jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuting Dai | Miao Xie | Can Wang | Yuying Wang | Bin Han | Shunqi Xu | Kexin Wang | Anna Zhuravlova | Bin Xu | Lifeng Chi | Wenjing Tian | Paolo Samorı́ | Zhaoyang Liu
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Highly efficient organic–graphene hybrid photodetectors <i>via</i> molecular peripheral editing Hybrid systems based on graphene and organic molecules are highly appealing for "correcting" the limited optoelectronic properties of 2D materials. However, an in-depth understanding of the correlation between the structure of the molecular sensitizer and the physical properties of the hybrid toward high-performance organic-graphene hybrid photodetectors remains elusive. Herein, an <i>ad hoc</i> molecular design <i>via</i> a peripheral editing approach on the organic molecules is employed to elucidate the structure-property relationship when interfaced with graphene forming hybrid systems. Efficient doping of graphene can be attained by physisorption of tetrathiafulvalene molecules exposing electron-donating peripheral groups, benefiting from a strong coupling yielding efficient charge transfer, ultimately leading to photodetectors with an ultra-high responsivity of 1.1 × 10<sup>7</sup> A W<sup>-1</sup> and a specific detectivity of 6.5 × 10<sup>14</sup> Jones, thereby outperforming state-of-the-art graphene-based photodetectors. These results offer valuable insights for future optimization of graphene-based photodetectors through molecular functionalization. Graphene ; Materials science ; Tetrathiafulvalene ; Photodetector ; Organic molecules ; Molecule ; Characterization (materials science) ; Optoelectronics ; Nanotechnology ; Organic chemistry ; Chemistry Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry ; Molecular Junctions and Nanostructures
Authors: Alaleh Vaghef‐Koodehi | Blanca Lapizco‐Encinas
Source: Biosensors (article, 2024-02-22)
Field: Chemistry — Physical and Theoretical Chemistry

Switching Separation Migration Order by Switching Electrokinetic Regime in Electrokinetic Microsystems Analyte migration order is a major aspect in all migration-based analytical separations methods. Presented here is the manipulation of the migration order of microparticles in an insulator-based electrokinetic separation. Three distinct particle mixtures were studied: a binary mixture of particles with similar electrical charge and different sizes, and two tertiary mixtures of particles of distinct sizes. Each one of the particle mixtures was separated twice, the first separation was performed under low voltage (linear electrokinetic regime) and the second separation was performed under high voltage (nonlinear electrokinetic regime). Linear electrophoresis, which discriminates particles by charge, is the dominant electrokinetic effect in the linear regime; while nonlinear electrophoresis, which discriminates particles by size and shape, is the dominant electrokinetic effect in the nonlinear regime. The separation results obtained with the three particle mixtures illustrated that particle elution order can be changed by switching from the linear electrokinetic regime to the nonlinear electrokinetic regime. Also, in all cases, better separation performances in terms of separation resolution (<i>Rs</i>) were obtained by employing the nonlinear electrokinetic regime allowing nonlinear electrophoresis to be the discriminatory electrokinetic mechanism. These findings could be applied to analyze complex samples containing bioparticles of interest within the micron size range. This is the first report where particle elution order is altered in an iEK system. Electrokinetic phenomena ; Electrophoresis ; Particle size ; Particle (ecology) ; Chemistry ; Analytical Chemistry (journal) ; Nonlinear system ; Chromatography ; Materials science ; Nanotechnology ; Physics Electrostatics and Colloid Interactions ; Microfluidic and Bio-sensing Technologies ; Microfluidic and Capillary Electrophoresis Applications
Authors: Yuelong Cao | Qi Guan | Yang He | Xinmei Wang | Zhang Lin | Enling Li | Wanli Jia
Source: Journal of Physics D Applied Physics (article, 2024-01-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Strain-induced enhancement of 2D electron gas density in AlGaN/GaN heterojunction: a first-principles study Abstract In this study, we investigated the impact of strain on the electronic structure and polarization of Al x Ga 1− x N and AlGaN/GaN heterojunctions using first-principles density functional theory. Our findings reveal that, in the absence of strain, the band gap and electron effective mass of Al x Ga 1− x N increase with higher Al composition. Similarly, the spontaneous and piezoelectric polarization also increase accordingly. Moreover, under biaxial 5% tensile strain and 5% compressive strain, the two-dimensional (2D) electron gas surface density in the AlGaN/GaN heterojunction reaches 8.12 × 10 12 cm −2 and 2.50 × 10 12 cm −2 , respectively. Comparatively, the surface density without strain is 5.62 × 10 12 cm −2 . Tensile strain significantly enhances the 2D electron gas surface density, which holds potential theoretical value for improving the electrical performance of AlGaN/GaN high electron mobility transistors. Heterojunction ; Materials science ; Tensile strain ; Strain (injury) ; Fermi gas ; Electron density ; Density functional theory ; Polarization (electrochemistry) ; Electron ; Band gap ; Condensed matter physics ; Ultimate tensile strength ; Optoelectronics ; Piezoelectricity ; Composite material ; Chemistry ; Computational chemistry ; Physics ; Physical chemistry Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; ZnO doping and properties
Authors: Prathyush Poduval | Mathias S. Scheurer
Source: Nature Communications (article, 2024-02-24)
Field: Materials Science — Materials Chemistry

Vestigial singlet pairing in a fluctuating magnetic triplet superconductor and its implications for graphene superlattices Abstract Stacking and twisting graphene layers allows to create and control a two-dimensional electron liquid with strong correlations. Experiments indicate that these systems exhibit strong tendencies towards both magnetism and triplet superconductivity. Motivated by this phenomenology, we study a 2D model of fluctuating triplet pairing and spin magnetism. Individually, their respective order parameters, d and N , cannot order at finite temperature. Nonetheless, the model exhibits a variety of vestigial phases, including charge-4 e superconductivity and broken time-reversal symmetry. Our main focus is on a phase characterized by finite d ⋅ N , which has the same symmetries as the BCS state, a Meissner effect, and metastable supercurrents, yet rather different spectral properties: most notably, the suppression of the electronic density of states at the Fermi level can resemble that of either a fully gapped or nodal superconductor, depending on parameters. This provides a possible explanation for recent tunneling experiments in the superconducting phase of graphene moiré systems. Pairing ; Graphene ; Superconductivity ; Superlattice ; Singlet state ; Condensed matter physics ; Physics ; Quantum mechanics Graphene research and applications ; Physics of Superconductivity and Magnetism ; Quantum and electron transport phenomena
Authors: Martin Mičica | Sabrine Ayari | Minoosh Hemmat | Mehdi Arfaoui | Daniel Vala | Kamil Postava | Hadrien Vergnet | J. Tignon | J. Mangeney | Shasha Guo | Xuechao Yu | Qi Jie Wang | Zheng Liu | S. Jaziri | Francesca Carosella | R. Ferreira | S. Dhillon
Source: Advanced Functional Materials (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Determining Bandgaps in the Layered Group‐10 2D Transition Metal Dichalcogenide PtSe<sub>2</sub> Abstract Unlike traditional group‐6 transition metal dichalcogenides (TMDs), group‐10 TMDs such as PtSe 2 and PdTe 2 possess highly tuneable indirect bandgaps, transitioning from semiconducting in the near‐infrared to semimetal behavior with a number of monolayers (MLs). This opens up the possibility of TMD‐based mid‐infrared and terahertz optoelectronics. Despite this large potential, the optical properties of such materials have shown an extremely large disparity between that predicted and measured. For example, simulations show that a few MLs is required for the semiconductor–semimetal transition, whilst tens of MLs is found experimentally. This is a result of widely used optical extrapolation methods to determine bandgaps, such as the Tauc plot approach, that are not adapted here owing to i) nearby direct transitions, ii) the material dimensionality and iii) large changes in the non‐parabolic bandstructure with MLs. Here, uniquely combining optical ellipsometry to determine the complex permittivity, terahertz time resolved spectroscopy for the complex conductivity and in‐depth density functional theory (DFT) simulations, it is shown that the optical properties and bandstructure can be determined reliably and demonstrate clearly that the semiconductor‐semimetal transition occurs for PtSe 2 layers ≈5 MLs. The microscopic origins of the observed transitions and the crucial role of the Coulomb interaction for thin semiconducting layers, and that of interlayer van der Waals forces for multilayer semimetallic samples are also demonstrated. This work of combining complimentary experimental techniques and extensive simulations avoids the application of constrained extrapolation methods to determine the optical properties of group‐10 TMDs, and will be of importance for future mid‐infrared and terahertz applications. Materials science ; Semimetal ; Semiconductor ; Bismuth ; Condensed matter physics ; Optical conductivity ; Monolayer ; Ellipsometry ; Infrared ; Band gap ; Terahertz radiation ; Density functional theory ; Transition metal ; van der Waals force ; Optoelectronics ; Thin film ; Optics ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Huihui Wang | Heliang Yin | Weiwei Yin | Yongli Song | Hong Chen
Source: Scientific Reports (article, 2024-06-06)
Field: Chemistry — Inorganic Chemistry

Cu(II)-based complex loaded with drug paclitaxel hydrogels against thyroid cancer and optimizing novel derivatives This study introduces a novel approach for synthesizing a Cu(II)-based coordination polymer (CP), {[Cu(L)(4,4´-OBA)]·H<sub>2</sub>O}n (1), using a mixed ligand method. The CP was successfully prepared by reacting Cu(NO<sub>3</sub>)<sub>2</sub>·3H<sub>2</sub>O with the ligand 3,6-bis(benzimidazol-1-yl)pyridazine in the presence of 4,4´-H<sub>2</sub>OBA, demonstrating an innovative synthesis strategy. Furthermore, a novel hydrogel composed of hyaluronic acid (HA) and carboxymethyl chitosan (CMCS) with a porous structure was developed for drug delivery purposes. This hydrogel facilitates the encapsulation of CP1, and enables the loading of paclitaxel onto the composite to form HA/CMCS-CP1@paclitaxel. In vitro cell experiments demonstrated the promising modulation of thyroid cancer biomarker genes S100A6 and ARID1A by HA/CMCS-CP1@paclitaxel. Finally, reinforcement learning simulations were employed to optimize novel metal-organic frameworks, underscoring the innovative contributions of this study. Paclitaxel ; Self-healing hydrogels ; Hyaluronic acid ; Chemistry ; Pyridazine ; Drug delivery ; Ligand (biochemistry) ; Combinatorial chemistry ; Polymer ; Drug ; Materials science ; Nuclear chemistry ; Pharmacology ; Polymer chemistry ; Stereochemistry ; Receptor ; Organic chemistry ; Biochemistry ; Cancer ; Biology Metal-Organic Frameworks: Synthesis and Applications ; S100 Proteins and Annexins ; Nanoplatforms for cancer theranostics
Authors: Tran Van Tan | Minh Phuong Le | Nguyen Hai Pham | Thi Hai Yen Le | Viet Tuyen Nguyen | Thi Hong Pham | Tan San Nguyen | Quang Hoa Nguyen | Van Thanh Pham | T. Nguyen | Cong Toan Nguyen | A. Ngac | O. Martı́nez | Thi Ha Tran
Source: Applied Surface Science (article, 2024-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unraveling the mechanism of photo-induced surface enhanced Raman scattering on ZnO/Au thin films Raman scattering ; Materials science ; Raman spectroscopy ; Excitation ; Thin film ; Optoelectronics ; Substrate (aquarium) ; Detection limit ; Analytical Chemistry (journal) ; Nanotechnology ; Optics ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Nanoparticles: synthesis and applications
Authors: Jan P.M. Andries | Yvan Vander Heyden
Source: Analytical and Bioanalytical Chemistry (article, 2024-08-03)
Field: Chemistry — Analytical Chemistry

Calibration transfer between NIR instruments using optimally predictive calibration subsets Calibration ; Analytical Chemistry (journal) ; Computer science ; Chromatography ; Chemistry ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Calibration and Measurement Techniques ; Fault Detection and Control Systems
Authors: J. Elliott Fowler | Matthew Kottwitz | Nat Trask | Rémi Dingreville
Source: Integrating materials and manufacturing innovation (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Beyond Combinatorial Materials Science: The 100 Prisoners Problem Pace ; Data science ; Computer science ; Field (mathematics) ; Status quo ; Perspective (graphical) ; Big data ; Artificial intelligence ; Political science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Jason Yang | Ariane Mora | Shengchao Liu | Bruce J. Wittmann | Anima Anandkumar | Frances H. Arnold | Yisong Yue
Source: arXiv (Cornell University) (preprint, 2024-06-21)
Field: Chemistry — Spectroscopy

CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are no standardized benchmarks to evaluate these methods. We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1) classification of a protein sequence by its enzyme commission (EC) number and (2) retrieval of an EC number given a chemical reaction. For each task, we design train-test splits to evaluate different kinds of out-of-distribution generalization that are relevant to real use cases. For the classification task, we provide baselines for state-of-the-art methods. Because the retrieval task has not been previously formalized, we propose a method called Contrastive Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this task and compare it to the recent method, CLIPZyme. CARE is available at https://github.com/jsunn-y/CARE/. Suite ; Benchmark (surveying) ; Computer science ; Information retrieval ; Artificial intelligence ; Political science ; Geography ; Cartography Advanced Proteomics Techniques and Applications
Authors: Naglaa Ahmed | Mosaad S. Mohamed | Samir M. Awad | Neama Abdallah Abd El-tawab | Mohamed Gaballah | Ahmed M. Said
Source: Journal of Enzyme Inhibition and Medicinal Chemistry (article, 2024-02-13)
Field: Chemistry — Organic Chemistry

Design, synthesis, molecular modelling and biological evaluation of novel 6-amino-5-cyano-2-thiopyrimidine derivatives as potent anticancer agents against leukemia and apoptotic inducers Herein, a novel series of 6-amino-5-cyano-2-thiopyrimidines and condensed pyrimidines analogues were prepared. All the synthesized compounds <b>(1a-c, 2a-c, 3a-c, 4a-r</b> and <b>5a-c)</b> were evaluated for <i>in vitro</i> anticancer activity by the National Cancer Institute (NCI; MD, USA) against 60 cell lines. Compound <b>1c</b> showed promising anticancer activity and was selected for the five-dose testing. Results demonstrated that compound <b>1c</b> possessed broad spectrum anti-cancer activity against the nine cancerous subpanels tested with selectivity ratio ranging from 0.7 to 39 at the GI<sub>50</sub> level with high selectivity towards leukaemia. Mechanistic studies showed that Compound <b>1c</b> showed comparable activity to Duvelisib against PI3Kδ (IC<sub>50</sub> = 0.0034 and 0.0025 μM, respectively) and arrested cell cycle at the S phase and displayed significant increase in the early and late apoptosis in HL60 and leukaemia SR cells. The necrosis percentage showed a significant increase from 1.13% to 3.41% in compound <b>1c</b> treated HL60 cells as well as from 1.51% to 4.72% in compound <b>1c</b> treated leukaemia SR cells. Also, compound <b>1c</b> triggered apoptosis by activating caspase 3, Bax, P53 and suppressing Bcl<sub>2</sub>. Moreover, <b>1c</b> revealed a good safety profile against human normal lung fibroblast cell line (WI-38 cells). Molecular analysis of Duvelisib and compound <b>1c</b> in PI3K was performed. Finally, these results suggest that 2-thiopyrimidine derivative <b>1c</b> might serve as a model for designing novel anticancer drugs in the future. HL60 ; Apoptosis ; Chemistry ; Cell culture ; Leukemia ; Cell cycle ; Lead compound ; In vitro ; Cell cycle checkpoint ; Stereochemistry ; Cancer research ; Pharmacology ; Biochemistry ; Biology ; Immunology ; Genetics Quinazolinone synthesis and applications ; Synthesis and biological activity ; Cancer therapeutics and mechanisms
Authors: Akshay Subramanian | Wenhao Gao | Regina Barzilay | Jeffrey C. Grossman | Tommi Jaakkola | Stefanie Jegelka | Mingda Li | Ju Li | Wojciech Matusik | Elsa Olivetti | Connor W. Coley | Rafael Gómez‐Bombarelli
Source:  (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards GenAI may enable computers to create drugs or sustainable materials. But impact in chemistry happens further downstream, following synthesis, testing, and scale-up. We propose paths for closing this execution gap and creating powerful, safe AIs that can realize novel chemicals. Closing (real estate) ; Generative grammar ; Computer science ; Environmental science ; Business ; Artificial intelligence ; Finance Machine Learning in Materials Science
Authors: Anders Hellman
Source: Current Opinion in Electrochemistry (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

A brief overview of deep generative models and how they can be used to discover new electrode materials As humankind searches for sustainable energy solutions, the demand for electrochemistry has increased. Thus, new and more advanced electrode materials are required. However, finding electrodes that meet the necessary performance is a challenge. Machine learning models can predict key properties such as catalytic activity and stability with surprisingly good accuracy, thus accelerating the process of evaluating materials. However, in most cases, the same models cannot explain how to generate new material compositions. Here, deep generative models can become very valuable. Although issues related to data availability and understanding how these models work still exist, combining deep generative models with computer simulations and laboratory experiments holds great potential for developing the next generation of electrodes. This short review will show recent progress in using deep generative models in related material fields and stress how these models can accelerate the discovery of electrode materials. Generative grammar ; Nanotechnology ; Electrode ; Computer science ; Materials science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Sebastian Fehse | Anish Das | Detlev Belder
Source: Chemical Communications (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integration of a recyclable silver substrate for <i>in situ</i> surface-enhanced Raman spectroscopy in digital microfluidics We report a new chip-integrated recyclable SERS substrate, achieved by photochemical deposition of silver nanoparticles onto titanium dioxide (TiO<sub>2</sub>) thin film. Facilitated by the photocatalytic activity of titanium dioxide the SERS substrate can be recycled for multiple analysis. This enables quasi-real time detection of various compounds in an automated and reusable DMF-SERS platform. In situ ; Microfluidics ; Substrate (aquarium) ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Nanotechnology ; Digital microfluidics ; Materials science ; Chemistry ; Chemical engineering ; Optoelectronics ; Raman scattering ; Engineering ; Optics ; Organic chemistry ; Physics ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Electrowetting and Microfluidic Technologies ; Biosensors and Analytical Detection
Authors: Pranav Shetty | Aishat Adeboye | Sonakshi Gupta | Chao Zhang | Rampi Ramprasad
Source: Chemistry of Materials (preprint, 2024-08-06)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing We present a simulation of various active learning strategies for the discovery of polymer solar cell donor/acceptor pairs using data extracted from the literature spanning ∼20 years by a natural language processing pipeline. While data-driven methods have been well established to discover novel materials faster than Edisonian trial-and-error approaches, their benefits have not been quantified for material discovery problems that can take decades. Our approach demonstrates a potential reduction in discovery time by approximately 75%, equivalent to a 15 year acceleration in material innovation. Our pipeline enables us to extract data from greater than 3300 papers which is ∼5 times larger and therefore more diverse than similar data sets reported by others. We also trained machine learning models to predict the power conversion efficiency and used our model to identify promising donor–acceptor combinations that are as yet unreported. We thus demonstrate a pipeline that goes from published literature to extracted material property data which in turn is used to obtain data-driven insights. Our insights include active learning strategies that can be used to train strong predictive models of material properties or be robust to the initial material system used. This work provides a valuable framework for data-driven research in materials science. Natural (archaeology) ; Natural polymers ; Data science ; Polymer ; Astrobiology ; Nanotechnology ; Computer science ; Materials science ; History ; Physics ; Composite material ; Archaeology Machine Learning in Materials Science ; Software Engineering Research