jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simona Zuppolini | Ahmed Salama | Iriczalli Cruz‐Maya | Vincenzo Guarino | Anna Borriello
Source: Pharmaceutics (review, 2022-02-10)
Field: Materials Science — Biomaterials

Cellulose Amphiphilic Materials: Chemistry, Process and Applications In the last decade, amphiphilic cellulose (AC) is emerging as attractive biomaterial for different therapeutic use, due to its unique chemical and physical properties. Using it as alternative to synthetic polymers, AC opens up new avenues to prepare new bio-sustainable materials with low impact in the cellular environment. Herein, most recent methods to synthesize and processing AC materials from different sources-i.e., cellulose nanofibers, bacterial cellulose, cellulose derivatives-will be discussed. By an accurate optimization of morphology and surface chemistry, it is possible to develop innovative amphiphilic platforms, promising for a wide range of biomedical applications, from drug delivery to molecular/particle adsorption. Amphiphile ; Cellulose ; Nanofiber ; Nanotechnology ; Biomaterial ; Polymer ; Bacterial cellulose ; Chemistry ; Drug delivery ; Polymer science ; Materials science ; Organic chemistry ; Copolymer Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications
Authors: Linhu Li | Renting Jiang | Beibei Shan | Yaxuan Lu | Chao Zheng | Ming Li
Source: Nature Communications (article, 2022-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Near-infrared II plasmonic porous cubic nanoshells for in vivo noninvasive SERS visualization of sub-millimeter microtumors In vivo surface-enhanced Raman scattering (SERS) imaging allows non-invasive visualization of tumors for intraoperative guidance and clinical diagnostics. However, the in vivo utility of SERS is greatly hampered by the strong optical scattering and autofluorescence background of biological tissues and the lack of highly active plasmonic nanostructures. Herein, we report a class of porous nanostructures comprising a cubic AuAg alloy nanoshell and numerous nanopores. Such porous nanostructures exhibit excellent near-infrared II plasmonic properties tunable in a broad spectral range by varying the pore features while maintaining a small dimension. We demonstrate their exceptional near-infrared II SERS performance varying with the porous properties. Additionally, near-infrared II SERS probes created with porous cubic AuAg nanoshells are demonstrated with remarkable capability for in vivo visualization of sub-millimeter microtumors in a living mouse model. Our near-infrared II SERS probes hold great potentials for precise demarcation of tumor margins and identification of microscopic tumors. Plasmon ; Nanoshell ; Materials science ; Millimeter ; Infrared ; Nanotechnology ; Visualization ; Optoelectronics ; Optics ; Computer science ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Ran An | Yuan Liang | Ruiping Deng | Pengpeng Lei | Hongjie Zhang
Source: Light Science & Applications (article, 2022-07-11)
Field: Materials Science — Materials Chemistry

Hollow nanoparticles synthesized via Ostwald ripening and their upconversion luminescence-mediated Boltzmann thermometry over a wide temperature range Upconversion nanoparticles (UCNPs) with hollow structures exhibit many fascinating optical properties due to their special morphology. However, there are few reports on the exploration of hollow UCNPs and their optical applications, mainly because of the difficulty in constructing hollow structures by conventional methods. Here, we report a one-step template-free method to synthesize NaBiF<sub>4</sub>:Yb,Er (NBFYE) hollow UCNPs via Ostwald ripening under solvothermal conditions. Moreover, we also elucidate the possible formation mechanism of hollow nanoparticles (HNPs) by studying the growth process of nanoparticles in detail. By changing the contents of polyacrylic acid and H<sub>2</sub>O in the reaction system, the central cavity size of NBFYE nanoparticles can be adjusted. Benefiting from the structural characteristics of large internal surface area and high surface permeability, NBFYE HNPs exhibit excellent luminescence properties under 980 nm near-infrared irradiation. Importantly, NBFYE hollow UCNPs can act as self-referenced ratiometric luminescent thermometers under 980 nm laser irradiation, which are effective over a wide temperature range from 223 K to 548 K and have a maximum sensitivity value of 0.0065 K<sup>-1</sup> at 514 K. Our work clearly demonstrates a novel method for synthesizing HNPs and develops their applications, which provides a new idea for constructing hollow structure UCNPs and will also encourage researchers to further explore the optical applications of hollow UCNPs. Ostwald ripening ; Materials science ; Luminescence ; Nanoparticle ; Nanotechnology ; Chemical engineering ; Optoelectronics Luminescence Properties of Advanced Materials ; Optical properties and cooling technologies in crystalline materials ; Perovskite Materials and Applications
Authors: Maha Mostafa | Oday Saleh | A.M.A. Henaish | Samir Ali Abd El-Kaream | R. Ghazy | O. M. Hemeda | A.M. Dorgham | Hanan Al–Ghamdi | Aljawhara H. Almuqrin | M.I. Sayyed | Sergei V. Trukhanov | E.L. Trukhanova | А.В. Труханов | Di Zhou | Moustafa A. Darwish
Source: Nanomaterials (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

Structure, Morphology and Electrical/Magnetic Properties of Ni-Mg Nano-Ferrites from a New Perspective Using the auto combustion flash method, Ni1-x+2Mgx+2Fe2+3O4 (x = 0, 0.2, 0.6, 0.8 and 1) nano-ferrites were synthesized. All samples were thermally treated at 973 K for 3 h. The structural analysis for the synthesized samples was performed using XRD, high-resolution transmission electron microscopy (HRTEM), and FTIR. Scanning electron microscopy (SEM) was undertaken to explore the surface morphology of all the samples. The thermal stability of these samples was investigated using thermogravimetric analysis (TGA). XRD data show the presence of a single spinel phase for all the prepared samples. The intensity of the principal peak of the spinel phase decreases as Mg content increases, showing that Mg delays crystallinity. The Mg content raised the average grain size (<i>D</i>) from 0.084 μm to 0.1365 μm. TGA shows two stages of weight loss variation. The vibrating sample magnetometer (VSM) measurement shows that magnetic parameters, such as initial permeability (<i>μ<sub>i</sub></i>) and saturation magnetization (<i>M<sub>s</sub></i>), decay with rising Mg content. The permeability and magnetic anisotropy at different frequencies and temperatures were studied to show the samples' magnetic behavior and determine the Curie temperature (<i>T<sub>C</sub></i>), which depends on the internal structure. The electrical resistivity behavior shows the semi-conductivity trend of the samples. Finally, the dielectric constant increases sharply at high temperatures, explained by the increased mobility of charge carriers, and decreases with increasing frequency. Materials science ; Analytical Chemistry (journal) ; High-resolution transmission electron microscopy ; Spinel ; Crystallinity ; Dielectric ; Electrical resistivity and conductivity ; Grain size ; Scanning electron microscope ; Thermogravimetric analysis ; Dielectric loss ; Transmission electron microscopy ; Nuclear magnetic resonance ; Mineralogy ; Composite material ; Nanotechnology ; Chemical engineering ; Chemistry ; Metallurgy Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Advancements in Battery Materials
Authors: Travis A. Meyer | César E. Ramírez | Matthew Tamasi | Adam J. Gormley
Source: ACS Polymers Au (review, 2022-11-17)
Field: Materials Science — Materials Chemistry

A User’s Guide to Machine Learning for Polymeric Biomaterials The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult <i>a priori</i> rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab. Curse of dimensionality ; Process (computing) ; Computer science ; Nanotechnology ; Lead (geology) ; Human–computer interaction ; Systems engineering ; Engineering ; Materials science ; Artificial intelligence ; Biology Machine Learning in Materials Science ; Cell Image Analysis Techniques ; 3D Printing in Biomedical Research
Authors: Nicholas Marcella | Jin Soo Lim | Anna M. Płonka | George Yan | Cameron J. Owen | Jessi E. S. van der Hoeven | Alexandre C. Foucher | Hio Tong Ngan | Steven B. Torrisi | Nebojša Marinković | Eric A. Stach | Jason F. Weaver | Joanna Aizenberg | Philippe Sautet | Boris Kozinsky | Anatoly I. Frenkel
Source: Nature Communications (article, 2022-02-11)
Field: Materials Science — Materials Chemistry

Decoding reactive structures in dilute alloy catalysts Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics require atomic level knowledge of the active sites. These structures often change dynamically during reactions and are difficult to decipher. A prototypical example is the hydrogen-deuterium exchange reaction catalyzed by dilute Pd-in-Au alloy nanoparticles. From a combination of catalytic activity measurements, machine learning-enabled spectroscopic analysis, and first-principles based kinetic modeling, we demonstrate that the active species are surface Pd ensembles containing only a few (from 1 to 3) Pd atoms. These species simultaneously explain the observed X-ray spectra and equate the experimental and theoretical values of the apparent activation energy. Remarkably, we find that the catalytic activity can be tuned on demand by controlling the size of the Pd ensembles through catalyst pretreatment. Our data-driven multimodal approach enables decoding of reactive structures in complex and dynamic alloy catalysts. Catalysis ; Alloy ; Rational design ; Activation energy ; Chemical physics ; Nanoparticle ; Deuterium ; Chemistry ; Kinetics ; Chemical kinetics ; Materials science ; Nanotechnology ; Combinatorial chemistry ; Chemical engineering ; Physical chemistry ; Physics ; Organic chemistry ; Atomic physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: Xueyang Li | Christian C. Roth | Colin Bonatti | Dirk Mohr
Source: International Journal of Plasticity (article, 2022-01-13)
Field: Materials Science — Materials Chemistry

Counterexample-trained neural network model of rate and temperature dependent hardening with dynamic strain aging Constitutive models dealing with the thermal and visco-plasticity of metals have seen wide applications in the automotive industry. A basic plasticity and fracture characterization of a 1.5 mm thick DP780 dual phase steel sheet based on uniaxial tensile (UT) experiments with seven distinct material orientations is complemented by low (∼0.001/s), intermediate (∼1/s) and high (∼150/s) strain rate experiments on notched tensile (NT) and shear (SH) specimens at temperatures ranging from 20 °C to 500 °C. At low strain rates, we observe a non-monotonic effect of the temperature on the force-displacement curves, with the highest curve obtained at 300 °C. Contrasting low speed tests, a monotonic effect of temperature is observed for intermediate and high strain rate experiments, with the highest curves obtained at 20 °C for both cases. Strain rate jump tests are performed proving the positive strain rate sensitivity of the steel. A machine-learning based plasticity model is developed to capture the observed complex strain rate- and temperature effect. The material is modeled as elasto-plastic, with a Hill’48 yield surface and a non-associated flow rule. The flow resistance is decoupled into a reference strain hardening term and a neural network term, which is a function of the plastic strain, strain rate, temperature, and an additional dynamic strain aging variable. The plasticity model is implemented into a material user subroutine and identified using a counterexample-guided hybrid experimental-numerical approach. The extracted loading paths reveal a complex rate and temperature effect on the ductility of DP780. A neural network based fracture initiation model is therefore adopted to describe the fracture onset across various stress states, strain rates and temperatures considered. Materials science ; Plasticity ; Strain rate ; Hardening (computing) ; Composite material ; Monotonic function ; Dynamic strain aging ; Strain hardening exponent ; Ultimate tensile strength ; Flow stress ; Structural engineering ; Mathematics ; Mathematical analysis High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques ; Microstructure and mechanical properties
Authors: Dongxu Cui | Yun Geng | Junning Kou | Guo‐Gang Shan | Chunyi Sun | Kunhao Zhang | Xinlong Wang | Zhong‐Min Su
Source: Nature Communications (article, 2022-07-11)
Field: Materials Science — Materials Chemistry

Chiral self-sorting and guest recognition of porous aromatic cages The synthesis of ultra-stable chiral porous organic cages (POCs) and their controllable chiral self-sorting at the molecular and supramolecular level remains challening. Herein, we report the design and synthesis of a serial of axially chiral porous aromatic cages (PAC 1-S and 1-R) with high chemical stability. The theoretical and experimental studies on the chiral self-sorting reveal that the exclusive self-recognition on cage formation is an enthalpy-driven process while the chiral narcissistic and self-sorting on supramolecular assembly of racemic cages can be precisely regulated by π-π and C-H…π interactions from different solvents. Regarding the chemical stability, the crystallinity of PAC 1 is maintained in aqueous solvents, such as boiling water, high-concentrated acid and alkali; mixtures of solvents, such as 1 M H<sub>2</sub>SO<sub>4</sub>/MeOH/H<sub>2</sub>O solution, are also tolerated. Investigations on the chiral sensing performance show that PAC 1 enables enantioselective recognition of axially chiral biaryl molecules. Supramolecular chemistry ; Aqueous solution ; Self-assembly ; Molecular recognition ; Diastereomer ; Crystallinity ; Molecule ; Chemistry ; Materials science ; Enantioselective synthesis ; Chemical engineering ; Organic chemistry ; Combinatorial chemistry ; Crystallography ; Catalysis Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Long Xia | Zhongxi Miao | Juguo Dai | Aoqi Zhu | Hui Xu | Jiahui Zhong | Yang Chen | Weiang Luo | Yiting Xu | Conghui Yuan | Birong Zeng | Hesheng Cao | Lizong Dai
Source: Chemical Engineering Journal (article, 2022-02-23)
Field: Materials Science — Polymers and Plastics

Facile fabrication of multifunctional flame retardant epoxy resin by a core–shell structural AgNC@boronate polymer Nanocomposite ; Materials science ; Epoxy ; Composite material ; Fire retardant ; Thermal conductivity ; Polymer Flame retardant materials and properties ; Electromagnetic wave absorption materials ; Fiber-reinforced polymer composites
Authors: Ubaid Khan | Yijia Luo | Ling Bing Kong | Wenxiu Que
Source: Journal of Alloys and Compounds (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Synthesis of fluorine free MXene through lewis acidic etching for application as electrode of proton supercapacitors MXenes ; Electrochemistry ; Electrolyte ; Supercapacitor ; Nitride ; Lewis acids and bases ; Inorganic chemistry ; Molten salt ; Electrode ; Materials science ; Fluorine ; Salt (chemistry) ; Carbide ; Ionic liquid ; Deprotonation ; Chemistry ; Chemical engineering ; Nanotechnology ; Ion ; Layer (electronics) ; Physical chemistry ; Organic chemistry ; Catalysis MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Basheer Ahmed Kalwar | Wang Fangzong | Amir Mahmood Soomro | Muhammad Rafique Naich | Muhammad Hammad Saeed | Irfan Ahmed
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Highly sensitive work function type room temperature gas sensor based on Ti doped hBN monolayer for sensing CO<sub>2</sub>, CO, H<sub>2</sub>S, HF and NO. A DFT study The adsorptions of toxic gas molecules (CO<sub>2</sub>, CO, H<sub>2</sub>S, HF and NO) on pristine and Ti atom doped hexagonal boron nitride (hBN) monolayer are investigated by density functional theory. Weak physisorption of gas molecules on pristine hBN results in micro seconds recovery time, limiting the gas sensing ability of pristine hBN. However Ti atom doping significantly enhances the adsorption ability. Ti atom best fits to be doped at B vacancy in hBN with lowest formation energy (-3.241 eV). Structural analysis reveals that structures of gas molecules change after being chemisorbed to Ti doped hBN monolayer. Partial density of states analysis illustrates strong hybridization among Ti-3d, gas-2p and BN-2p orbitals, moreover Bader charge transfer indicates that gas molecules act as charge acceptors. Ti doped hBN monolayer undergoes transition from semiconductor to narrow band semiconductor with adsorption of CO<sub>2</sub>, H<sub>2</sub>S and NO, while with CO and HF adsorption it transforms into metal. The change of conductance of Ti doped hBN monolayer in response to adsorption of gas molecules reveals its high sensitivity, however it is not selective to HF and NO gases. The recovery times of gas molecules desorption from monolayer are too long at ambient condition however it can significantly be shortened by annealing at elevated temperature with UV exposure. Since recovery time for NO removal from monolayer is still very long at 500 K with UV exposure, Ti doped hBN monolayer is more suitable as a scavenger of NO gas rather than as a gas sensor. It is thus predicted that Ti doped hBN monolayer can be a work-function type CO<sub>2</sub>, CO, H<sub>2</sub>S and HF sensor and NO gas scavenger. Monolayer ; Work function ; Doping ; Physisorption ; Adsorption ; Molecule ; Materials science ; Desorption ; Density functional theory ; Analytical Chemistry (journal) ; Chemical physics ; Chemistry ; Nanotechnology ; Physical chemistry ; Computational chemistry ; Organic chemistry Graphene research and applications ; 2D Materials and Applications ; Molecular Junctions and Nanostructures
Authors: Cristian Rodríguez-Tinoco | Marta González-Silveira | M. A. Ramos | J. Rodríguez‐Viejo
Source: Rivista Del Nuovo Cimento (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Ultrastable glasses: new perspectives for an old problem Abstract Ultrastable glasses (mostly prepared from the vapor phase under optimized deposition conditions) represent a unique class of materials with low enthalpies and high kinetic stabilities. These highly stable and dense glasses show unique physicochemical properties, such as high thermal stability, improved mechanical properties or anomalous transitions into the supercooled liquid, offering unprecedented opportunities to understand many aspects of the glassy state. Their improved properties with respect to liquid-cooled glasses also open new prospects to their use in applications where liquid-cooled glasses failed or where not considered as usable materials. In this review article we summarize the state of the art of vapor-deposited (and other) ultrastable glasses with a focus on the mechanism of equilibration, the transformation to the liquid state and the low temperature properties. The review contains information on organic, metallic, polymeric and chalcogenide glasses and an updated list with relevant properties of all materials known today to form a stable glass. Supercooling ; Materials science ; Chalcogenide ; Glass transition ; Thermal stability ; USable ; Phase (matter) ; Nanotechnology ; Polymer ; Engineering physics ; Chemical engineering ; Thermodynamics ; Composite material ; Chemistry ; Metallurgy ; Computer science ; Organic chemistry ; Physics Material Dynamics and Properties ; Glass properties and applications ; Phase-change materials and chalcogenides
Authors: Diddo Diddens | Williams Agyei Appiah | Youssef Mabrouk | Andreas Heuer | Tejs Vegge | Arghya Bhowmik
Source: Advanced Materials Interfaces (article, 2022-01-30)
Field: Materials Science — Materials Chemistry

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer? Abstract The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its essential function is to prevent the electrolyte from continuous electrochemical degradation, while simultaneously allowing ions to pass through, thus constituting an electronically insulating, but ionically conducting material. Its properties crucially affect the overall performance and aging of a battery cell. Despite decades of intense research, understanding the SEI's precise formation mechanism, structure, composition, and evolution remains a conundrum. State‐of‐the‐art computational modeling techniques are powerful tools to gain additional insights, although confronted with a trade‐off between accuracy and accessible time‐ and length scales. In this review, it is discussed how recent advances in data‐driven models, especially the development of fast and accurate surrogate simulators and deep generative models, can work with physics‐based and physics‐informed approaches to enable the next generation of breakthroughs in this field. Machine learning‐enhanced multiscale models can provide new pathways to inverse the design of interphases with desired properties. Materials science ; Interphase ; Electrolyte ; Nanotechnology ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials