jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. Narasimha Murthy | Kristina Lilova | Virgel Torremocha | Jothsna Kethar
Source: Journal of Student Research (article, 2024-08-31)
Field: Materials Science — Biomaterials

Reconstruction and Prevention: Nanotechnology and Tissue Engineering Approaches for Cancer Patients Cancer is a complex disease responsible for one-in-sixth deaths. Various environmental contaminants: heavy metals, synthetic organic dyes, asbestos, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons are carcinogenic in nature. This paper investigates the potential of nanotechnology in tissue repair and cancer prevention by environmental remediation of carcinogenic pollutants. Solid lipid nanoparticles, including liposomes, are promising candidates for targeted drug delivery and tissue engineering applications. These nanocarriers can be engineered to selectively bind to diseased cells, delivering therapeutic drugs and potentially facilitating tissue repair processes. The development of those nanoparticles can benefit from combining it with Artificial Intelligence, i.e. AI can help to determine the shape and the size of the solid lipid nanoparticles targeting damaged tissues, and more importantly their biocompatibility to prevent further harm to the already damaged tissues. Molecular imaging techniques used for early detection and monitoring of carcinogenic cells can identify areas of damaged tissue with high sensitivity. This information can then be used to guide the targeted delivery of nanocarriers to the tumor site. Beyond drug delivery and tissue engineering applications, nanotechnology holds potential for cancer prevention strategies by environmental remediation of known carcinogenic pollutants. For example, ferric oxide nanoparticles have been shown to eliminate cadmium and arsenic from toxic substances. Nanotechnology ; Materials science Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Bone Tissue Engineering Materials
Authors: Andrey Shavorskiy | Calley Eads | Weijia Wang | Ulrike Küst | Julia Prumbs | Robert H. Temperton | Mattia Scardamaglia | Joachim Schnadt | Jan Jnudsen
Source: Research Square (preprint, 2024-10-18)
Field: Materials Science — Materials Chemistry

Resolving Active and Spectator Species during the Oxidation of Carbon Monoxide over Pt(111) on the Microsecond Timescale Microsecond ; Carbon monoxide ; Chemistry ; Photochemistry ; Chemical physics ; Physics ; Organic chemistry ; Catalysis ; Optics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Gas Sensing Nanomaterials and Sensors
Authors: C.–H. Sung | Tzung-Ta Kao | Yu‐Cheng Lin
Source: Nanomaterials (article, 2024-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication of Three-Dimensional Dendritic Ag Nanostructures: A SERS Substrate for Non-Invasive Detection This paper discusses the fabrication of three-dimensional dendritic Ag nanostructures, showcasing pronounced Localized Surface Plasmon Resonance (LSPR) effects. These nanostructures, employed in surface-enhanced Raman scattering (SERS), function as sensors for lactic acid in artificial sweat. The dendritic structures of the silver nanoparticles (AgNPs) create an effective SERS substrate, with additional hotspots at branch junctures enhancing LSPR. We achieve differential LSPR effects by varying the distribution and spacing of branches and the overall morphology. Adjustments to electrodeposition parameters, such as current and plating solution protective agents on an anodized aluminum oxide (AAO) base, allow for precise control over LSPR intensities. By pre-depositing AgNPs, the electron transmission paths during electrodeposition are modified, which leads to optimized dendritic morphology and enhanced LSPR effects. Parameter optimization produces elongated rods with main and secondary branches, covered with uniformly sized, densely packed, non-overlapping spherical AgNPs. This configuration enhances the LSPR effect by generating additional hotspots beyond the branch tips. Fine-tuning the electrodeposition parameters improved the AgNPs' morphology, achieving uniform particle distribution and optimal spacing. Compared to non-SERS substrates, our structure amplified the Raman signal for lactic acid detection by five orders of magnitude. This method can effectively tailor SERS substrates for specific analytes and laser-based detection. Materials science ; Anodizing ; Nanostructure ; Nanotechnology ; Substrate (aquarium) ; Raman scattering ; Surface plasmon resonance ; Fabrication ; Nanoparticle ; Discrete dipole approximation ; Nanoring ; Plasmon ; Raman spectroscopy ; Optoelectronics ; Scattering ; Aluminium ; Optics ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical Analysis and Applications
Authors: N Nithya
Source: ComFin Research (article, 2024-02-14)
Field: Materials Science — Polymers and Plastics

A Synopsis of Smart Textiles for Sportswear Engaging in sports during hot and muggy weather can lead to a variety of heat-related illnesses that can have either mild or fatal consequences. The creation and manufacture of sportswear has become increasingly important with the growth of contemporary sports, particularly in many challenging and hazardous sports. The most crucial element is the choice of fiber fabrics since it affects efficiency and performance while guaranteeing safety and physical comfort. Globally, there has been a surge in demand for smart textiles and materials in recent years. E-textiles and smart clothing can serve as a second skin to help track activity beneath the surface of the body. These days, smart textiles use sensors extensively. So, in order to become a good textile engineer, we must be knowledgeable about sensor technology and how it operates. People’s changes in body composition and the environment around them can be monitored by electronic or digital sensors painted or woven into clothing. Here are several textile-based sensors that have been incorporated into clothing for a range of sports applications. It is the hope of some researchers that smart clothes will be powered by the body’s natural energy. Since the sensors are integrated into the garment, they can come into close contact with the skin of the wearer and pick up a remarkably wide range of personal data. Sensors from various production-method levels are combined in one system, depending on the application area. To ensure the correct operation of the system, the textiles used for textile-based sensors must be carefully tailored to the specific application. A literature search shows that a small number of studies have been published on the development of smart clothing for monitoring, the features of textile sensors are, Longer functional life, High performance, Easy maintenance and Reliable operations. There are various sensor technologies employed in the textile sector like, Humidity and moisture monitoring, Pressure-mapping systems, Fiber-coated sensors, Printed sensors etc. Architectural engineering ; Computer science ; Engineering Textile materials and evaluations
Authors: Lei Shi | Markus Schröder | Hans‐Dieter Meyer | Daniel Peláez | Alec M. Wodtke | Kai Golibrzuch | Anna-Maria Schönemann | Alexander Kandratsenka | Fabien Gatti
Source: arXiv (Cornell University) (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

Full Quantum dynamics study for H atom scattering from graphene This study deals with the understanding of hydrogen atom scattering from graphene, a process critical for exploring C-H bond formation and energy transfer during the atom surface collision. In our previous work (J.Chem.Phys \textbf{159}, 194102, (2023)), starting from a cell with 24 carbon atoms treated periodically, we have achieved quantum dynamics (QD) simulations with a reduced-dimensional model (15D) and a simulation in full dimensionality (75D). In the former work, the H atom attacked the top of a single C atom, enabling a comparison of QD simulation results with classical molecular dynamics (cMD). Our approach required the use of sophisticated techniques such as Monte Carlo Canonical Polyadic Decomposition (MCCPD) and Multilayer Multi-Configuration Time-Dependent Hartree (ML-MCTDH), as well as a further development of quantum flux calculations. We could benchmark our calculations by comparison with cMD calculations. We have now refined our method to better mimic experimental conditions. Specifically, rather than sending the H atom to a specific position on the surface, we have employed a plane wave for the H atom in directions parallel to the surface. Key findings for these new simulations include the identification of discrepancies between classical molecular dynamics (cMD) simulations and experiments, which are attributed to both the potential energy surface (PES) and quantum effects. Additionally, the study sheds light on the role of classical collective normal modes during collisions, providing insights into energy transfer processes. The results validate the robustness of our simulation methodologies and highlight the importance of considering quantum mechanical effects in the study of hydrogen-graphene interactions. Graphene ; Scattering ; Atom (system on chip) ; Quantum ; Dynamics (music) ; Physics ; Quantum dynamics ; Condensed matter physics ; Atomic physics ; Quantum mechanics ; Computer science Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Quantum and electron transport phenomena
Authors: Huazhang Zhang | Hao‐Cheng Thong | Louis Bastogne | Churen Gui | Xu He | Philippe Ghosez
Source: arXiv (Cornell University) (preprint, 2024-06-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Finite-temperature properties of antiferroelectric perovskite $\rm PbZrO_3$ from deep learning interatomic potential The prototypical antiferroelectric perovskite $\rm PbZrO_3$ (PZO) has garnered considerable attentions in recent years due to its significance in technological applications and fundamental research. Many unresolved issues in PZO are associated with large length- and time-scales, as well as finite temperatures, presenting significant challenges for first-principles density functional theory studies. Here, we introduce a deep learning interatomic potential of PZO, enabling investigation of finite-temperature properties through large-scale atomistic simulations. Trained using an elaborately designed dataset, the model successfully reproduces a large number of phases, in particular, the recently discovered 80-atom antiferroelectric $Pnam$ phase and ferrielectric $Ima2$ phase, providing precise predictions for their structural and dynamical properties. Using this model, we investigated phase transitions of multiple phases, including $Pbam$/$Pnam$, $Ima2$ and $R3c$, which show high similarity to the experimental observation. Our simulation results also highlight the crucial role of free-energy in determining the low-temperature phase of PZO, reconciling the apparent contradiction: $Pbam$ is the most commonly observed phase in experiments, while theoretical calculations predict other phases exhibiting even lower energy. Furthermore, in the temperature range where the $Pbam$ phase is thermodynamically stable, typical double polarization hysteresis loops for antiferroelectrics were obtained, along with a detailed elucidation of the structural evolution during the electric-field induced transitions between the non-polar $Pbam$ and polar $R3c$ phases. Perovskite (structure) ; Antiferroelectricity ; Interatomic potential ; Materials science ; Condensed matter physics ; Physics ; Crystallography ; Chemistry ; Molecular dynamics ; Quantum mechanics ; Ferroelectricity ; Optoelectronics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Perovskite Materials and Applications
Authors: Hengjie Ouyang | Wei Liu | Jiajun Tao | Yanghong Luo | Wanjia Zhang | Jiayu Zhou | Shuqi Geng | Chengpeng Zhang
Source: Scientific Reports (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

ChemReco: automated recognition of hand-drawn carbon–hydrogen–oxygen structures using deep learning Chemical molecular structures are a direct and convenient means of expressing chemical knowledge, playing a vital role in academic communication. In chemistry, hand drawing is a common task for students and researchers. If we can convert hand-drawn chemical molecular structures into machine-readable formats, like SMILES encoding, computers can efficiently process and analyze these structures, significantly enhancing the efficiency of chemical research. Furthermore, with the progress of educational technology, automated grading is gaining popularity. When machines automatically recognize chemical molecular structures and assess the correctness of the drawings, it offers great convenience to teachers. We created ChemReco, a tool designed to identify chemical molecular structures involving three atoms: C, H, and O, providing convenience for chemical researchers. Currently, there are limited studies on hand-drawn chemical molecular structures. Therefore, the primary focus of this paper is constructing datasets. We propose a synthetic image method to rapidly generate images resembling hand-drawn chemical molecular structures, enhancing dataset acquisition efficiency. Regarding model selection, the hand-drawn chemical molecule structural recognition model developed in this article achieves a final recognition accuracy of 96.90%. This model employs the encoder-decoder architecture of EfficientNet + Transformer, demonstrating superior performance compared to other encoder-decoder combinations. Computer science ; Oxygen ; Artificial intelligence ; Deep learning ; Carbon fibers ; Chemistry ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Oleg V. Angelsky | O. A. Demchenko | Vasyl Kryvetskyi | Nina Horodynska | Dmitry M. Burkovets
Source:  (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Synthesis of carbon dots for the investigation of optical fields To conduct effective research on internal energy flows, we synthesized fluorescent carbon nanodots (CNDs) as test particles. The choice of synthesis methods and starting materials was based on relatively simple technologies. Structural and optical parameters of the CNDs were determined. To address the issue of capturing and retaining fluorescent nanoparticles smaller than 100 nm, we created polymer microspheres by introducing synthesized carbon quantum dots with high quantum yield into them. Nanodot ; Quantum dot ; Fluorescence ; Quantum yield ; Materials science ; Carbon fibers ; Nanotechnology ; Polymer ; Nanoparticle ; Carbon Nanoparticles ; Carbon quantum dots ; Microsphere ; Optoelectronics ; Chemical engineering ; Optics ; Composite material ; Physics ; Engineering Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Zhigang Shuai | Qi Sun | Jiajun Ren | Tong Jiang | Weitang Li
Source:  (preprint, 2024-12-27)
Field: Chemistry — Physical and Theoretical Chemistry

Excited state structure and decay rates for aggregates Electronic excited state in molecular aggregate or exciton states continue to attract great attentions due to the increasing demands for applications of molecular optoelectronics and sensing technology. The working principle behind the application is closely related to the excited state structure and dynamic processes in molecular aggregate. In our previous review article (Aggregate 2021; 2: e91), we had focused more on the molecular mechanism for aggregation induced emission (AIE) process. Here, we are going to summarize our recent progresses on theoretical investigations on the effects of excitonic coupling ( J) and the intermolecular charge transfer (CT) on the excited state structure and dynamic processes. These are in general missing for molecular quantum chemistry studies. We will first present a novel definition of exciton coherence length which can present a bijective relation with the radiative decay rate and obviously we have clarified the confusions appeared in literature. Then, we will look at the CT effect for aggregate starting from a simple three-state model coupled with quantum chemical calculation for molecular dimer and we focus on the intensity borrowing which can turn H-aggregate into emissive when the electron transfer and hole transfer integrals possessing the same sign and being large enough. We are able to propose a molecular descriptor to design molecular materials possibly possessing both high PLQY and carrier mobility. Finally, we introduced our work on the modified energy gap law for non-radiative decay rate in aggregate. We found there exist optimal J to minimize the non-radiative decay loss. Excited state ; State (computer science) ; Atomic physics ; Physics ; Computer science ; Algorithm Photochemistry and Electron Transfer Studies ; Quantum optics and atomic interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Caja Annweiler | Simone Di Cataldo | M. W. Haverkort | Lilia Boeri
Source: arXiv (Cornell University) (preprint, 2024-10-02)
Field: Materials Science — Materials Chemistry

A method for the automatic generation of a minimal basis set of structural templates for material phase-space exploration We present a novel method for predicting binary phase diagrams through the automatic construction of a minimal basis set of representative templates. The core assumption is that any materials space can be divided into a small number of regions with similar chemical tendencies and bonding properties, and that a minimal set of templates can efficiently represent the key chemical trends across the different regions. By combining data-driven techniques with ab-initio crystal structure prediction, we can efficiently partition the materials space and construct templates reflecting variations in chemical behavior. Preliminary results demonstrate that our method predicts binary convex hulls with accuracy comparable to resource-intensive EA searches, while achieving a significant reduction in computational time (by a factor of 25). The method can be extended to ternary and multinary systems, enabling efficient high-throughput exploration and mapping of complex material spaces. By providing a transformative solution for high-throughput materials discovery, our approach paves the way for uncovering advanced quantum materials and accelerating in silico design. Template ; Basis (linear algebra) ; Set (abstract data type) ; Space (punctuation) ; Computer science ; Phase (matter) ; Algorithm ; Engineering drawing ; Mathematics ; Geometry ; Engineering ; Chemistry ; Programming language Solidification and crystal growth phenomena ; Advanced Measurement and Metrology Techniques ; Material Science and Thermodynamics
Authors: Shammi Kumar | Raja Sen | Mamta Arya | S. Dhar | Priya Johari
Source: arXiv (Cornell University) (preprint, 2024-11-22)
Field: Materials Science — Materials Chemistry

Unveiling the structural, chemical state, and optical band-gap evolution of Ta-doped epitaxial SrTiO3 thin films using first-principles calculations and spectroscopic ellipsometry In this report, the optical properties of Ta doped SrTiO3 (STO) due to its potential in transparent conducting oxides (TCOs) is explored by a combination of theoretical studies based on density functional theory and spectroscopic ellipsometry. To achieve this theoretically, we vary the concentration of Ta from 0 - 12.5% in SrTi1-xTaxO3 system by substitutional doping and report its effect on the resulting structural, chemical, electronic, chemical, and optical properties. Additionally, we perform band unfolding to shed light on the true nature of optical transitions due to Ta doping. We verify these results experimentally by fabricating epitaxial SrTi1-xTaxO3 thin films ( x = 0 - 5%) by pulsed laser deposition and obtain the optical dielectric properties of the system with the help of spectroscopic ellipsometry. By combining theoretical and experimental studies, we provide evidence that the band gap of STO increases due to Ta doping while also enhancing its electronic properties. The findings of our study offer an extensive understanding of the intricacies associated with elemental doping in perovskite oxides and propose strategies for addressing obstacles associated with TCOs. Doping ; Ellipsometry ; Epitaxy ; Materials science ; Thin film ; Condensed matter physics ; Band gap ; State (computer science) ; Optoelectronics ; Nanotechnology ; Physics ; Computer science ; Layer (electronics) Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices
Authors: Xinqiang Liu | Xian Kong
Source: ChemRxiv (preprint, 2024-03-22)
Field: Chemistry — Physical and Theoretical Chemistry

Minimal coarse-grained models of polar solvent for electrolytes: Stockmayer versus dumbbell This study explores the potential of the dumbbell solvent as a minimal model for understanding electrolytes solution in polar solvents. Our investigation involves a comparative analysis of the dumbbell model and the Stockmayer model, focusing on ion solvation and ion-ion correlations. We examine electrolytes containing symmetric monovalent salts dissolved in polar solvents while varying ion density and solvent polarity. Both models predict an augmented solvent coordination number around ions as solvent polarity increases, with the dumbbell solvent displaying a more pronounced effect. Notably, radial distribution functions (RDFs) between solvent and ions yield differing trends; Stockmayer models exhibit a non-monotonic relationship due to strong dipole-dipole interactions at higher polarity, while RDFs for ions and dumbbell solvents consistently rise. In response to increased solvent polarity, Stockmayer solvents within the ion's solvation shell undergo continuous dipole orientation shifts, whereas the dumbbell solvent predominantly adopts pointing-away dipole orientations, diminishing pointing-to orientations. This underscores the significance of the interplay between solvent molecular orientation and dipole rotation. Both models qualitatively predict ion pairing and clustering behaviors across varying solvent dipole strengths and salt concentrations. The Stockmayer solvent generally provides stronger electrostatic screening than the dumbbell solvent due to its neglect of the coupling between molecular orientation and dipole rotation. What's more, at a high dipole moment regime, ion-ion correlations in Stockmayer solvent can become stronger with increasing dipole moment due to stronger solvent-solvent correlations. This study underscores the effectiveness of the dumbbell solvent model in systematically elucidating the fundamental principles governing electrolytes, offering potential applications in the rational design of electrolyte systems. Dumbbell ; Polar ; Solvent ; Electrolyte ; Chemistry ; Materials science ; Physics ; Physical chemistry ; Organic chemistry ; Biology Electrostatics and Colloid Interactions ; Surfactants and Colloidal Systems ; Block Copolymer Self-Assembly