jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Param Punj Singh | Raghavan Ranganathan
Source: ACS Omega (article, 2024-02-16)
Field: Materials Science — Biomaterials

Mechanical and Viscoelastic Properties of Stacked and Grafted Graphene/Graphene Oxide–Polyethylene Nanocomposites: A Coarse-Grained Molecular Dynamics Study High-performance natural materials with superior mechanical properties often possess a hierarchical structure across multiple length scales. Nacre, also known as the mother of pearl, is an example of such a material and exhibits remarkable strength and toughness. The layered hierarchical architecture across different length scales is responsible for the efficient toughness and energy dissipation. To develop high-performance artificial nacre-like composites, it is necessary to mimic this layered structure and understand the molecular phenomena at the interface. This study uses coarse-grained molecular dynamics simulations to investigate the structure-property relationship of stacked graphene-polyethylene (PE) nanocomposites. Uniaxial and oscillatory shear deformation simulations were conducted to explore the composites' mechanical and viscoelastic behavior. The effect of grafting on the glass-transition temperature and the mechanical and viscoelastic behavior was also examined. The two examined microstructures, the stacked and grafted GnP (graphene nanoplatelet)-PE composites, demonstrated significant enhancement in the Young's modulus and yield strength when compared to the pristine PE. The study also delves into the viscoelastic properties of polyethylene nanocomposites containing graphene and graphene oxide. The grafted composite demonstrated an increased elastic energy and improved capacity for stress transfer. Our study sheds light on the energy dissipation properties of layered nanocomposites through underlying molecular mechanisms, providing promising prospects for designing novel biomimetic polymer nanocomposites. Materials science ; Graphene ; Nanocomposite ; Composite material ; Toughness ; Viscoelasticity ; Polyethylene ; Microstructure ; Molecular dynamics ; Oxide ; Polymer nanocomposite ; Modulus ; Nanotechnology Calcium Carbonate Crystallization and Inhibition ; Graphene and Nanomaterials Applications ; Bone Tissue Engineering Materials
Authors: Thomas Pickles | Chantal L. Mustoe | Cameron J. Brown | Alastair J. Florence
Source: Crystal Growth & Design (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

Comparative Study on Adaptive Bayesian Optimization for Batch Cooling Crystallization for Slow and Fast Kinetic Regimes Crystallization kinetic parameter estimation is important for the classification, design, and scale-up of pharmaceutical manufacturing processes. This study investigates the impact of supersaturation and temperature on the induction time, nucleation rate, and growth rate for the compounds lamivudine (slow kinetics) and aspirin (fast kinetics). Adaptive Bayesian optimization (AdBO) has been used to predict experimental conditions that achieve target crystallization kinetic values for each of these parameters of interest. The use of AdBO to guide the choice of the experimental conditions reduced material usage up to 5-fold when compared to a more traditional statistical design of experiments (DoE) approach. The reduction in material usage demonstrates the potential of AdBO to accelerate process development as well as contribute to Net-Zero and green chemistry strategies. Implementation of AdBO can lead to reduced experimental effort and increase efficiency in pharmaceutical crystallization process development. The integration of AdBO into the experimental development workflows for crystallization development and kinetic experiments offers a promising avenue for advancing the field of autonomous data collection exploiting digital technologies and the development of sustainable chemical processes. Crystallization ; Process engineering ; Nucleation ; Supersaturation ; Design of experiments ; Computer science ; Biological system ; Materials science ; Process optimization ; Process (computing) ; Bayesian optimization ; Biochemical engineering ; Thermodynamics ; Mathematics ; Machine learning ; Chemical engineering ; Physics ; Engineering Crystallization and Solubility Studies ; Process Optimization and Integration ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sean Timothy Okonsky | Neil Robert Hogan | Hilal Ezgi Toraman
Source: AIChE Journal (article, 2024-08-06)
Field: Materials Science — Polymers and Plastics

Effect of pyrolysis operating conditions on the catalytic co‐pyrolysis of <scp>low‐density polyethylene</scp> and <scp>polyethylene terephthalate</scp> with zeolite catalysts Abstract In this study, the catalytic (co‐)pyrolysis of low‐density polyethylene (LDPE) and polyethylene terephthalate (PET) with HZSM‐5 and HY zeolite catalysts was conducted in a micro‐pyrolysis reactor coupled to a two‐dimensional gas chromatography system. Pyrolysis operating conditions, such as the pyrolysis temperature, the catalyst to feedstock (CF) ratio, and the LDPE:PET ratio, were varied. It was found that for the co‐pyrolysis of LDPE and PET, HZSM‐5 led to higher yields of C2‐C4 olefins and monoaromatic products. Lower CF ratios increased the yield of C2‐C4 olefins for LDPE pyrolysis, but decreased benzene yield for PET pyrolysis, concomitant with an increased yield in benzoic acid. A lower temperature of 400°C which was sufficient for the pyrolysis of LDPE, led to incomplete conversion of PET. Surface response diagrams were used to visualize the impact of the various pyrolysis operating conditions on the yield of C2‐C4 olefins and BTEX, which serve as target products for the circular economy. Low-density polyethylene ; Pyrolysis ; Polyethylene ; Polyethylene terephthalate ; Yield (engineering) ; Catalysis ; Zeolite ; Materials science ; Chemical engineering ; Raw material ; Organic chemistry ; Benzene ; Chemistry ; Composite material Polymer crystallization and properties ; Microplastics and Plastic Pollution ; Thermochemical Biomass Conversion Processes
Authors: Li Tang | Xin Kang Li | Yue Huang | Xiang‐Zhi Zhang | Bao Qiong Li
Source: Food Chemistry X (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Accurate and visualiable discrimination of Chenpi age using 2D-CNN and Grad-CAM++ based on infrared spectral images Dried tangerine peel ("Chenpi"), has numerous clinical and nutritional benefits, with its quality being significantly influenced by its storage age, referred to as "Chen Jiu Zhe Liang" in Chinese. Concequently, the rapid and accurate identification of Chenpi's age is important for consumers. In this study, Fourier transform infrared spectroscopy (FTIR) was employed to capture spectral images of Chenpi. These FTIR images were then analyzed using a two-dimensional convolutional neural networks (2D-CNN) model, achieving a discrimination accuracy of 97.92%. To address the "black box" nature of the 2D-CNN, Gradient-weighted Class Activation Mapping Plus Plus (Grad-CAM++) was utilized to highlight the important regions contributing to the model's performance. Additionally, six other machine learning models were developped using features identified by the 2D-CNN to validate their effectiveness. The results demonstrated that the combination of FTIR spectral images and 2D-CNN provides a highly effective method for accurately determining the age of Chenpi. Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Fourier transform infrared spectroscopy ; Fourier transform ; Mathematics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Phytochemicals and Antioxidant Activities
Authors: Ruxuan Fang | Xinru Zhang | Bo Song | Zhi Zhang | Lei Zhang | Jun Song | Yonggang Yao | Ming Gao | Kun Zhou | Pengfei Wang | Jian Lü | Yusheng Shi
Source: Engineering (article, 2024-11-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3D and 4D Printing of Electromagnetic Metamaterials Electromagnetic devices have been widely used in the fields of information communication, medical treatment, electrical engineering, and national defense, and their properties are strongly dependent on the constituent electromagnetic materials. Conversely, electromagnetic metamaterials (EMMs), which are artificially engineered with distinctive electromagnetic properties, can overcome the limitations of natural materials owing to their structural advantages. Three-dimensional (3D) printing is the most effective technique for fabricating EMM devices with different geometric parameters and associated properties. However, conventional 3D-printed EMM devices may lack manufacturing flexibility and environmental adaptability to different physical stimuli, such as electric and magnetic fields. Four-dimensional (4D) printing is an ideal technique for schemes to integrate structural design with intelligent materials environmentally adaptive to external fields, for example, the printed components can change shape under electric stimulation. Given the rapid advancements in the EMM field, this paper first reviews typical EMM devices, their design theories, and underlying principles. Subsequently, it presents various EMM structural topologies and manufacturing technologies, emphasizing the feasibility of combining 3D and 4D printing. In addition, we highlight the important applications of EMMs and their future trends and the challenges associated with functional EMMs and additive manufacturing. Metamaterial ; 3D printing ; Computer science ; Acoustics ; Mechanical engineering ; Materials science ; Engineering ; Physics ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Poonguzhali Elangovan | Vijayalakshmi Dhurairajan | Malaya Kumar Nath | Pratheepan Yogarajah | Joan Condell
Source: Applied Sciences (article, 2024-07-09)
Field: Chemistry — Analytical Chemistry

A Novel Approach for Meat Quality Assessment Using an Ensemble of Compact Convolutional Neural Networks The rising awareness of nutritional values has led to an increase in the popularity of meat-based diets. Hence, to ensure public health, industries and consumers are focusing more on the quality and freshness of this food. Authentic meat quality assessment methods can indeed be exorbitant and catastrophic. Furthermore, it is subjective and reliant on the knowledge of specialists. Fully automated computer-aided diagnosis systems are required to eradicate the variability among experts. However, evaluating the quality of meat automatically is challenging. Deep convolutional neural networks have shown a tremendous improvement in meat quality assessment. This research intends to utilize an ensemble framework of shallow convolutional neural networks for assessing the quality and freshness of the meat. Two compact CNN architectures (ConvNet-18 and ConvNet-24) are developed, and the efficacy of the models are evaluated using two publicly available databases. Experimental findings reveal that the ConvNet-18 outperforms other state-of-the models in classifying fresh and spoiled meat with an overall accuracy of 99.4%, whereas ConvNet-24 shows a better outcome in categorizing the meat based on its freshness. This model yields an accuracy of 96.6%, which is much better compared with standard models. Furthermore, the suggested models effectively detect the quality and freshness of the meat with less complexity than the existing state-of-the art techniques. Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Ping Wu | Yingdong Fu | Tony Vancov | Hailong Wang | Yujun Wang | Wenfu Chen
Source: Biochar (article, 2024-09-19)
Field: Chemistry — Inorganic Chemistry

Analyzing the trends and hotspots of biochar’s applications in agriculture, environment, and energy: a bibliometrics study for 2022 and 2023 Abstract Biochar, produced from the thermochemical conversion of biomass waste, has various applications owing to its broad utility and advantageous properties. This study employs a scientometric approach to comprehensively assess the advancements in biochar application from 2022 to 2023. Utilizing 13,357 bibliographic records sourced from the Web of Science Core Collection with the search term “biochar”, the analysis focuses on authorship, national contributions, and keyword trends. Findings demonstrate a continual rise in annual publications since 2009, albeit with a moderated growth rate in 2023. China leads in publication outputs, followed by USA and India, with Hailong Wang emerging as a prominent figure in biochar research. Keyword co-occurrence analyses identify key research themes such as biochar’s role in climate change mitigation, easing salinity and drought stress, immobilizing toxic metals, degrading organic pollutants, serving as additives in anaerobic digestion, and functioning as electrodes in microbial fuel cells. Among these, biochar’s application for global climate change mitigation gains significant attention, while its utilization as electrodes in microbial fuel cells emerges as a promising research frontier, indicating the growing need for sustainable energy sources. The study also outlines critical research gaps and future priorities for enhancing biochar application. Overall, it highlights the diverse applicability of biochar and offers valuable insight into research progression and forthcoming directions in biochar studies. Graphical Abstract Biochar ; Bibliometrics ; Agriculture ; Environmental science ; Agricultural economics ; Earth science ; Natural resource economics ; Geography ; Environmental resource management ; Computer science ; Economics ; Engineering ; Geology ; Library science ; Archaeology ; Waste management Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping ; Municipal Solid Waste Management
Authors: Valentina V. Sobornova | Konstantin V. Belov | Michael A. Krestyaninov | Ilya A. Khodov
Source: International Journal of Molecular Sciences (article, 2024-07-28)
Field: Chemistry — Physical and Theoretical Chemistry

Influence of Solvent Polarity on the Conformer Ratio of Bicalutamide in Saturated Solutions: Insights from NOESY NMR Analysis and Quantum-Chemical Calculations The study presents a thorough and detailed analysis of bicalutamide's structural and conformational properties. Quantum chemical calculations were employed to explore the conformational properties of the molecule, identifying significant energy differences between conformers. Analysis revealed that hydrogen bonds stabilise the conformers, with notable variations in torsion angles. Conformers were classified into 'closed' and 'open' types based on the relative orientation of the cyclic fragments. NOE spectroscopy in different solvents (CDCl<sub>3</sub> and DMSO-d<sub>6</sub>) was used to study the conformational preferences of the molecule. NOESY experiments provided the predominance of 'closed' conformers in non-polar solvents and a significant presence of 'open' conformers in polar solvents. The proportions of open conformers were 22.7 ± 3.7% in CDCl<sub>3</sub> and 59.8 ± 6.2% in DMSO-d<sub>6</sub>, while closed conformers accounted for 77.3 ± 3.7% and 40.2 ± 6.2%, respectively. This comprehensive study underscores the solvent environment's impact on its structural behaviour. The findings significantly contribute to a deeper understanding of conformational dynamics, stimulating further exploration in drug development. Conformational isomerism ; Polarity (international relations) ; Quantum chemical ; Solvent polarity ; Chemistry ; Solvent ; Two-dimensional nuclear magnetic resonance spectroscopy ; Computational chemistry ; Organic chemistry ; Stereochemistry ; Molecule Crystallography and molecular interactions ; Molecular spectroscopy and chirality ; Structural and Chemical Analysis of Organic and Inorganic Compounds
Authors: Li Xue | Du Wang | Li Yu | Fei Ma | Xuefang Wang | Dolores Pérez‐Marín | Peiwu Li | Liangxiao Zhang
Source: Food Frontiers (article, 2024-01-10)
Field: Chemistry — Analytical Chemistry

Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging Abstract Stable isotopes, multi‐elements, metabolic profiles, and integrated spectroscopic fingerprints are priority options for food geographical origin traceability. However, til now, it is still hard to detect adteration with the same one from other geographic origins, which is harder than geographical origin traceability. In this study, partial least square discriminant analysis was employed to build a classification model to discriminate the domestic and imported soybeans after variable selection by uninformative variable elimination using near infrared hyperspectral imaging. As a result, this model could completely discriminate domestic and imported soybeans. Moreover, the developed model was used to detect the adulterated domestic soybean was adulterated with 13.3%, 20.0%, 26.7%, and 33.3% of imported soybean. When the skewness value was less than 0.76 and kurtosis value was less than 1.57 of a sample, the sample was considered as the adulterated. The results indicated that the domestic soybeans adulterated with 20.0%, 26.7%, and 33.3% of imported soybeans were successfully identified. This method could not only identify origin traceability but also detect adulteration of soybeans, which will be beneficial to guarantee the quality and safety of soybean. Traceability ; Hyperspectral imaging ; Linear discriminant analysis ; Sample (material) ; Mathematics ; Food science ; Pattern recognition (psychology) ; Artificial intelligence ; Statistics ; Computer science ; Biology ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Isotope Analysis in Ecology
Authors: Sami Soudani | Michaël Paris | Yann Morizet
Source: Journal of Non-Crystalline Solids (article, 2024-06-15)
Field: Materials Science — Ceramics and Composites

Influence of high-pressure on the short-range structure of Ca or Na aluminoborosilicate glasses from 11B and 27Al solid-state NMR • 11 B NMR revealed a non-linear increase of the boron coordination in Na-bearing samples, where Ca-bearing ones display a linear evolution. • 27 Al NMR shows negligible pressure dependence on aluminium coordination (AlO 4 ) in Na glasses. In contrast, Ca glasses exhibit pressure-induced formation of higher-coordinated AlO 5 and AlO 6 units. • Low R' (([Na 2 O]+[CaO])/([B 2 O 3 ]+[Al 2 O 3 ])) glasses have limited NBOs available for charge compensation. Therefore, the primary source is likely Na + or Ca 2+ cations released from the transformation of AlO 4 units. The present study investigates the boron and aluminium speciation of aluminoborosilicate glasses synthesised under ambient to high-pressure conditions (up to 2.0 GPa) using 11 B and 27 Al Nuclear Magnetic Resonance. Four glass series were prepared in the SiO 2 -Al 2 O 3 -B 2 O 3 -Na 2 O/CaO system, with either high or low R' values (R'=([Na 2 O]+[CaO])/([B 2 O 3 ]+[Al 2 O 3 ])). 11 B NMR reveals a linear increase in four-coordinated boron (BO 4 ) with pressure for Ca glasses and non-linear with a plateau between 0.5 and 1.5 GPa for Na glasses. 27 Al NMR reveals minimal pressure impact on aluminium coordination in sodium glasses. Conversely, calcium glasses form higher-coordinated AlO 5 and AlO 6 units under pressure. The determination of non-bridging oxygen (NBO) variation witnesses the change in compensation mechanism for boron species. In low R' glasses, limited NBOs suggest Na + /Ca 2+ release from AlO 4 as the primary compensation source. This work sheds light on pressure's influence on network connectivity in simple glasses, aiding the understanding of complex glass behaviour. Solid-state ; Range (aeronautics) ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Physical chemistry ; Composite material ; Chromatography Glass properties and applications ; Luminescence Properties of Advanced Materials ; Solid-state spectroscopy and crystallography
Authors: Michelle M. Kelley | Joshua Quinton | Kamron Fazel | Nima Karimitari | Christopher Sutton | Ravishankar Sundararaman
Source: The Journal of Chemical Physics (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

Bridging electronic and classical density-functional theory using universal machine-learned functional approximations The accuracy of density-functional theory (DFT) calculations is ultimately determined by the quality of the underlying approximate functionals, namely the exchange-correlation functional in electronic DFT and the excess functional in the classical DFT formalism of fluids. For both electrons and fluids, the exact functional is highly nonlocal, yet most calculations employ approximate functionals that are semi-local or nonlocal in a limited weighted-density form. Machine-learned (ML) nonlocal density-functional approximations show promise in advancing applications of both electronic and classical DFTs, but so far these two distinct research areas have implemented disparate approaches with limited generality. Here, we formulate a universal ML framework and training protocol to learn nonlocal functionals that combine features of equivariant convolutional neural networks and the weighted-density approximation. We prototype this new approach for several 1D and quasi-1D problems and demonstrate that functionals with exactly the same hyperparameters achieve excellent accuracy for a diverse set of systems, including the hard-rod fluid, the inhomogeneous Ising model, the exact exchange energy of electrons, the electron kinetic energy for orbital-free DFT, as well as for liquid water with 1D inhomogeneities. These results lay the foundation for a universal ML approach to approximate exact 3D functionals spanning electronic and classical DFTs. Density functional theory ; Hybrid functional ; Orbital-free density functional theory ; Statistical physics ; Electron ; Physics ; Ising model ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies