each tailored to target specific particles, with features like an electrostatically charged melt-blown fabric and eco-friendly materials like lithium brine by-product magnesia. Its durability, antiviral, and antibacterial properties make it a sustainable choice for air purification, suitable for both commercial and residential use. This system represents healthier living environments, effectively removing airborne contaminants, and demonstrating a commitment to a sustainable future. Additionally, the study introduces the F-robot specifically designed for laboratory environments to ensure pristine air quality. One-Sentence Summary T-robot air filter, which use F-MAX, a multilayer composite consisted of self-healing cellular coating, Desert Rose (DR) coating, melt-blown cloth, and BMSC with high-efficiency, environmentally sustainable filtration, and antiviral properties, suitable for diverse environments. Robot ; Microorganism ; Carbon fibers ; Materials science ; Carbon nanofoam ; Composite material ; Mechanical engineering ; Nanotechnology ; Computer science ; Engineering ; Biology ; Artificial intelligence ; Bacteria ; Genetics Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies ; Aerosol Filtration and Electrostatic Precipitation
Authors: Omar A. M. Abdelraouf | Sahar M. Mousa | Mohamed Ragab
Source: arXiv (Cornell University) (preprint, 2024-09-11)
Field: Chemistry — Organic Chemistry

Next-Generation Multi-layer Metasurface Design: Hybrid Deep Learning Models for Beyond-RGB Reconfigurable Structural Colors Metasurfaces are key to the development of flat optics and nanophotonic devices, offering significant advantages in creating structural colors and high-quality factor cavities. Multi-layer metasurfaces (MLMs) further amplify these benefits by enhancing light-matter interactions within individual nanopillars. However, the numerous design parameters involved make traditional simulation tools impractical and time-consuming for optimizing MLMs. This highlights the need for more efficient approaches to accelerate their design. In this work, we introduce NanoPhotoNet, an AI-driven design tool based on a hybrid deep neural network (DNN) model that combines convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) networks. NanoPhotoNet enhances the design and optimization of MLMs, achieving a prediction accuracy of over 98.3% and a speed improvement of 50,000x compared to conventional methods. The tool enables MLMs to produce structural colors beyond the standard RGB region, expanding the RGB gamut area by 163%. Furthermore, we demonstrate the generation of tunable structural colors, extending the metasurface functionality to tunable color filters. These findings present a powerful method for applying NanoPhotoNet to MLMs, enabling strong light-matter interactions in applications such as tunable nanolasers and reconfigurable beam steering. RGB color model ; Deep learning ; Structural coloration ; Layer (electronics) ; Computer science ; Computer architecture ; Artificial intelligence ; Materials science ; Optoelectronics ; Nanotechnology ; Photonic crystal Polydiacetylene-based materials and applications ; Advanced Antenna and Metasurface Technologies ; Architecture and Computational Design
Authors: Xiumei Jia | Zhendong Xin | Yizheng Fu | Hongji Duan
Source: Molecules (article, 2024-10-11)
Field: Chemistry — Physical and Theoretical Chemistry

Theoretical Investigation into Polymorphic Transformation between β-HMX and δ-HMX by Finite Temperature String Polymorphic transformation is important in chemical industries, in particular, in those involving explosive molecular crystals. However, due to simulating challenges in the rare event method and collective variables, understanding the transformation mechanism of molecular crystals with a complex structure at the molecular level is poor. In this work, with the constructed order parameters (OPs) and K-means clustering algorithm, the potential of mean force (PMF) along the minimum free-energy path connecting <i>β</i>-HMX and <i>δ</i>-HMX was calculated by the finite temperature string method in the collective variables (SMCV), the free-energy profile and nucleation kinetics were obtained by Markovian milestoning with Voronoi tessellations, and the temperature effect on nucleation was also clarified. The barriers of transformation were affected by the finite-size effects. The configuration with the lower potential barrier in the PMF corresponded to the critical nucleus. The time and free-energy barrier of the polymorphic transformation were reduced as the temperature increased, which was explained by the pre-exponential factor and nucleation rate. Thus, the polymorphic transformation of HMX could be controlled by the temperatures, as is consistent with previous experimental results. Finally, the HMX polymorph dependency of the impact sensitivity was discussed. This work provides an effective way to reveal the polymorphic transformation of the molecular crystal with a cyclic molecular structure, and further to prepare the desired explosive by controlling the transformation temperature. Transformation (genetics) ; String (physics) ; Thermodynamics ; Materials science ; Physics ; Theoretical physics ; Chemistry Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Synthesis and Characterization of Heterocyclic Compounds
Authors: І. В. Дощечкіна | Nataliia Lalazarova
Source:  (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

PLASTICIZATION OF COLD-ROLLED LOW-CARBON STEELS BY EPILAMIZATION OF THE SURFACE The article presents the results of evaluating the effectiveness of treatment of patients with osteoarthritis of the knee joint of 1-3 degrees using platelet-rich plasma.According to the results of the study, it was found that the maximum decrease in indicators on the WOMAC and YOUR scales, in the form of relief of pain, reduction of stiffness and increase in range of motion, occurred in patients after 3 procedures of intraarticular injection of PRP. Plasticizer ; Materials science ; Carbon fibers ; Metallurgy ; Composite material Metal Alloys Wear and Properties ; Material Properties and Applications ; Polymer Science and Applications