jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Domenica Tonelli | Matteo Tonelli | Stefano Gianvittorio | Andreas Lesch
Source: Micromachines (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

LDH-Based Voltammetric Sensors Layered double hydroxides (LDHs), also named hydrotalcite-like compounds, are anionic clays with a lamellar structure which have been extensively used in the last two decades as electrode modifiers for the design of electrochemical sensors. These materials can be classified into LDHs containing or not containing redox-active centers. In the former case, a transition metal cation undergoing a reversible redox reaction within a proper potential window is present in the layers, and, therefore, it can act as electron transfer mediator, and electrocatalyze the oxidation of an analyte for which the required overpotential is too high. In the latter case, a negatively charged species acting as a redox mediator can be introduced into the interlayer spaces after exchanging the anion coming from the synthesis, and, again, the material can display electrocatalytic properties. Alternatively, due to the large specific surface area of LDHs, molecules with electroactivity can be adsorbed on their surface. In this review, the most significant electroanalytical applications of LDHs as electrode modifiers for the development of voltammetric sensors are presented, grouping them based on the two types of materials. Layered double hydroxides ; Overpotential ; Redox ; Hydrotalcite ; Intercalation (chemistry) ; Electrochemistry ; Lamellar structure ; Inorganic chemistry ; Electron transfer ; Electrode ; Electrocatalyst ; Materials science ; Chemistry ; Adsorption ; Combinatorial chemistry ; Nanotechnology ; Catalysis ; Photochemistry ; Organic chemistry ; Physical chemistry ; Crystallography Layered Double Hydroxides Synthesis and Applications ; Supercapacitor Materials and Fabrication ; Polyoxometalates: Synthesis and Applications
Authors: Jun Harada | Mika Takehisa | Yuto Kawamura | Hiroyuki Hasegawa | Tomoyasu Usui
Source: Journal of the American Chemical Society (article, 2024-07-19)
Field: Materials Science — Materials Chemistry

Solid Solutions of Plastic/Ferroelectric Crystals: Toward Tailor-Made Functional Materials Plastic crystals that show ferroelectricity are highly promising materials for a wide range of applications. Their inherent remarkable malleability and highly symmetric cubic structures in the plastic crystal phase ensure that their ferroelectricity and related properties are retained in their bulk polycrystals. To develop functional materials based on such plastic/ferroelectric crystals, methods to tune their properties for specific applications are required. Here, we report the preparation of solid solutions of plastic/ferroelectric ionic crystals by mixing crystals with a common anion but different cations, or crystals with a common cation but different anions, which allows a continuous modification of the Curie temperature of the ferroelectric system over a range of 100 K. This adjustment of the Curie temperature allows the flexible tuning of the pyroelectric properties of the solid solutions, including a significant enhancement of room-temperature performance. The solid solutions also exhibit morphotropic phase boundaries in the composition-temperature phase diagrams, which shows an abrupt change in crystal structures with a variation of composition. This study showcases a simple and versatile property-tuning method that can be expected to pave the way for major progress in the development of materials based on plastic/ferroelectric crystals, which will eventually advance to the stage of pursuing tailor-made functional materials with desired properties. Ferroelectricity ; Plastic crystal ; Curie temperature ; Pyroelectricity ; Phase (matter) ; Solid solution ; Crystal (programming language) ; Chemistry ; Ionic bonding ; Phase boundary ; Mixing (physics) ; Materials science ; Chemical physics ; Nanotechnology ; Crystallography ; Condensed matter physics ; Ion ; Dielectric ; Optoelectronics ; Organic chemistry ; Ferromagnetism ; Computer science Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Jogirdas Vainauskas | Alexander Wahrhaftig‐Lewis | Tomislav Friščić
Source: Angewandte Chemie International Edition (article, 2024-05-23)
Field: Chemistry — Physical and Theoretical Chemistry

Utilizing “Latent” Carbon: Repositioning Hydrogen‐Bonded Synthons and Assemblies via Halogen Bonding to π‐Systems Molecules bearing carboxylic acid, amide, and hydroxyl groups are ubiquitous in crystal engineering, where robust hydrogen-bonded synthons centred on these functionalities enable reliable crystal structure design. We now show that halogen bonding to the carbon π-system of such molecules, traditionally ignored in crystal engineering, permits the recognition and directional assembly of the resulting hydrogen-bonded structural subunits, leaving the archetypal hydrogen-bonded ring, ladder, and chain homosynthons intact, but repositioned in space. When applied to heteromolecular synthons, this enables rearranging more complex hydrogen-bonded motifs and the evolution of binary cocrystals into ternary ones through "latent" carbon-based recognition sites, demonstrating a rational approach to build higher-order solid-state supramolecular assemblies. Synthon ; Crystal engineering ; Supramolecular chemistry ; Hydrogen bond ; Halogen bond ; Amide ; Molecule ; Chemistry ; Halogen ; Crystal structure ; Carbon fibers ; Materials science ; Combinatorial chemistry ; Crystallography ; Stereochemistry ; Organic chemistry ; Alkyl Crystallography and molecular interactions ; X-ray Diffraction in Crystallography ; Building materials and conservation
Authors: Weiren Zhong | Yanchen Wu | Yu-Ting Lin | Shouji Li | Jianyu Zhang | Xu‐Min Cai
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Fabrication of efficient and red-emissive salicylaldehyde Schiff base isomers for multi-scenario information decryption This work proposes a general strategy for constructing efficient and red-emissive salicylaldehyde Schiff bases with both AIE and stimuli-responsive properties for multi-scenario information decryption. Salicylaldehyde ; Schiff base ; Materials science ; Fabrication ; Base (topology) ; Work (physics) ; Combinatorial chemistry ; Polymer chemistry ; Thermodynamics ; Chemistry ; Mathematics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Photochromic and Fluorescence Chemistry
Authors: Luca Vannucci | José Ferreira Neto | Claudia Piccinini | Athanasios Paralikis | Niels Gregersen | Battulga Munkhbat
Source: Physical review. B./Physical review. B (article, 2024-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-photon emitters in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">WSe</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>: Critical role of phonons on excitation schemes and indistinguishability Quantum emitters in few-layer WSe${}_{2}$ hold promise for scalable quantum computation. However, a significant challenge is to prepare the emitter in a specific excited state ``on-demand''. Here, the authors demonstrate that the emitter-phonon coupling has a crucial influence on the state preparation fidelity, because such a coupling is orders of magnitude stronger in WSe${}_{2}$ compared to other platforms. While excitation with resonant laser pulses is found to be significantly damped by phonons, alternative strategies based on detuned pulses allow for fast and reliable state preparation. Excitation ; Phonon ; Photon ; Physics ; Condensed matter physics ; Two-photon excitation microscopy ; Atomic physics ; Quantum mechanics Organic and Molecular Conductors Research ; 2D Materials and Applications ; Molecular Junctions and Nanostructures
Authors: Qiang Liu | Mengyu Du | Hyacinthe Randriamahazaka | Wei Chen
Source: Materials Science and Engineering R Reports (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Graphdiyne-based molecular active materials and devices for emerging smart applications Graphdiyne (GDY), as a novel two-dimensional carbon material, showcases immense potential in the field of smart materials due to its intrinsic properties and microstructure. Unlike conventional smart materials, GDY exhibits stimulus-responsive behaviors without the need for external chemical modifications, dopants, or composite materials. Its unique sp/sp 2 hybridized carbon framework, porous structure, and abundance of highly reactive acetylenic linkages, enable this material to directly interact with environmental stimuli and exhibit superior performance across a variety of applications, including muscle-like actuators, wearable sensors, optoelectronic adaptive regulation, low-grade energy harvesting, and cutting-edge biomedical applications. As a new type of smart material, the application potential of GDY in many frontier fields still needs to be fully explored and exploited. The review provides a timely and comprehensive overview of the state-of-the-art in GDY-based smart materials and applications, emphasizing its unique molecular-scale activity and key challenges in synthesis, scalability, stability, and sensitivity. We believe that this article will provide very valuable insights into technological innovation and collaboration in the field of new material and artificial intelligence. Computer science ; Nanotechnology ; Materials science Graphene research and applications ; 2D Materials and Applications ; Molecular Junctions and Nanostructures
Authors: Isaac Brotons‐Alcázar | Jason S. Terreblanche | Silvia Giménez‐Santamarina | Gerliz M. Gutiérrez-Finol | Karl S. Ryder | Alicia Forment‐Aliaga | Eugenio Coronado
Source: ACS Applied Materials & Interfaces (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements Developing new functionalities of two-dimensional materials (2Dms) can be achieved by their chemical modification with a broad spectrum of molecules. This functionalization is commonly studied by using spectroscopies such as Raman, IR, or XPS, but the detection limit is a common problem. In addition, these methods lack detailed spatial resolution and cannot provide information about the homogeneity of the coating. Atomic force microscopy (AFM), on the other hand, allows the study of 2Dms on the nanoscale with excellent lateral resolution. AFM has been extensively used for topographic analysis; however, it is also a powerful tool for evaluating other properties far beyond topography such as mechanical ones. Therefore, herein, we show how AFM adhesion mapping of transition metal chalcogenide 2Dms (i.e., MnPS3 and MoS2) permits a close inspection of the surface chemical properties. Moreover, the analysis of adhesion as relative values allows a simple and robust strategy to distinguish between bare and functionalized layers and significantly improves the reproducibility between measurements. Remarkably, it is also confirmed by statistical analysis that adhesion values do not depend on the thickness of the layers, proving that they are related only to the most superficial part of the materials. In addition, we have implemented an unsupervised classification method using k-means clustering, an artificial intelligence-based algorithm, to automatically classify samples based on adhesion values. These results demonstrate the potential of simple adhesion AFM measurements to inspect the chemical nature of 2Dms and may have implications for the broad scientific community working in the field. Materials science ; Atomic force microscopy ; Adhesion ; Nanotechnology ; Microscopy ; Kelvin probe force microscope ; Scanning probe microscopy ; Composite material ; Optics 2D Materials and Applications ; Graphene research and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Dihua Wu | Jiangwei Zhu | Yuhong Zheng | Li Fu
Source: Molecules (review, 2024-10-24)
Field: Chemistry — Analytical Chemistry

Electrochemical Sensing Strategies for Synthetic Orange Dyes This review explores electrochemical sensing strategies for synthetic orange dyes, addressing the growing need for sensitive and selective detection methods in various industries. We examine the fundamental principles underlying the electrochemical detection of these compounds, focusing on their redox behavior and interaction with electrode surfaces. The review covers a range of sensor designs, from unmodified electrodes to advanced nanomaterial-based platforms. Chemically modified electrodes incorporating polymers and molecularly imprinted polymers are discussed for their enhanced selectivity. Particular attention is given to nanomaterial-based sensors, including those utilizing carbon nanotubes, graphene derivatives, and metal nanoparticles, which have demonstrated exceptional sensitivity and wide linear ranges. The potential of biological-based approaches, such as DNA interaction sensors and immunosensors, is also evaluated. Current challenges in the field are addressed, including matrix effects in complex samples and long-term stability issues. Emerging trends are highlighted, including the development of multi-modal sensing platforms and the integration of artificial intelligence for data analysis. The review concludes by discussing the commercial potential of these sensors in food safety, environmental monitoring, and smart packaging applications, emphasizing their importance in ensuring the safe use of synthetic orange dyes across industries. Nanotechnology ; Nanomaterials ; Materials science ; Molecularly imprinted polymer ; Biochemical engineering ; Computer science ; Carbon nanotube ; Graphene ; Chemistry ; Selectivity ; Engineering Dye analysis and toxicity ; Biochemical Analysis and Sensing Techniques ; Electrochemical Analysis and Applications
Authors: Zhengyu Chen | Xiangyu Zhao | Jian Wu | Chunming Xu | Linzhou Zhang
Source: Chemical Engineering Journal (article, 2024-03-15)
Field: Chemistry — Analytical Chemistry

Recent progress toward molecular-level kinetic model for complex hydrocarbon conversion processes • A thorough review for modeling molecular-level kinetic models was presented. • Reaction chemistry and reactor model for typical refining processes were summarized. • Kinetic models incorporating molecular characterization and AI should be developed. The simulation and optimization of complex molecular conversion systems are one of the primary challenges in the chemical reaction engineering field. Petroleum refining is a typically complex hydrocarbon conversion system. Building a molecular-level kinetic model for petroleum refining is a crucial basic technology for precisely utilizing petroleum resources. This work briefly describes the typical approach for developing the molecular-level kinetic model. 5 representative frameworks for modeling the molecular-level kinetic model were summarized, containing the single-event approach, bond-electron matrix, structure-oriented lumping, structural unit and bond-electron matrix framework, and molecular type homologous series matrix. The application cases of these frameworks to typical hydrocarbon conversion processes were reviewed. On this basis, this work analyzed the issues and challenges of molecular-level kinetic models for industrial practice. The future direction for the model was also discussed. Moreover, this work believes that building a data-driven model will be the upcoming trend for simulating and optimizing the complex molecular reaction system. Hydrocarbon ; Kinetic energy ; Biochemical engineering ; Chemistry ; Process engineering ; Chemical engineering ; Engineering ; Organic chemistry ; Physics Petroleum Processing and Analysis ; Zeolite Catalysis and Synthesis ; Heat transfer and supercritical fluids
Authors: Rami Al-Jarrah | Faris M. AL‐Oqla
Source: Engineering Computations (article, 2024-05-30)
Field: Materials Science — Polymers and Plastics

Artificial intelligence schemes to predict the mechanical performance of lignocellulosic fibers with unseen data to enhance the reliability of biocomposites Purpose This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Design/methodology/approach Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. A reference dataset contains comprehensive information regarding mechanical behavior of the lignocellulosic fibers was compiled from previous experimental investigations on mechanical properties for eight different fiber materials. Data encompass three key factors: Density of 0.9–1.6 g/cm 3 , Diameter of 5.9–1,000 µm, and Microfibrillar angle of 2–49 deg were utilized. Initially, fuzzy clustering technique was utilized for the data. For validating proposed model, ultimate tensile strength and elongation at break were predicted and then examined against unseen new data that had not been used during model development. Findings The output results demonstrated remarkably accurate and highly acceptable predictions results. The error analysis for the proposed method was discussed by using statistical criteria. The stacked model proved to be effective in significantly reducing level of uncertainty in predicting the mechanical properties, thereby enhancing model’s reliability and precision. The study demonstrates the robustness and efficacy of the stacked method in accurately estimating mechanical properties of lignocellulosic fibers, making it a valuable tool for material scientists and engineers in various applications. Originality/value Cellulosic fibers are essential for biomaterials to enhance developing green sustainable bio-products. However, such fibers have diverse characteristics according to their types, chemical composition and structure causing inconsistent mechanical performance. This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. Robustness (evolution) ; Reliability (semiconductor) ; Ultimate tensile strength ; Fuzzy logic ; Computer science ; Cluster analysis ; Boosting (machine learning) ; Materials science ; Experimental data ; Machine learning ; Data mining ; Process engineering ; Artificial intelligence ; Composite material ; Engineering ; Mathematics ; Statistics Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Tree Root and Stability Studies
Authors: Chuan-Shen Hu | Rishikanta Mayengbam | Min-Chun Wu | Kelin Xia | Tze Chien Sum
Source: Communications Materials (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Geometric data analysis-based machine learning for two-dimensional perovskite design Abstract With extraordinarily high efficiency, low cost, and excellent stability, 2D perovskite has demonstrated a great potential to revolutionize photovoltaics technology. However, inefficient material structure representations have significantly hindered artificial intelligence (AI)-based perovskite design and discovery. Here we propose geometric data analysis (GDA)-based perovskite structure representation and featurization and combine them with learning models for 2D perovskite design. Both geometric properties and periodicity information of the material unit cell, are fully characterized by a series of 1D functions, i.e., density fingerprints (DFs), which are mathematically guaranteed to be invariant under different unit cell representations and stable to structure perturbations. Element-specific DFs, which are based on different site combinations and atom types, are combined with gradient boosting tree (GBT) model. It has been found that our GDA-based learning models can outperform all existing models, as far as we know, on the widely used new materials for solar energetics (NMSE) databank. Computer science ; Perovskite (structure) ; Artificial intelligence ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Machine Learning and ELM ; X-ray Diffraction in Crystallography
Authors: Hossein Hasannezhad | Ali Bakhshi | Masoud Mozafari | Seyed Morteza Naghib
Source: International Journal of Biological Macromolecules (review, 2024-12-29)
Field: Materials Science — Biomaterials

A review of chitosan role in milk bioactive-based drug delivery, smart packaging and biosensors: Recent advances and developments Chitosan ; Drug delivery ; Nanotechnology ; Drug ; Chemistry ; Biotechnology ; Biochemical engineering ; Materials science ; Medicine ; Pharmacology ; Engineering ; Biology ; Biochemistry Nanocomposite Films for Food Packaging ; Advanced Drug Delivery Systems ; Biochemical Analysis and Sensing Techniques
Authors: Wang Luo | F. Chen | Cuiling Zhang | Yuen Yee Cheng | Wenbo Bu | Yanyan Liu
Source: MedMat. (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Reactive oxygen species and neurodegenerative diseases: insights into nanozyme therapeutics Oxidative stress plays a critical role in the onset and progression of neurodegenerative diseases. Traditional methods for regulating oxidative stress using drugs or enzyme molecules often face limitations in efficacy, potential side effects, and the ability to fully meet clinical needs. The emergence of nanozymes offers a novel approach to overcome these challenges and explore therapeutic mechanisms. Focusing on the interaction between reactive oxygen species (ROS) and the nervous system, this article reviews the latest advancements in the use of nanozymes for treating neurodegenerative diseases. First, the mechanism of ROS interaction with neurons and glial cells in the complex nervous network is summarized by analyzing the characteristics of ROS. Second, the application examples and mechanism exploration of different types of ROS-related nanozymes in many neurodegenerative diseases are introduced and summarized. Additionally, the current situation and future prospects of nanozymes combined with advanced technologies such as in vitro detection and artificial intelligence for disease treatment are further discussed. This approach is poised to significantly advance the development of therapies for neurodegenerative diseases. Reactive oxygen species ; Neuroscience ; Disease ; Medicine ; Biology ; Pathology ; Cell biology Advanced Nanomaterials in Catalysis ; Neuroinflammation and Neurodegeneration Mechanisms ; S100 Proteins and Annexins