jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. Sofana Reka | Ankita Bagelikar | Prakash Venugopal | V. Ravi | Harimurugan Devarajan
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2023-12-21)
Field: Chemistry — Analytical Chemistry

Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality, flavor and nutritional value. The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers. The impact of rotten fruits can foster harmful bacteria, molds and other microorganisms that can cause food poisoning and other illnesses to the consumers. The overall purpose of the study is to classify rotten fruits, which can affect the taste, texture, and appearance of other fresh fruits, thereby reducing their shelf life. The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life. Hence, this research work mainly focuses on the Convolutional Neural Network’s (CNN) deep learning model, which helps in the classification of rotten fruits. The proposed methodology involves real-time analysis of a dataset of various types of fruits, including apples, bananas, oranges, papayas and guavas. Similarly, machine learning models such as Gaussian Naïve Bayes (GNB) and random forest are used to predict the fruit’s shelf life. The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model. In comparison to other pretrained models, the visual geometry group16 (VGG16) obtained a higher accuracy score of 95%. Likewise, the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life. By developing an accurate classification model, only fresh and safe fruits reach consumers, reducing the risks associated with contaminated produce. Thereby, the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits. Shelf life ; Naive Bayes classifier ; Artificial intelligence ; Random forest ; Machine learning ; Convolutional neural network ; Flavor ; Taste ; Computer science ; Mathematics ; Food science ; Horticulture ; Support vector machine ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Kyle Brewer | Fengxiang Bai | Anton Blencowe
Source: Pharmaceutics (article, 2023-03-18)
Field: Materials Science — Biomaterials

pH-Responsive Poly(ethylene glycol)-b-poly(2-vinylpyridine) Micelles for the Triggered Release of Therapeutics The use of pH-responsive polymeric micelles is a promising approach to afford the targeted, pH-mediated delivery of hydrophobic drugs within the low-pH tumour milieu and intracellular organelles of cancer cells. However, even for a common pH-responsive polymeric micelle system-e.g., those utilising poly(ethylene glycol)-<i>b</i>-poly(2-vinylpyridine) (PEG-<i>b</i>-PVP) diblock copolymers-there is a lack of available data describing the compatibility of hydrophobic drugs, as well as the relationships between copolymer microstructure and drug compatibility. Furthermore, synthesis of the constituent pH-responsive copolymers generally requires complex temperature control or degassing procedures that limit their accessibility. Herein we report the facile synthesis of a series of diblock copolymers via visible-light-mediated photocontrolled reversible addition-fragmentation chain-transfer polymerisation, with a constant PEG block length (90 repeat units (RUs)) and varying PVP block lengths (46-235 RUs). All copolymers exhibited narrow dispersity values (<i>Đ</i> ≤ 1.23) and formed polymeric micelles with low polydispersity index (PDI) values (typically <0.20) at physiological pH (7.4), within a suitable size range for passive tumour targeting (<130 nm). The encapsulation and release of three hydrophobic drugs (cyclin-dependent kinase inhibitor (CDKI)-73, gossypol, and doxorubicin) were investigated in vitro at pH 7.4-4.5 to simulate drug release within the tumour milieu and cancer cell endosome. Marked differences in drug encapsulation and release were observed when the PVP block length was increased from 86 to 235 RUs. With a PVP block length of 235 RUs, the micelles exhibited differing encapsulation and release properties for each drug. Minimal release was observed for doxorubicin (10%, pH 4.5) and CDKI-73 exhibited moderate release (77%, pH 4.5), whereas gossypol exhibited the best combination of encapsulation efficiency (83%) and release (91% pH 4.5) overall. These data demonstrate the drug selectivity of the PVP core, where both the block molecular weight and hydrophobicity of the core (and accordingly the hydrophobicity of the drug) have a significant effect on drug encapsulation and release. These systems remain a promising means of achieving targeted, pH-responsive drug delivery-albeit for select, compatible hydrophobic drugs-which warrants their further investigation to develop and evaluate clinically relevant micelle systems. Micelle ; Copolymer ; Ethylene glycol ; Dispersity ; PEG ratio ; Chemistry ; Amphiphile ; Polymer chemistry ; Materials science ; Biophysics ; Chemical engineering ; Aqueous solution ; Organic chemistry ; Polymer Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Surface Modification and Superhydrophobicity
Authors: Nutth Tuchinda | Christopher A. Schuh
Source: Physical Review Materials (article, 2023-02-16)
Field: Materials Science — Materials Chemistry

Triple junction solute segregation in Al-based polycrystals This study focuses on the impact of grain triple junctions on the bulk thermodynamics of nanomaterials, particularly in Al-based polycrystals where the volume fraction of these junctions is high. The investigation evaluates the energetics of solute segregation at both grain boundaries and triple junctions, and identifies systems where junction segregation is preferred over grain boundaries. The study generates a comprehensive database of segregation spectra for 39 solute elements in Al, which reveals important chemical trends that can aid in alloy designs. Furthermore, the framework employed in this study is adaptable to other alloy systems. Materials science ; Grain boundary ; Triple junction ; Alloy ; Volume fraction ; Nanomaterials ; Energetics ; Condensed matter physics ; Metallurgy ; Thermodynamics ; Nanotechnology ; Microstructure ; Composite material ; Optoelectronics Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; nanoparticles nucleation surface interactions
Authors: Nicholas Cusato | Seyed Amin Nabavizadeh | Mohsen Eshraghi
Source: Metals (review, 2023-06-23)
Field: Materials Science — Materials Chemistry

A Review of Large-Scale Simulations of Microstructural Evolution during Alloy Solidification During the past two decades, researchers have shown interest in large-scale simulations to analyze alloy solidification. Advances in in situ X-ray observations of the microstructural evolution of dendrites have shown defects that can be very costly for manufacturers. These simulations provide the basis for understanding applied meso-/macro-scale phenomena with microscale details using various numerical schemes to simulate the morphology and solve for transport phenomena. Methods for simulating methodologies include cellular automaton, phase field, direct interface tracking, level set, dendritic needle networks, and Monte Carlo while finite element, finite difference, finite volume, and lattice Boltzmann methods are commonly used to solve for transport phenomena. In this paper, these methodologies are explored in detail with respect to simulating the dendritic microstructure evolution and other solidification-related features. The current research, from innovations in algorithms for scaling to parallel processing details, is presented with a focus on understanding complex real-world phenomena. Topics include large-scale simulations of features with and without convection, columnar to equiaxed transition, dendrite interactions, competitive growth, microsegregation, permeability, and applications such as additive manufacturing. This review provides the framework and methodologies for achieving scalability while highlighting the areas of focus that need more attention. Microscale chemistry ; Cellular automaton ; Equiaxed crystals ; Lattice Boltzmann methods ; Computer science ; Statistical physics ; Scaling ; Dendrite (mathematics) ; Finite element method ; Scale (ratio) ; Scalability ; Materials science ; Computational science ; Microstructure ; Mechanics ; Artificial intelligence ; Physics ; Geometry ; Mathematics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Nanoporous metals and alloys
Authors: Ewa Ropelewska | Ahmed Rady | Nicholas J. Watson
Source: Sustainability (article, 2023-06-08)
Field: Chemistry — Analytical Chemistry

Apricot Stone Classification Using Image Analysis and Machine Learning Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production. Artificial intelligence ; Support vector machine ; Scanner ; Computer vision ; Pattern recognition (psychology) ; Digital camera ; Linear discriminant analysis ; RGB color model ; Naive Bayes classifier ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Smart Agriculture and AI
Authors: Soheila Sharafinia | Alimorad Rashidi | Behnam Babaei | Yasin Orooji
Source: Scientific Reports (article, 2023-08-12)
Field: Materials Science — Materials Chemistry

Nanoporous carbons based on coordinate organic polymers as an efficient and eco-friendly nano-sorbent for adsorption of phenol from wastewater The major part of water pollutants includes of organic such as phenolic pollutant, thus there are every hazardous to environment. Present work is a comparative onto surface chemistry and adsorptive characteristics of coordinate organic polymer (Cop-150) and nanoporous carbon (NPC) prepared using solvothermal method. New NPC was successfully synthesized to remove of phenol. FT-IR, XRD, XPS, SEM, TGA, and BET techniques have been used to characterization and confirm physicochemical variation during preparing Cop-150 and NPC. Box-Behnken response surface methodology (BBRSM) was used to optimize four important factors of the pH (2-10), contact time (1-40 min), temperature (25-60 °C), and initial concentration of phenol (5-50 mg L<sup>-1</sup>). To analyze the data obtained from the adsorption of phenol by synthesized adsorbents, four linear, 2FI, quadratic and cubic models were examined, which the quadratic model was recognized as the best model. To the NPC the equal adsorption capacity 500 mg g<sup>-1</sup> is achieved at the initial concentration of phenol = 49.252 mg L<sup>-1</sup>, contact time = 15.738 min, temperature = 28.3 °C, and pH 7.042. On the other hand, the adsorption capacity for Cop-150 in pH 4.638, the contact time = 19.695 min, the temperature = 56.8 °C, and the initial concentration of phenol = 6.902 mg L<sup>-1</sup> was equal to 50 mg g<sup>-1</sup>. The experimental data at different conditions were investigated by some famous kinetic and isotherm models, which among them, were corresponded to the pseudo-second-order kinetic model and the Langmuir isotherm. Moreover, based to result of thermodynamics to the both Cop-150 and NPC, the adsorption process is exothermic and spontaneous. According to results the Cop-150 and NPC could be used for up to four and five cycles without significantly reducing their performance, respectively. Adsorption ; Phenol ; Sorbent ; Nanoporous ; Chemistry ; Activated carbon ; Response surface methodology ; Chemical engineering ; Nuclear chemistry ; Materials science ; Organic chemistry ; Chromatography Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Abdelfattah Amari | Heba Saed Kariem Alawameleh | Mubeen Isam | Mohammed Abdul Jaleel Maktoof | Haitham Osman | Balamurugan Panneerselvam | Maciej Thomas
Source: Sustainability (article, 2023-01-30)
Field: Chemistry — Inorganic Chemistry

Thermodynamic Investigation and Study of Kinetics and Mass Transfer Mechanisms of Oily Wastewater Adsorption on UIO-66–MnFe2O4 as a Metal–Organic Framework (MOF) Hitherto, a considerable amount of research has been carried out to investigate the equilibrium condition of adsorption process; nevertheless, there is no comprehensive study to evaluate the surface adsorption properties of MOFs. Therefore, the adsorption mechanism and equilibrium capacity of MOFs have not been fully understood. Furthermore, the mass transfer mechanism is still unknown and so it is not possible to predict the adsorption process using MOFs. In this work, a new metal–organic framework (MOF) named UIO-66–MnFe2O4 was synthesized as an adsorbent for oily wastewater treatment. In this way the effects of temperature, amount of adsorbent, adsorption time, pH, and pollutant initial concentration were studied in the treatment of oily wastewater using the UIO-66-MnFe2O4 MOF through the adsorption process. Furthermore, to examine the process of surface adsorption, different adsorption kinetic models (pseudo-first-order, pseudo-second-order, and Elovich) have been performed for the removal of oily pollutants on MOF adsorbents and the surface adsorption mechanism has been discussed carefully. Moreover, to investigate the mass transfer mechanism of oily pollutants in the surface adsorption process, different mass transfer models (Weber and Morris, liquid film diffusion, and Bangham and Burt) have been investigated on porous adsorbents, and finally the mass transfer mechanism of the adsorption process has been proposed. Adsorption ; Mass transfer ; Metal-organic framework ; Wastewater ; Chemical engineering ; Diffusion ; Chemistry ; Pollutant ; Kinetics ; Porosity ; Materials science ; Chromatography ; Thermodynamics ; Organic chemistry ; Environmental engineering ; Environmental science Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Enhanced Oil Recovery Techniques
Authors: Abdulrahman Mallah | M. Debbichi | Mohamed Houcine Dhaou | Bilel Bellakhdhar
Source: Crystals (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Structural, Mechanical, and Piezoelectric Properties of Janus Bidimensional Monolayers In the present work, the noncentrosymmetric 2D ternary Janus monolayers Al2XX’(X/X’ = S, Se, Te and O), Si2XX’(X/X’ = P, As, Sb and Bi), and A2PAs(A = Ge, Sn and Pb) have been studied based on first-principles calculations. We find that all the monolayers exhibit in-plane d12, and out-of-plane d13 piezoelectric coefficients due to the lack of reflection symmetry with respect to the central A atoms. Moreover, our calculations show that Al2OX(T = S, Se, Te) chalcogenide monolayers have higher absolute in-plane piezoelectric coefficients. However, the highest out-of-plane values are achieved in the Si2PBi monolayer, larger than those of some advanced piezoelectric materials, making them very promising transducer materials for lightweight and high-performance piezoelectric nanodevices. Monolayer ; Piezoelectricity ; Materials science ; Janus ; Chalcogenide ; Ternary operation ; Plane (geometry) ; Condensed matter physics ; Reflection (computer programming) ; Piezoelectric coefficient ; Optoelectronics ; Nanotechnology ; Composite material ; Physics ; Geometry ; Computer science 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices
Authors: Lena Langenohl | Tobias Brink | Gunther Richter | Gerhard Dehm | Christian H. Liebscher
Source: Physical review. B./Physical review. B (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Atomic-resolution observations of silver segregation in a [111] tilt grain boundary in copper Alloying a material and hence segregating solutes to grain boundaries is one way to tailor a material to the demands of its application. Direct observation of solute segregation is necessary to understand how the interfacial properties are altered. In this study, we investigate the atomic structure of a high-angle grain boundary both in pure copper and upon silver segregation by aberration-corrected scanning transmission electron microscopy and spectroscopy. We further correlate the experiments to atomistic simulations to quantify the local solute excess and its impact on grain boundary properties. We observe that the grain boundary structure remains intact upon silver segregation and up to five different positions within a structural unit serve as segregation sites. By combining the atomic-resolution observation with atomistic modeling, we are able to quantify the local silver concentration and elucidate the underlying segregation mechanism. Grain boundary ; Materials science ; Copper ; Chemical physics ; Transmission electron microscopy ; Tilt (camera) ; High-resolution transmission electron microscopy ; Spectroscopy ; Crystallography ; Scanning transmission electron microscopy ; Condensed matter physics ; Microstructure ; Metallurgy ; Nanotechnology ; Chemistry ; Geometry ; Physics Microstructure and mechanical properties ; Surface and Thin Film Phenomena ; Advanced Materials Characterization Techniques
Authors: Yongyan Wu | Yufeng Qian | Wei Peng | Xuchen Qi
Source: PeerJ (article, 2023-07-04)
Field: Materials Science — Biomaterials

Functionalized nanoparticles crossing the brain–blood barrier to target glioma cells Glioma is the most common tumor of the central nervous system (CNS), with a 5-year survival rate of <35%. Drug therapy, such as chemotherapeutic and immunotherapeutic agents, remains one of the main treatment modalities for glioma, including temozolomide, doxorubicin, bortezomib, cabazitaxel, dihydroartemisinin, immune checkpoint inhibitors, as well as other approaches such as siRNA, ferroptosis induction, <i>etc</i>. However, the filter function of the blood-brain barrier (BBB) reduces the amount of drugs needed to effectively target CNS tumors, making it one of the main reasons for poor drug efficacies in glioma. Thus, finding a suitable drug delivery platform that can cross the BBB, increase drug aggregation and retainment in tumoral areas and avoid accumulation in non-targeted areas remains an unsolved challenge in glioma drug therapy. An ideal drug delivery system for glioma therapy should have the following features: (1) prolonged drug life in circulation and effective penetration through the BBB; (2) adequate accumulation within the tumor (3) controlled-drug release modulation; (4) good clearance from the body without significant toxicity and immunogenicity, etc. In this regard, due to their unique structural features, nanocarriers can effectively span the BBB and target glioma cells through surface functionalization, providing a new and effective strategy for drug delivery. In this article, we discuss the characteristics and pathways of different nanocarriers for crossing the BBB and targeting glioma by listing different materials for drug delivery platforms, including lipid materials, polymers, nanocrystals, inorganic nanomaterials, <i>etc</i>. Nanocarriers ; Glioma ; Drug delivery ; Temozolomide ; Medicine ; Blood–brain barrier ; Pharmacology ; Drug ; Targeted drug delivery ; Cancer research ; Central nervous system ; Nanotechnology ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Glioma Diagnosis and Treatment ; Nanoplatforms for cancer theranostics
Authors: Samsul Arifin | Andri Wijaya | Rinda Nariswari | I Gusti Agung Anom Yudistira | Suwarno Suwarno | Faisal Faisal | Diah Wihardini
Source: International Journal of Emerging Technology and Advanced Engineering (article, 2023-05-13)
Field: Materials Science — Materials Chemistry

Long Short-Term Memory (LSTM): Trends and Future Research Potential -One of the most widely used machine learning methods, Long Short-Term Memory (LSTM), is particularly useful for time series prediction. In this study, we carried out a bibliometric analysis against publications about LSTMs to identify trends and contributions of researchers in the development of machine learning technology. We collect bibliometric data from the Scopus database and use the bibliometric analysis method to analyze trends and contributions of researchers in publications about LSTM. Results of the bibliometric analysis show that LSTM is a lot used in related machine learning applications with time series data and is one the most popular technique for use in predictions. In addition, the use of LSTM is often combined with other deep learning methods, such as neural networks, to improve accuracy prediction. In addition, the results of the bibliometric analysis also show that the use of LSTM has spread to various fields, such as in handwriting recognition, processing Language experience, and recognition of a face. Implications from the results of this study are that the use of LSTM can provide solutions that are accurate and effective in solving prediction problems in various fields, especially in practical applications such as business, health, and transportation. The results of the LSTM bibliometric analysis can provide a broader view of trends and the contributions of researchers to the development of machine learning technology, as well as identify potential research areas for further development. Therefore, this research provides an important contribution to strengthening the results of previous research and showing that the use of LSTM has great potential in the development of future machine learning technology Computer science ; Artificial intelligence ; Machine learning ; Deep learning ; Handwriting ; Long short term memory ; Face (sociological concept) ; Scopus ; Term (time) ; Data science ; Artificial neural network ; Recurrent neural network Machine Learning in Materials Science ; Stock Market Forecasting Methods ; Data Stream Mining Techniques