jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suehyun Park | Raseong Kim | Ian A. Young
Source: Journal of Applied Physics (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

Collective behavior in intrinsic polarization switching of PbTiO3 and Pb(Zr,Ti)O3 Ferroelectric materials play a pivotal role in various industrial and scientific applications due to their ability to exhibit spontaneous electric polarization above a critical temperature. The application of a sufficiently high external electric field can induce the switching of the spontaneous polarization, with the specific mechanism varying across different materials. Understanding the intrinsic switching mechanism is paramount for regulating polarization domains, thereby unlocking potential applications in nanoelectronic devices. Different types of switching mechanisms have been experimentally reported and various models have been developed, among them the nucleation-limited-switching (NLS) model, which is distinguished by nucleation and limited propagation. We investigate the intrinsic polarization switching mechanisms in PbTiO3 and Pb(Zr,Ti)O3 using molecular dynamics simulations. We found that both PbTiO3 and Pb(Zr,Ti)O3 exhibit the change of switching mechanisms as the field increases. At high electric field, they both follow homogeneous switching mechanism without the nucleation of domains. At weak electric fields, the NLS model effectively described the switching behavior of both PbTiO3 and Pb(Zr,Ti)O3, although the atomistic details of their respective switching mechanisms diverge. We demonstrate that, for PbTiO3, the switching mechanism at weak fields involves the collective behavior near nuclei such as the formation of vortices, which is characterized by the hypertoroidal moment. We also report the substantial in-plane dipolar pattern of Pb(Zr,Ti)O3 at low fields, independent of switching. This work contributes to a comprehensive understanding of ferroelectric switching and, thus, results in better prediction of designing new nanoelectronic devices. Materials science ; Polarization (electrochemistry) ; Condensed matter physics ; Chemistry ; Physics ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Advanced Memory and Neural Computing
Authors: S. A. Abd El-Azeem | Nareman M. Harpy
Source: Materials (article, 2024-07-12)
Field: Materials Science — Materials Chemistry

Radioactive Attenuation Using Different Types of Natural Rocks Humans benefit from nuclear technology, but it also generates nuclear radiation that is bad for both the environment and human health. The serious issue of radiation leakage affects many technological applications. Shielding is required to protect both users and the environment from negative side effects. This work describes the radioactive attenuation properties of some natural rocks, such as claystone, bentonitic claystone, bentonitic shale, sandstone, and basalt using a NaI(Tl) detector. The mass attenuation coefficients μm of these rocks at various photon energies, half-value layer (HVL), tenth-value layer (TVL), and mean free path (MFP) were determined. The validation of obtained values of μm was carried out against the theoretical calculations from the XCOM program, and the correlation factor and relative deviation between the two methods were evaluated. It was noted that basalt samples exhibit superior shielding parameters when compared to other rock samples. Also, the concentrations of naturally occurring radioactive elements (<sup>238</sup>U, <sup>226</sup>Ra, <sup>232</sup>Th, and <sup>40</sup>K) were measured, allowing for the calculation of environmental hazard indices and assessment of attenuation (%) efficiency for certain natural rocks, such as bentonite, sandstone, and basalt. The results revealed that increasing the thickness of Basalt-AZ from 1.5 cm to 2 cm results in an approximate 11% rise in attenuation percentage, with values reaching 77.12%, 67.2%, 67.65%, and 59.8% for NMA-U, IAEA-Th, IAEA-Ra, and IAEA-K, respectively. Basalt ; Attenuation ; Half-value layer ; Mean free path ; Electromagnetic shielding ; Geology ; Radioactive waste ; Mineralogy ; Materials science ; Nuclear physics ; Geochemistry ; Radiation shielding ; Physics ; Composite material ; Optics Radiation Shielding Materials Analysis ; Radioactivity and Radon Measurements ; Graphite, nuclear technology, radiation studies
Authors: Valentin V. Karasiev | Deyan Mihaylov | Shuai Zhang | Joshua Hinz | R. M. N. Goshadze | S. X. Hu
Source: Physics of Plasmas (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Tunable noninteracting free-energy density functionals for high-energy-density physics applications In this work, we introduce the concept of a tunable noninteracting free-energy density functional and present two examples realized: (i) via a simple one-parameter convex combination of two existing functionals and (ii) via the construction of a generalized gradient approximation (GGA) enhancement factor that contains one free parameter and is designed to satisfy a set of incorporated constraints. Functional (i), constructed as a combination of the local Thomas–Fermi and a pseudopotential-adapted GGA for the noninteracting free-energy, has already demonstrated its practical usability for establishing the high temperature end of the equation of state of deuterium [Phys. Rev. B 104, 144104 (2021)] and CHON resin [Phys. Rev. E 106, 045207 (2022)] for inertial confinement fusion applications. Hugoniot calculations for liquid deuterium are given as another example of how the application of computationally efficient orbital-free density functional theory (OF-DFT) can be utilized with the employment of the developed functionals. Once the functionals have been tuned such that the OF-DFT Hugoniot calculation matches the Kohn–Sham solution at some low-temperature point, agreement with the reference Kohn–Sham results for the rest of the high temperature Hugoniot path is very good with relative errors for compression and pressure on the order of 2% or less. Physics ; Pseudopotential ; Density functional theory ; Hybrid functional ; Energy (signal processing) ; Quantum mechanics ; Work (physics) ; Statistical physics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Quantum, superfluid, helium dynamics
Authors: Somin Lee | Yoojung Han | Changsang Yun
Source: Fashion and Textiles (article, 2024-01-22)
Field: Materials Science — Polymers and Plastics

Development of a fabric classification system using drapability and tactile characteristics Abstract When producing clothing using virtual fitting technology or purchasing textile and clothing products online, it is challenging to make judgments or communicate information about sensory characteristics, such as drapability and tactile sensations, as there are no clear objective indicators for these factors. Therefore, the study aims to develop a classification system for the sensory properties of fabrics using drapability and tactile characteristics as quantitative indicators. The developed system was verified through subjective evaluations by an expert group, and it was found to be meaningful in reflecting classification levels in practice. The drapability and tactile sensation (softness; TS7) of the fabric were classified using fuzzy c-means cluster analysis, and the results were confirmed through a subjective evaluation by experts. The classification system was then used to predict the classification group, constituted by drapability and tactile characteristics, from mechanical properties using an artificial neural network. The network was trained on 534 fabric samples for drapability and tactile sensation (softness), and it correctly predicted 202 samples out of 243 validation data, with a forecasting accuracy of 83.5%. The developed classification system enables predictions and judgments about subjective characteristics like fabric drapability and tactile sensation based on the mechanical property values of various samples. Artificial intelligence ; Clothing ; Computer science ; Sensation ; Artificial neural network ; Pattern recognition (psychology) ; Textile ; Machine learning ; Psychology ; Cognitive psychology ; Materials science Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Industrial Vision Systems and Defect Detection
Authors: Sohyung Kim | Jiheon Lim | Suyeon Kim | Joonseok Lee
Source: Advanced Materials (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Near‐Infrared Luminescent Imaging‐Based 3D QR Cube Platform for Spatial Information Storage and Security The growing significance of information technology requires advanced information storage and security solutions. While extending traditional 2D codes with additional parameters has led to promising 3D codes, increasing information capacity and security remains challenging. Herein, a 3D quick response (QR) cube platform that utilizes near-infrared (NIR)-to-NIR upconversion nanoparticles as light-emitting probes, benefiting from their photostability and low scattering properties. These features enable precise reconstruction of the 3D QR cube. The platform employs volumetric space for information encoding by leveraging spatial information in a 3D environment, demonstrating potential to significantly increase information capacity and facilitate access from all three spatial dimensions (x, y, z), while enhancing security. This study develops a platform for analyzing and reconstructing 3D QR cubes using NIR imaging and employs a convolutional neural network model to determine the 3D structure from image intensity variations, achieving 99.9% accuracy in predicting cube configurations. By leveraging 3D spatial information and logical circuits, the encryption method has the potential to significantly surpass the encryption strength of traditional 2D codes. The findings demonstrate high prediction accuracy and introduce new possibilities for multi-level encryption with spatial security keys in 3D space, offering a robust solution for advanced information storage and security. Encryption ; Cube (algebra) ; Computer science ; Spatial analysis ; Encoding (memory) ; Information security ; Compressed sensing ; Convolutional neural network ; Materials science ; Artificial intelligence ; Remote sensing ; Computer network Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials ; Carbon and Quantum Dots Applications
Authors: Prabhakar C. G | M Sreenivas Reddy | Shashanka Rajendrachari | Rayappa Shrinivas Mahale | V. Mahesh | Anup Pandith
Source: Journal of Composites Science (article, 2024-01-13)
Field: Materials Science — Polymers and Plastics

The Behavior of Banyan (B)/Banana (Ba) Fibers Reinforced Hybrid Composites Influenced by Chemical Treatment on Tensile, Bending and Water Absorption Behavior: An Experimental and FEA Investigation Natural fiber-based composites are highly prioritized in present industries due to their properties and benefits over synthetic fibers. Due to their biodegradable nature, banyan and banana fibers were used for the present work. This paper deals with an experimental and FEA investigation of the tensile and bending behavior of banyan (B) and banana (Ba)-reinforced composites with different volume fractions, such as 25B/25Ba, 30B/20Ba, and 35B/15Ba, with a 50% weight fraction of epoxy resin and different fiber orientations. The hybrid composites treated with a 5% NaOH solution have better results as compared to untreated hybrid composites, with a volume fraction of 30% banyan fibers and 20% banana fiber (30B/20Ba), giving greater tensile and flexural properties for both treated and untreated fiber composites when compared to other volume fraction composites at 0/0/0/0 orientation. The maximum tensile and bending strength was found in the 30B/20Ba volume fractions to be 63.37 MPa and 67.07 MPa, respectively. For treated fiber composites, water absorption increases with an increase in the duration of immersion in composites up to 144 h. Composite material ; Materials science ; Ultimate tensile strength ; Flexural strength ; Volume fraction ; Absorption of water ; Epoxy ; Fiber ; Banyan ; Natural fiber ; Composite number ; Bending Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research
Authors: Satoshi Kawaichi | Rhitu Kotoky | Jacek Fiutowski | Amelia‐Elena Rotaru
Source: npj Biofilms and Microbiomes (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Adaptation of a methanogen to Fe0 corrosion via direct contact Due to unique genomic adaptations, Methanococcus maripaludis Mic1c10 is highly corrosive when in direct contact with Fe<sup>0</sup>. A critical adaptation involves increased glycosylation of an extracellular [NiFe]-hydrogenase, facilitating its anchoring to cell surface proteins. Corrosive strains adapt to the constructed environment via horizontal gene transfer while retaining ancestral genes important for intraspecies competition and surface attachment. This calls for a reevaluation of how the built environment impacts methane cycling. Methanogen ; Adaptation (eye) ; Corrosion ; Materials science ; Business ; Psychology ; Biology ; Metallurgy ; Neuroscience ; Ecology Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Fuel Cells and Related Materials
Authors: Ana Karla da Silva Oliveira | Rodnei Rizzo | Carlos Augusto Alves Cardoso Silva | Natália Correr Ré | M. Caron | Peterson Ricardo Fiorio
Source: AgriEngineering (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

Prediction of Corn Leaf Nitrogen Content in a Tropical Region Using Vis-NIR-SWIR Spectroscopy Traditional techniques for measuring leaf nitrogen content (LNC) involve slow and laborious processes, and radiometric data have been used to assist in the nutritional analysis of plants. Therefore, this study aimed to evaluate the performance of LNC predictions in corn plants based on laboratory hyperspectral Vis-NIR-SWIR data. The treatments corresponded to 60, 120, 180, and 240 kg ha−1 of nitrogen, in addition to the control (0 kg ha−1), and they were distributed using a randomized complete block design. At the laboratory, hyperspectral data of the leaves and LNC were obtained. The hyperspectral data were used in the calculation of different vegetation indices (VIs), which were applied in a predictive model—partial least squares regression (PLSR)—and the capacity of the prediction was assessed. The combination of bands and VIs generated a better prediction (0.74 &lt; R2 &lt; 0.87; 1.00 &lt; RMSE &lt; 1.50 kg ha−1) in comparison with the individual prediction by band (0.69 &lt; R2 &lt; 0.85; 1.00 &lt; RMSE &lt; 1.77 kg ha−1) and by VI (0.55 &lt; R2 &lt; 0.68; 1.00 &lt; RMSE &lt; 1.78 kg ha−1). Hyperspectral data offer a new opportunity to monitor the LNC in corn plants, especially in the region comprising the bands from 450 to 750 nm, since these were the bands that were most sensitive to the LNC. Nitrogen ; Spectroscopy ; Environmental science ; Content (measure theory) ; Agronomy ; Chemistry ; Mathematics ; Physics ; Biology ; Astronomy Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Alper Alver | Bahar Akyuz | Behlul Koc | Emine Baştürk | Murat Kaya | Mustafa Işık
Source: Arabian Journal for Science and Engineering (article, 2024-04-16)
Field: Chemistry — Organic Chemistry

Carbamazepine Adsorption onto Giant Macroporous Silica and Adaptive Neuro-Fuzzy Inference System Modeling Abstract There is an imperative need to eliminate pharmaceutical residues from aquatic environments due to their hazardous properties, including toxicity, mutagenicity, and carcinogenicity, particularly when present in water sources. Conventional water treatment methods have proven insufficient in addressing nano-pollutants such as pharmaceutical residues. Consequently, the ongoing quest for economically viable, sustainable, and environmentally friendly removal mechanisms persists. In this particular study, we employed Giant Macroporous Silica (GMS) derived from marine sponges as a promising biosorbent. GMS exhibits commendable characteristics, including a high specific surface area, swift mass transfer capabilities, and non-discriminatory adsorption qualities. The efficacy of GMS in adsorbing carbamazepine (CBZ), a common drug residue, was scrutinized under diverse experimental conditions, including a sorbate/sorbent ratio ranging from 0.005 to 1.500 weight ratio, contact times spanning from 0 to 240 min, and initial pH values ranging from 5 to 9. Remarkably, at a concentration of 1000 µg L −1 , GMS demonstrated an attractive adsorption rate (98.88%) of carbamazepine at pH 7.07, within 90 min. To enhance our understanding, we developed an ANFIS model utilizing the experimental parameters as inputs. The developed model exhibited a high correlation coefficient of 0.9944% and a root mean square error (RMSE) of 1.6693, indicating its dependability in accurately predicting the adsorption of CBZ on GMS. The results of our study highlight the efficacy of GMS in adsorbing CBZ, suggesting its considerable potential for adsorbing other pharmaceutical residues and nano-pollutants. Furthermore, we propose the possibility of developing a solid-phase extraction cartridge from GMS. Adsorption ; Carbamazepine ; Sorbent ; Hazardous waste ; Chemistry ; Pollutant ; Chemical engineering ; Contamination ; Chromatography ; Environmental chemistry ; Pulp and paper industry ; Waste management ; Organic chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Analytical chemistry methods development
Authors: Andang Miatmoko | Rifda Tarimi Octavia | Tamasa Araki | Takeshi Annoura | Retno Sari
Source: Saudi Pharmaceutical Journal (article, 2024-04-24)
Field: Materials Science — Biomaterials

Advancing liposome technology for innovative strategies against malaria This review discusses the potential of liposomes as drug delivery systems for antimalarial therapies. Malaria continues to be a significant cause of mortality and morbidity, particularly among children and pregnant women. Drug resistance due to patient non-compliance and troublesome side effects remains a significant challenge in antimalarial treatment. Liposomes, as targeted and efficient drug carriers, have garnered attention owing to their ability to address these issues. Liposomes encapsulate hydrophilic and/or hydrophobic drugs, thus providing comprehensive and suitable therapeutic drug delivery. Moreover, the potential of passive and active drug delivery enables drug concentration in specific target tissues while reducing adverse effects. However, successful liposome formulation is influenced by various factors, including drug physicochemical characteristics and physiological barriers encountered during drug delivery. To overcome these challenges, researchers have explored modifications in liposome nanocarriers to achieve efficient drug loading, controlled release, and system stability. Computational approaches have also been adopted to predict liposome system stability, membrane integrity, and drug-liposome interactions, improving formulation development efficiency. By leveraging computational methods, optimizing liposomal drug delivery systems holds promise for enhancing treatment efficacy and minimizing side effects in malaria therapy. This review consolidates the current understanding and highlights the potential of liposome strategies against malaria. Liposome ; Nanocarriers ; Drug delivery ; Drug ; Malaria ; Targeted drug delivery ; Medicine ; Intensive care medicine ; Pharmacology ; Nanotechnology ; Risk analysis (engineering) ; Materials science ; Immunology Nanoparticle-Based Drug Delivery ; Lipid Membrane Structure and Behavior ; Malaria Research and Control
Authors: Angela Balzano | Maks Merela | Veronica De Micco
Source: Forests (article, 2024-10-14)
Field: Chemistry — Organic Chemistry

Advances in Wood Anatomy: Cutting-Edge Techniques for Identifying Wood and Analyzing Its Structural Modifications Wood, a natural lignocellulosic polymer, plays several important roles in trees, including water conduction, structural support, and nutrient storage [...] Enhanced Data Rates for GSM Evolution ; Engineered wood ; Computer science ; Pulp and paper industry ; Materials science ; Engineering ; Composite material ; Artificial intelligence Wood and Agarwood Research ; Forest ecology and management ; Wood Treatment and Properties
Authors: S. Manikandan | A. Nanthakumar
Source: Thermal Science (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Development of a predictive model for thermal conductivity in graphene nanoplatelets-infused damper oil using ANN/RSM This study aims to develop a predictive model for the thermal conductivity of graphene nanoplatelets/SAE10W oil nanofluids using artificial neural networks (ANN) and response surface methodology (RSM). Generally, the property of thermal conductivity has been measured to enhance the heat transfer efficiency of traditional heat transfer fluids. Experiments were conducted using a thermal constants analyzer at different operating conditions, such as varying the volume concentrations of nanoparticles from 0.050% to 0.150% and increasing the temperature from 20?C to 80?C. Results showed an improvement in the thermal conductivity of the nanofluid, ranging from 19% to 41%. A single hidden layer with 12 neurons was found to be the most effective architecture for the ANN model. Additionally, a response surface was closely fitted to experimental data points in the RSM. Then, mean squared error, root mean square error, and R2 values were employed to validate the accuracy of the predicted models. The correlation coefficients of the ANN and RSM models were 0.99761 and 0.9877, respectively. Also, the accuracy of the models was assessed in terms of margins of deviation. The margin of deviation for the ANN model ranged between +0.3926% and ?0.4640%, whereas for the RSM model, it was between +0.4137% and ?0.4166%. The comparison of the ANN model indicates greater accuracy than the RSM technique. This method for predicting the thermal conductivity of graphene nanoplatelets/SAE10W oil nanofluids is both cost-effective and inventive, minimizing experimental re-search durations. Damper ; Thermal conductivity ; Materials science ; Composite material ; Response surface methodology ; Thermal ; Computer science ; Structural engineering ; Physics ; Thermodynamics ; Machine learning ; Engineering Polymer Nanocomposites and Properties ; Synthesis and properties of polymers ; Tribology and Wear Analysis