jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daisy R. S. Pooler | Daniel Doellerer | Stefano Crespi | Ben L. Feringa
Source: Organic Chemistry Frontiers (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Controlling rotary motion of molecular motors based on oxindole Molecular motors are essential components of artificial molecular machines, which can be used to manipulate and amplify mechanical motion at the nanoscale to create machine-like function. Since the discovery of light-driven rotary molecular motors, the field has been widely developed, including the introduction of molecular motors based on oxindole by our group in 2019. The rotational properties of molecular motors, <i>e.g.</i> absorption wavelength, quantum yield and rotation speed, often critically depend on substituent effects. Up to now, the substituent effects of oxindole-based molecular motors have not yet been investigated. Herein, we present a family of oxindole-based molecular motors functionalised at three different positions on the motor core, with either CN or OMe groups. The motors prepared in this work retain the favourable features of oxindole-based motors, <i>i.e.</i> simple synthesis and visible light addressability. We find that functionalisation has substantial effects on the absorption wavelength of the motors, meanwhile the rotation speed is unaffected. Furthermore, we found that functionalisation of the oxindole molecular motors increases their quantum efficiency considerably in comparison to previous motors of their class. Oxindole ; Molecular motor ; Chemistry ; Rotation around a fixed axis ; Molecular machine ; Rotation (mathematics) ; Quantum ; Nanotechnology ; Biological system ; Physics ; Computer science ; Materials science ; Classical mechanics ; Quantum mechanics ; Organic chemistry ; Artificial intelligence Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials ; Photoreceptor and optogenetics research
Authors: S. Swathilakshmi | S. Anandhan
Source: Sensors & Diagnostics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Recent development in carbon dot-based gas sensors The excellent gas sensing ability of carbon dot–ceramic nanoparticle composites is depicted in the above figure in which formaldehyde and isopropanol vapors are sensed by a graphene quantum dot–TiO 2 nanocomposite. Quantum dot ; Nanocomposite ; Materials science ; Graphene ; Carbon fibers ; Carbon quantum dots ; Nanotechnology ; Carbon Nanoparticles ; Ceramic ; Nanoparticle ; Formaldehyde ; Graphene quantum dot ; Composite material ; Chemistry ; Composite number ; Organic chemistry Carbon and Quantum Dots Applications ; Gas Sensing Nanomaterials and Sensors ; Electrochemical sensors and biosensors
Authors: Thomas L. Moore | Alexander Cook | Elena Bellotti | Roberto Palomba | Purnima Naresh Manghnani | Raffaele Spanò | Sayanti Brahmachari | Martina Di Francesco | Anna Lisa Palange | Daniele Di Mascolo | Paolo Decuzzi
Source: Drug Delivery and Translational Research (review, 2022-03-13)
Field: Materials Science — Biomaterials

Shape-specific microfabricated particles for biomedical applications: a review The storied history of controlled the release systems has evolved over time; from degradable drug-loaded sutures to monolithic zero-ordered release devices and nano-sized drug delivery formulations. Scientists have tuned the physico-chemical properties of these drug carriers to optimize their performance in biomedical/pharmaceutical applications. In particular, particle drug delivery systems at the micron size regime have been used since the 1980s. Recent advances in micro and nanofabrication techniques have enabled precise control of particle size and geometry-here we review the utility of microplates and discoidal polymeric particles for a range of pharmaceutical applications. Microplates are defined as micrometer scale polymeric local depot devices in cuboid form, while discoidal polymeric nanoconstructs are disk-shaped polymeric particles having a cross-sectional diameter in the micrometer range and a thickness in the hundreds of nanometer range. These versatile particles can be used to treat several pathologies such as cancer, inflammatory diseases and vascular diseases, by leveraging their size, shape, physical properties (e.g., stiffness), and component materials, to tune their functionality. This review highlights design and fabrication strategies for these particles, discusses their applications, and elaborates on emerging trends for their use in formulations. Nanotechnology ; Drug delivery ; Micrometer ; Materials science ; Nanometre ; Particle (ecology) ; Microsphere ; Nanolithography ; Particle size ; Fabrication ; Nanoscopic scale ; Chemistry ; Mechanical engineering ; Composite material ; Chemical engineering Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research ; Advanced Drug Delivery Systems
Authors: Erenildo Ferreira de Macedo | Nivia Salles Santos | Lucca Silva Nascimento | Raphaël Mathey | Sophie Brenet | Matheus Sacilotto de Moura | Yanxia Hou | Dayane Batista Tada
Source: International Journal of Molecular Sciences (article, 2022-12-29)
Field: Materials Science — Biomaterials

Interaction between Nanoparticles, Membranes and Proteins: A Surface Plasmon Resonance Study Regardless of the promising use of nanoparticles (NPs) in biomedical applications, several toxic effects have increased the concerns about the safety of these nanomaterials. Although the pathways for NPs toxicity are diverse and dependent upon many parameters such as the nature of the nanoparticle and the biochemical environment, numerous studies have provided evidence that direct contact between NPs and biomolecules or cell membranes leads to cell inactivation or damage and may be a primary mechanism for cytotoxicity. In such a context, this work focused on developing a fast and accurate method to characterize the interaction between NPs, proteins and lipidic membranes by surface plasmon resonance imaging (SPRi) technique. The interaction of gold NPs with mimetic membranes was evaluated by monitoring the variation of reflectivity after several consecutive gold NPs injections on the lipidic membranes prepared on the SPRi biochip. The interaction on the membranes with varied lipidic composition was compared regarding the total surface concentration density of gold NPs adsorbed on them. Then, the interaction of gold and silver NPs with blood proteins was analyzed regarding their kinetic profile of the association/dissociation and dissociation constants (k<sub>off</sub>). The surface concentration density on the membrane composed of 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine and cholesterol (POPC/cholesterol) was 2.5 times higher than the value found after the injections of gold NPs on POPC only or with dimethyldioctadecylammonium (POPC/DDAB). Regarding the proteins, gold NPs showed preferential binding to fibrinogen resulting in a value of the variation of reflectivity that was 8 times higher than the value found for the other proteins. Differently, silver NPs showed similar interaction on all the tested proteins but with a variation of reflectivity on immunoglobulin G (IgG) 2 times higher than the value found for the other tested proteins. POPC ; Surface plasmon resonance ; Membrane ; Colloidal gold ; Chemistry ; Nanoparticle ; Biophysics ; Lipid bilayer ; Nanomaterials ; Nanotechnology ; Biochemistry ; Materials science Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Frederik Müggenburg | Sabine Müller
Source: The Chemical Record (review, 2022-02-21)
Field: Chemistry — Organic Chemistry

Azide‐Modified Nucleosides as Versatile Tools for Bioorthogonal Labeling and Functionalization Azide-modified nucleosides are important building blocks for RNA and DNA functionalization by click chemistry based on azide-alkyne cycloaddition. This has put demand on synthetic chemistry to develop approaches for the preparation of azide-modified nucleoside derivatives. We review here the available methods for the synthesis of various nucleosides decorated with azido groups at the sugar residue or nucleobase, their incorporation into oligonucleotides and cellular RNAs, and their application in azide-alkyne cycloadditions for labelling and functionalization. Bioorthogonal chemistry ; Azide ; Surface modification ; Click chemistry ; Chemistry ; Combinatorial chemistry ; Nucleobase ; Cycloaddition ; Alkyne ; Oligonucleotide ; Nucleoside ; Bioconjugation ; Organic chemistry ; DNA ; Biochemistry ; Catalysis Click Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Vageesha W. Liyana Gunawardana | Tyler J. Finnegan | Carson E. Ward | Curtis E. Moore | Jovica D. Badjić
Source: Angewandte Chemie International Edition (article, 2022-06-20)
Field: Chemistry — Organic Chemistry

Dissipative Formation of Covalent Basket Cages Living systems use chemical fuels to transiently assemble functional structures. As a step toward constructing abiotic mimics of such structures, we herein describe dissipative formation of covalent basket cage CBC 5 by reversible imine condensation of cup-shaped aldehyde 2 (i.e., basket) with trivalent aromatic amine 4. This nanosized [4+4] cage (V=5 nm<sup>3</sup> , M<sub>w</sub> =6150 Da) has shape of a truncated tetrahedron with four baskets at its vertices and four aromatic amines forming the faces. Importantly, tris-aldehyde basket 2 and aliphatic tris-amine 7 undergo condensation to give small [1+1] cage 6. The imine metathesis of 6 and aromatic tris-amine 4 into CBC 5 was optimized to bias the equilibrium favouring 6. Addition of tribromoacetic acid (TBA) as a chemical fuel perturbs this equilibrium to result in the transient formation of CBC 5, with subsequent consumption of TBA via decarboxylation driving the system back to the starting state. Imine ; Dynamic covalent chemistry ; Chemistry ; Tris ; Covalent bond ; Amine gas treating ; Aldehyde ; Cage ; Condensation ; Condensation reaction ; Polymer chemistry ; Photochemistry ; Organic chemistry ; Supramolecular chemistry ; Molecule ; Catalysis Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis
Authors: Monika Kuczyńska | Patrycja Jakubek | Agnieszka Bartoszek
Source: Antioxidants (review, 2022-12-04)
Field: Chemistry — Organic Chemistry

More than Just Antioxidants: Redox-Active Components and Mechanisms Shaping Redox Signalling Network The concept of oxidative stress as a condition underlying a multitude of human diseases has led to immense interest in the search for antioxidant-based remedies. The simple and intuitive story of "the bad" reactive oxygen species (ROS) and "the good" antioxidants quickly (and unsurprisingly) lead to the commercial success of products tagged "beneficial to health" based solely on the presence of antioxidants. The commercial success of antioxidants by far preceded the research aimed at understanding the exact redox-related mechanisms that are in control of shaping the states of health and disease. This review describes the redox network formed by the interplay of ROS with cellular molecules and the resulting regulation of processes at the genomic and proteomic levels. Key players of this network are presented, both involved in redox signalling and control of cellular metabolism linked to most, if not all, physiological processes. In particular, this review focuses on the concept of reductive stress, which still remains less well-established compared to oxidative stress. Redox ; Chemistry ; Oxidation reduction ; Biochemistry ; Inorganic chemistry Free Radicals and Antioxidants ; Vitamin C and Antioxidants Research ; Antioxidant Activity and Oxidative Stress
Authors: Simon Yves | Matheus I. N. Rosa | Yuning Guo | Mohit Gupta | Massimo Ruzzene | Andrea Alù
Source: Advanced Science (article, 2022-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Moiré‐Driven Topological Transitions and Extreme Anisotropy in Elastic Metasurfaces The twist angle between a pair of stacked 2D materials has been recently shown to control remarkable phenomena, including the emergence of flat-band superconductivity in twisted graphene bilayers, of higher-order topological phases in twisted moiré superlattices, and of topological polaritons in twisted hyperbolic metasurfaces. These discoveries, at the foundations of the emergent field of twistronics, have so far been mostly limited to explorations in atomically thin condensed matter and photonic systems, with limitations on the degree of control over geometry and twist angle, and inherent challenges in the fabrication of carefully engineered stacked multilayers. Here, this work extends twistronics to widely reconfigurable macroscopic elastic metasurfaces consisting of LEGO pillar resonators. This work demonstrates highly tailored anisotropy over a single-layer metasurface driven by variations in the twist angle between a pair of interleaved spatially modulated pillar lattices. The resulting quasi-periodic moiré patterns support topological transitions in the isofrequency contours, leading to strong tunability of highly directional waves. The findings illustrate how the rich phenomena enabled by twistronics and moiré physics can be translated over a single-layer metasurface platform, introducing a practical route toward the observation of extreme phenomena in a variety of wave systems, potentially applicable to both quantum and classical settings without multilayered fabrication requirements. Metamaterial ; Superlattice ; Photonics ; Anisotropy ; Materials science ; Topology (electrical circuits) ; Fabrication ; Condensed matter physics ; Physics ; Optoelectronics ; Optics Metamaterials and Metasurfaces Applications ; Topological Materials and Phenomena ; Plasmonic and Surface Plasmon Research
Authors: Manik Kumer Ghosh | Sarah N. Elliott | Kieran P. Somers | Stephen J. Klippenstein | Henry J. Curran
Source: Combustion and Flame (article, 2022-11-20)
Field: Chemistry — Organic Chemistry

Group additivity values for the heat of formation of C2–C8 alkanes, alkyl hydroperoxides, and their radicals Chemistry ; Radical ; Alkyl ; Ab initio ; Standard enthalpy of formation ; Additive function ; Thermochemistry ; Ab initio quantum chemistry methods ; Computational chemistry ; Alkane ; Combustion ; Thermodynamics ; Physical chemistry ; Molecule ; Hydrocarbon ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Free Radicals and Antioxidants ; Advanced Chemical Physics Studies
Authors: Robert Tempke | Terence Musho
Source: Communications Chemistry (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

Autonomous design of new chemical reactions using a variational autoencoder Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set. Autoencoder ; Chemical space ; Computer science ; Space (punctuation) ; Artificial intelligence ; Training set ; Set (abstract data type) ; Sampling (signal processing) ; Machine learning ; Artificial neural network ; Chemistry ; Drug discovery ; Computer vision Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kou Li | Yuya Kinoshita | Daiki Sakai | Yukio Kawano
Source: Micromachines (review, 2022-12-26)
Field: Materials Science — Materials Chemistry

Recent Progress in Development of Carbon-Nanotube-Based Photo-Thermoelectric Sensors and Their Applications in Ubiquitous Non-Destructive Inspections The photo-thermoelectric (PTE) effect in electronic materials effectively combines photo-absorption-induced local heating and associated thermoelectric conversion for uncooled and broadband photo-detection. In particular, this work comprehensively summarizes the operating mechanism of carbon nanotube (CNT)-film-based PTE sensors and ubiquitous non-destructive inspections realized by exploiting the material properties of CNT films. Formation of heterogeneous material junctions across the CNT-film-based PTE sensors, namely photo-detection interfaces, triggers the Seebeck effect with photo-absorption-induced local heating. Typical photo-detection interfaces include a channel-electrode boundary and a junction between P-type CNTs and N-type CNTs (PN junctions). While the original CNT film channel exhibits positive Seebeck coefficient values, the material selections of the counterpart freely govern the intensity and polarity of the PTE response signals. Based on these operating mechanisms, CNT film PTE sensors demonstrate a variety of physical and chemical non-destructive inspections. The device aggregates broad multi-spectral optical information regarding the targets and reconstructs their inner composite or layered structures. Arbitrary deformations of the device are attributed to the macroscopic flexibility of the CNT films to further monitor targets from omni-directional viewing angles without blind spots. Detection of blackbody radiation from targets using the device also visualizes their behaviors and associated changes. Carbon nanotube ; Thermoelectric effect ; Materials science ; Optoelectronics ; Electrode ; Nanotechnology ; Absorption (acoustics) ; Broadband ; Thermoelectric materials ; Optics ; Composite material ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Enguang Zuo | Lei Sun | Junyi Yan | Cheng Chen | Chen Chen | Xiaoyi Lv
Source: Scientific Reports (article, 2022-08-10)
Field: Chemistry — Analytical Chemistry

Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine Fennel contains many antioxidant and antibacterial substances, and it has very important applications in food flavoring and other fields. The kinds and contents of chemical substances in fennel vary from region to region, which can affect the taste and efficacy of the fennel and its derivatives. Therefore, it is of great significance to accurately classify the origin of the fennel. Recently, origin detection methods based on deep networks have shown promising results. However, the existing methods spend a relatively large time cost, a drawback that is fatal for large amounts of data in practical application scenarios. To overcome this limitation, we explore an origin detection method that guarantees faster detection with classification accuracy. This research is the first to use the machine learning algorithm combined with the Fourier transform-near infrared (FT-NIR) spectroscopy to realize the classification and identification of the origin of the fennel. In this experiment, we used Rubberband baseline correction on the FT-NIR spectral data of fennel (Yumen, Gansu and Turpan, Xinjiang), using principal component analysis (PCA) for data dimensionality reduction, and selecting extreme learning machine (ELM), Convolutional Neural Network (CNN), recurrent neural network (RNN), Transformer, generative adversarial networks (GAN) and back propagation neural network (BPNN) classification model of the company realizes the classification of the sample origin. The experimental results show that the classification accuracy of ELM, RNN, Transformer, GAN and BPNN models are above 96%, and the ELM model using the hardlim as the activation function has the best classification effect, with an average accuracy of 100% and a fast classification speed. The average time of 30 experiments is 0.05 s. This research shows the potential of the machine learning algorithm combined with the FT-NIR spectra in the field of food production area classification, and provides an effective means for realizing rapid detection of the food production area, so as to merchants from selling shoddy products as good ones and seeking illegal profits. Extreme learning machine ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Convolutional neural network ; Artificial neural network ; Principal component analysis ; Dimensionality reduction ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Diego Segura Ibarra | Jacob Mathews | Fan Li | Hongfang Lü | Guoqiang Li | Jinyuan Chen
Source: Polymer (article, 2022-10-10)
Field: Materials Science — Polymers and Plastics

Deep learning for predicting the thermomechanical behavior of shape memory polymers Shape-memory polymer ; Artificial neural network ; Computer science ; Work (physics) ; Convolutional neural network ; Shape-memory alloy ; Thermomechanical analysis ; Artificial intelligence ; Constitutive equation ; Materials science ; Mechanical engineering ; Structural engineering ; Composite material ; Finite element method ; Engineering Polymer composites and self-healing ; Fuel Cells and Related Materials ; Shape Memory Alloy Transformations
Authors: Wenwen Li | Pu Chen | Bo Xiong | Guandong Liu | Shuliang Dou | Yaohui Zhan | Zhiyuan Zhu | Tao Chu | Yao Li | Wei Ma
Source: Journal of Physics Materials (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

Deep learning modeling strategy for material science: from natural materials to metamaterials Abstract Computational modeling is a crucial approach in material-related research for discovering new materials with superior properties. However, the high design flexibility in materials, especially in the realm of metamaterials where the sub-wavelength structure provides an additional degree of freedom in design, poses a formidable computational cost in various real-world applications. With the advent of big data, deep learning (DL) brings revolutionary breakthroughs in many conventional machine learning and pattern recognition tasks such as image classification. The accompanied data-driven modeling paradigm also provides transformative methodology shift in materials science, from trial-and-error routine to intelligent material discovery and analysis. This review systematically summarize the application of DL in material science, based on a model selection perspective for both natural materials and metamaterials. The review aims to uncover the logic behind data-model relation with emphasis on suitable data structures for different scenarios in the material study and the corresponding problem-solving DL model architectures. Computer science ; Metamaterial ; Deep learning ; Transformative learning ; Artificial intelligence ; Flexibility (engineering) ; Big data ; Data science ; Relation (database) ; Realm ; Computational model ; Data mining ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques