jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin Guan | Yang Liu | Zhili Wan | Ying‐Lung Steve Tse | To Ngai
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Non-covalent reconfigurable microgel colloidosomes with a well-defined bilayer shell Microgels are extremely interfacially active and are widely used to stabilize emulsions. However, they are commonly used to stabilize oil-in-water emulsions due to their intrinsic hydrophilicity and initially dispersed in water. In addition, there have been no attempts to control microgel structural layers that are formed at the interface and as a result it limits applications of microgel in advanced materials. Here, we show that by introducing octanol into poly(<i>N</i>-isopropylacrylamide-<i>co</i>-methacrylic acid) (PNIPAM-<i>co</i>-MAA) microgels, octanol-swollen microgels can rapidly diffuse from the initially dispersed oil phase onto the water droplet surface. This facilitates the formation of microgel-laden interfacial layers with strong elastic responses and also generates stable inverse water-in-oil Pickering emulsions. These emulsions can be used as templates to produce microgel colloidosomes, herein termed 'microgelsomes', with shells that can be fine-tuned from a particle monolayer to a well-defined bilayer. The microgelsomes can then be used to encapsulate and/or anchor nanoparticles, proteins, vitamin C, bio-based nanocrystals or enzymes. Moreover, the programmed release of these substances can be achieved by using ethanol as a trigger to mediate shell permeability. Thus, these reconfigurable microgelsomes with a microgel-bilayer shell can respond to external stimuli and demonstrate tailored properties, which offers novel insights into microgels and promise wider application of Pickering emulsions stabilized by soft colloids. Bilayer ; Pickering emulsion ; Materials science ; Chemical engineering ; Colloid ; Monolayer ; Nanotechnology ; Nanoparticle ; Membrane ; Chemistry Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Hydrogels: synthesis, properties, applications
Authors: Noelia M. Sanchez‐Ballester | Flavien Sciortino | Sajjad Husain Mir | Gaulthier Rydzek
Source: Molecules (review, 2022-05-19)
Field: Materials Science — Surfaces, Coatings and Films

Weak Polyelectrolytes as Nanoarchitectonic Design Tools for Functional Materials: A Review of Recent Achievements The ionization degree, charge density, and conformation of weak polyelectrolytes can be adjusted through adjusting the pH and ionic strength stimuli. Such polymers thus offer a range of reversible interactions, including electrostatic complexation, H-bonding, and hydrophobic interactions, which position weak polyelectrolytes as key nano-units for the design of dynamic systems with precise structures, compositions, and responses to stimuli. The purpose of this review article is to discuss recent examples of nanoarchitectonic systems and applications that use weak polyelectrolytes as smart components. Surface platforms (electrodeposited films, brushes), multilayers (coatings and capsules), processed polyelectrolyte complexes (gels and membranes), and pharmaceutical vectors from both synthetic or natural-type weak polyelectrolytes are discussed. Finally, the increasing significance of block copolymers with weak polyion blocks is discussed with respect to the design of nanovectors by micellization and film/membrane nanopatterning via phase separation. Polyelectrolyte ; Polymer ; Materials science ; Nanotechnology ; Ionic strength ; Membrane ; Copolymer ; Chemical physics ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material Polymer Surface Interaction Studies ; Advanced Polymer Synthesis and Characterization ; Surfactants and Colloidal Systems
Authors: José C. Páez-Franco | Miriam Rosario Zermeño-Ortega | Carmen Myriam De la O Contreras | Daniel Canseco‐González | Jesús Ricardo Parra‐Unda | Alcives Avila‐Sorrosa | Raúl G. Enríquez | Juan Manuel Germán-Acacio | David Morales‐Morales
Source: Pharmaceutics (review, 2022-02-11)
Field: Chemistry — Organic Chemistry

Relevance of Fluorinated Ligands to the Design of Metallodrugs for Their Potential Use in Cancer Treatment Fluorination of pharmaceutical agents has afforded crucial modifications to their pharmacological profiles, leading to important advances in medicinal chemistry. On the other hand, metallodrugs are considered to be valuable candidates in the treatment of several diseases, albeit with the caveat that they may exhibit pharmacological disadvantages, such as poor water solubility, low bioavailability and short circulating time. To surmount these limitations, two approaches have been developed: one based on the design of novel metallodrug-delivering carriers and the other based on optimizing the structure of the ligands bound to the metal center. In this context, fluorination of the ligands may bring beneficial changes (physicochemical and biological) that can help to elude the aforementioned drawbacks. Thus, in this review, we discuss the use of fluorinated ligands in the design of metallodrugs that may exhibit potential anticancer activity. Chemistry ; Context (archaeology) ; Nanotechnology ; Bioavailability ; Combinatorial chemistry ; Biochemical engineering ; Pharmacology ; Medicine ; Materials science ; Engineering ; Biology Ferrocene Chemistry and Applications ; Metal complexes synthesis and properties ; Synthesis and Biological Evaluation
Authors: Kwanele Kunene | Syreina Sayegh | Matthieu Weber | Myalowenkosi I. Sabela | Damien Voiry | Igor Iatsunskyi | Emerson Coy | Suvardhan Kanchi | Krishna Bisetty | Mikhaël Bechelany
Source: Talanta (article, 2022-10-19)
Field: Chemistry — Analytical Chemistry

Smart electrochemical immunosensing of aflatoxin B1 based on a palladium nanoparticle-boron nitride-coated carbon felt electrode for the wine industry Chemistry ; Dielectric spectroscopy ; Cyclic voltammetry ; Detection limit ; Bovine serum albumin ; Electrode ; Electrochemistry ; Nanoparticle ; Nuclear chemistry ; Chromatography ; Nanotechnology ; Materials science Analytical chemistry methods development ; Advanced biosensing and bioanalysis techniques ; Electrochemical Analysis and Applications
Authors: Shaquib Rahman Ansari | Nele‐Johanna Hempel | Shno Asad | Peter Svedlindh | Christel A. S. Bergström | Korbinian Löbmann | Alexandra Teleki
Source: ACS Applied Materials & Interfaces (article, 2022-04-22)
Field: Materials Science — Biomaterials

Hyperthermia-Induced In Situ Drug Amorphization by Superparamagnetic Nanoparticles in Oral Dosage Forms Superparamagnetic iron oxide nanoparticles (SPIONs) generate heat upon exposure to an alternating magnetic field (AMF), which has been studied for hyperthermia treatment and triggered drug release. This study introduces a novel application of magnetic hyperthermia to induce amorphization of a poorly aqueous soluble drug, celecoxib, in situ in tablets for oral administration. Poor aqueous solubility of many drug candidates is a major hurdle in oral drug development. A novel approach to overcome this challenge is in situ amorphization of crystalline drugs. This method facilitates amorphization by molecular dispersion of the drug in a polymeric network inside a tablet, circumventing the physical instability encountered during the manufacturing and storage of conventional amorphous solid dispersions. However, the current shortcomings of this approach include low drug loading, toxicity of excipients, and drug degradation. Here, doped SPIONs produced by flame spray pyrolysis are compacted with polyvinylpyrrolidone and celecoxib and exposed to an AMF in solid state. A design of experiments approach was used to investigate the effects of SPION composition (Zn<sub>0.5</sub>Fe<sub>2.5</sub>O<sub>4</sub> and Mn<sub>0.5</sub>Fe<sub>2.5</sub>O<sub>4</sub>), doped SPION content (10-20 wt %), drug load (30-50 wt %), and duration of AMF (3-15 min) on the degree of drug amorphization. The degree of amorphization is strongly linked to the maximum tablet temperature achieved during the AMF exposure (<i>r</i> = 0.96), which depends on the SPION composition and content in the tablets. Complete amorphization is achieved with 20 wt % Mn<sub>0.5</sub>Fe<sub>2.5</sub>O<sub>4</sub> and 30 wt % celecoxib in the tablets that reached the maximum temperature of 165.2 °C after 15 min of AMF exposure. Furthermore, manganese ferrite exhibits no toxicity in human intestinal Caco-2 cell lines. The resulting maximum solubility of in situ amorphized celecoxib is 5 times higher than that of crystalline celecoxib in biorelevant intestinal fluid. This demonstrates the promising capability of SPIONs as enabling excipients to magnetically induce amorphization in situ in oral dosage forms. Materials science ; Polyvinylpyrrolidone ; Drug ; Nanoparticle ; Aqueous solution ; Hyperthermia ; Chemical engineering ; Solubility ; Dosage form ; Amorphous solid ; Magnetic hyperthermia ; Drug delivery ; Nanotechnology ; Pharmacology ; Chromatography ; Organic chemistry ; Magnetic nanoparticles ; Chemistry ; Polymer chemistry ; Medicine Nanoparticle-Based Drug Delivery ; Drug Solubulity and Delivery Systems ; Advanced Drug Delivery Systems
Authors: Cheng Hu | Tongyao Wu | Xinyue Huang | Yulong Dong | Jiajun Chen | Zhichun Zhang | Bosai Lyu | Saiqun Ma | Kenji Watanabe | Takashi Taniguchi | Guibai Xie | Xiaojun Li | Qi Liang | Zhiwen Shi
Source: Scientific Reports (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

In-situ twistable bilayer graphene The electrical and optical properties of twisted bilayer graphene (tBLG) depend sensitively on the twist angle. To study the angle dependent properties of the tBLG, currently it is required fabrication of a large number of samples with systematically varied twist angles. Here, we demonstrate the construction of in-situ twistable bilayer graphene, in which the twist angle of the two graphene monolayers can be in-situ tuned continuously in a large range with high precision. The controlled tuning of the twist angle is confirmed by a combination of real-space and spectroscopic characterizations, including atomic force microscopy (AFM) identification of crystal lattice orientation, scanning near-field optical microscopy (SNOM) imaging of superlattice domain walls, and resonant Raman spectroscopy of the largely enhanced G-mode. The developed in-situ twistable homostructure devices enable systematic investigation of the twist angle effects in a single device, thus could largely advance the research of twistronics. Bilayer graphene ; Graphene ; Materials science ; Raman spectroscopy ; Superlattice ; Bilayer ; Microscopy ; Monolayer ; Twist ; Optics ; Nanotechnology ; Optoelectronics ; Membrane ; Chemistry ; Physics Graphene research and applications ; Quantum and electron transport phenomena ; Molecular Junctions and Nanostructures
Authors: Steven W. Hall | Grisell Díaz Leines | Sapna Sarupria | Jutta Rogal
Source: arXiv (Cornell University) (article, 2022-03-21)
Field: Materials Science — Materials Chemistry

Practical guide to replica exchange transition interface sampling and forward flux sampling Path sampling approaches have become invaluable tools to explore the mechanisms and dynamics of so-called rare events that are characterized by transitions between metastable states separated by sizeable free energy barriers. Their practical application, in particular to ever more complex molecular systems, is, however, not entirely trivial. Focusing on replica exchange transition interface sampling (RETIS) and forward flux sampling (FFS), we discuss a range of analysis tools that can be used to assess the quality and convergence of such simulations which is crucial to obtain reliable results. The basic ideas of a step-wise evaluation are exemplified for the study of nucleation in several systems with different complexity, providing a general guide for the critical assessment of RETIS and FFS simulations. Replica ; Sampling (signal processing) ; Computer science ; Umbrella sampling ; Interface (matter) ; Metastability ; Statistical physics ; Convergence (economics) ; Flux (metallurgy) ; Molecular dynamics ; Range (aeronautics) ; Physics ; Aerospace engineering ; Chemistry ; Computational chemistry ; Engineering ; Detector ; Telecommunications Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Artem M. Rumyantsev | A. Johner | Matthew Tirrell | Juan Pablo
Source: Macromolecules (article, 2022-07-06)
Field: Chemistry — Physical and Theoretical Chemistry

Unifying Weak and Strong Charge Correlations within the Random Phase Approximation: Polyampholytes of Various Sequences International audience Random phase approximation ; Charge (physics) ; Coulomb ; Coacervate ; Statistical physics ; Scaling ; Charge density ; Random coil ; Phase (matter) ; Physics ; Chemistry ; Quantum mechanics ; Mathematics ; Nuclear magnetic resonance Electrostatics and Colloid Interactions ; Material Dynamics and Properties ; Proteins in Food Systems
Authors: Tianyou Mou | Xue Han | Huiyuan Zhu | Hongliang Xin
Source: Current Opinion in Chemical Engineering (article, 2022-05-02)
Field: Materials Science — Materials Chemistry

Machine learning of lateral adsorbate interactions in surface reaction kinetics Parameterized complexity ; Surface (topology) ; Cluster (spacecraft) ; Field (mathematics) ; Computer science ; Space (punctuation) ; Chemical physics ; Kinetics ; Statistical physics ; Nanotechnology ; Materials science ; Biological system ; Chemistry ; Physics ; Algorithm ; Mathematics ; Classical mechanics ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Omid Aghababaei Tafreshi | Zia Saadatnia | Shahriar Ghaffari‐Mosanenzadeh | Sogand Okhovatian | Chul B. Park | Hani E. Naguib
Source: SPE Polymers (article, 2022-12-08)
Field: Materials Science — Materials Chemistry

Machine learning‐based model for predicting the material properties of nanostructured aerogels Abstract Data‐driven modeling in material science rose to prominence in the last decade, and various supervised and unsupervised machine learning techniques have been employed for material development and deriving insights for decision‐making purposes. In this context, machine learning can have prominent importance in the field of nanostructured aerogels for accelerated materials design and material properties prediction. Current attempts rely only on experimental approach, which have inherent shortcomings, including inefficiency due to the prolonged synthesis process, and necessity of analyzing microstructure and properties. In order to address the challenges associated with the traditional experimental approach, in this study, an artificial neural network (ANN) is employed to predict the material properties of nanostructured aerogels. Polyimide (PI) organic aerogels are selected for this purpose. Through understanding the contributing material and processing factors in PI aerogel synthesis, a dataset is prepared. Data preprocessing is performed, and through hyperparameter tuning, ANN is constructed and trained for a given dataset. Various material properties are predicted, including compressive modulus, density, and porosity. Results show that ANN is trained with high accuracy, which demonstrates the versatility and accuracy of model in materials properties prediction. This study can therefore pave the way for establishing a platform for data‐driven materials innovation. Context (archaeology) ; Artificial neural network ; Machine learning ; Artificial intelligence ; Aerogel ; Computer science ; Materials science ; Material properties ; Process (computing) ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Aerogels and thermal insulation ; Advanced Photocatalysis Techniques
Authors: Debabrata Palai | Hiroyuki Tahara | Shunta Chikami | Glenn Villena Latag | Shoichi Maeda | Chisato Komura | Hideharu Kurioka | T. Hayashi
Source: ACS Biomaterials Science & Engineering (article, 2022-07-29)
Field: Materials Science — Surfaces, Coatings and Films

Prediction of Serum Adsorption onto Polymer Brush Films by Machine Learning Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films' physicochemical information and the monomers' chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film's physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films. Brush ; Monomer ; Adsorption ; Polymer ; Materials science ; Polymer brush ; Protein adsorption ; Ranking (information retrieval) ; Molecule ; Chemical engineering ; Nanotechnology ; Computer science ; Chemistry ; Machine learning ; Composite material ; Organic chemistry Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Antimicrobial agents and applications
Authors: Yong-Chen Pei | Baohua Wang | Ji-Tuo Wu | Chenyang Wang | Jing-Han Guan | Huiqi Lu
Source: IEEE Transactions on Industrial Electronics (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

A Machine Learning Empowered Shape Memory Alloy Gripper With Displacement-Force-Stiffness Self-Sensing This article proposes a multiple self-sensing gripper (MssGripper) driven by the shape memory alloy (SMA) and empowered by machine learning algorithms. The MssGripper can identify objects without external sensors. A single SMA wire can drive the gripper for self-sensing accurately. This article confirms the resistance of the SMA can reflect the phase transition and can be used for displacement and force prediction, as well as for object stiffness prediction. Through machine learning, a backpropagation neural network and long-short-term-memory (LSTM) are used to establish multiple self-sensing models for prediction. The robustness experimental results show that the self-sensing models based on LSTM have higher prediction accuracy. The average root-mean-square errors of displacement prediction and force prediction are 0.063 mm and 0.236 N, respectively, and the stiffness prediction error is less than 9.4%. Moreover, the accuracy of the classifier in stiffness identification is 97.2%. The MssGripper can accurately predict the displacement, force, and stiffness and identify objects such as springs, rubber bars and steel bars. The establishment of the models expands the novel idea of gripper sensing, which is beneficial to promoting miniaturization and compactness. Eccentricity (behavior) ; SIGNAL (programming language) ; Computer science ; Accuracy and precision ; Displacement (psychology) ; Control theory (sociology) ; Stiffness ; Signal processing ; Algorithm ; Engineering ; Mechanical engineering ; Electronic engineering ; Artificial intelligence ; Structural engineering ; Mathematics ; Digital signal processing Shape Memory Alloy Transformations ; Soft Robotics and Applications ; Structural Health Monitoring Techniques
Authors: Lauri Himanen
Source:  (preprint, 2022-09-13)
Field: Materials Science — Materials Chemistry

DScribe: Library of descriptors for machine learning in materials science This talk introduces DScribe which is a Python package for transforming atomic structures into fixed-size numerical fingerprints. These fingerprints are often called "descriptors" and they can be used in various tasks, including machine learning, visualization, similarity analysis, etc. DScribe provides user-friendly, off-the-shelf descriptor implementations and is freely available under the open-source Apache License 2.0. This seminar will include several hands-on examples on how these descriptors can be used for different tasks, including property prediction through a regression model, data clustering through unsupervised learning, and visualization of complex chemical environments. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Smooth Overlap of Atomic Positions (SOAP), Many-body Tensor Representation (MBTR), Local Many-body Tensor Representation (LMBTR), Atom-centered Symmetry Function (ACSF) and the Valle-Oganov descriptor. Python (programming language) ; Computer science ; Visualization ; Cluster analysis ; Implementation ; Artificial intelligence ; Matrix (chemical analysis) ; Tensor (intrinsic definition) ; Representation (politics) ; Computational science ; Theoretical computer science ; Machine learning ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ivan M. Novitskiy | Andrei G. Kutateladze
Source: The Journal of Organic Chemistry (article, 2022-06-18)
Field: Chemistry — Spectroscopy

Peculiar Reaction Products and Mechanisms Revisited with Machine Learning-Augmented Computational NMR DU8ML, a fast and accurate machine learning-augmented density functional theory (DFT) method for computing nuclear magnetic resonance (NMR) spectra, proved effective for high-throughput revision of misassigned natural products. In this paper, we disclose another important aspect of its application: correction of unusual reaction mechanisms originally proposed because of incorrect product structures. Chemistry ; Density functional theory ; Natural product ; Product (mathematics) ; Throughput ; Computational chemistry ; Nuclear magnetic resonance spectroscopy ; Organic chemistry ; Computer science Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Axial and Atropisomeric Chirality Synthesis
Authors: Vipul Mann | Karoline Dantas Brito | Rafiqul Gani | Venkat Venkatasubramanian
Source: Fluid Phase Equilibria (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Hybrid, Interpretable Machine Learning for Thermodynamic Property Estimation using Grammar2vec for Molecular Representation Interpretability ; Representation (politics) ; Property (philosophy) ; Feature (linguistics) ; Benchmark (surveying) ; Artificial intelligence ; Grammar ; Computer science ; Point (geometry) ; Theoretical computer science ; String (physics) ; Boiling point ; Chemistry ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics