jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Wang | Xiaoyuan Zhang | Keming Wan | Nan Zhou | Gang Wei | Zhiqiang Su
Source: Journal of Nanobiotechnology (review, 2021-08-23)
Field: Materials Science — Biomaterials

Supramolecular peptide nano-assemblies for cancer diagnosis and therapy: from molecular design to material synthesis and function-specific applications Peptide molecule has high bioactivity, good biocompatibility, and excellent biodegradability. In addition, it has adjustable amino acid structure and sequence, which can be flexible designed and tailored to form supramolecular nano-assemblies with specific biomimicking, recognition, and targeting properties via molecular self-assembly. These unique properties of peptide nano-assemblies made it possible for utilizing them for biomedical and tissue engineering applications. In this review, we summarize recent progress on the motif design, self-assembly synthesis, and functional tailoring of peptide nano-assemblies for both cancer diagnosis and therapy. For this aim, firstly we demonstrate the methodologies on the synthesis of various functional pure and hybrid peptide nano-assemblies, by which the structural and functional tailoring of peptide nano-assemblies are introduced and discussed in detail. Secondly, we present the applications of peptide nano-assemblies for cancer diagnosis applications, including optical and magnetic imaging as well as biosensing of cancer cells. Thirdly, the design of peptide nano-assemblies for enzyme-mediated killing, chemo-therapy, photothermal therapy, and multi-therapy of cancer cells are introduced. Finally, the challenges and perspectives in this promising topic are discussed. This work will be useful for readers to understand the methodologies on peptide design and functional tailoring for highly effective, specific, and targeted diagnosis and therapy of cancers, and at the same time it will promote the development of cancer diagnosis and therapy by linking those knowledges in biological science, nanotechnology, biomedicine, tissue engineering, and analytical science. Supramolecular chemistry ; Nanotechnology ; Cancer therapy ; Function (biology) ; Nano- ; Peptide ; Cancer ; Chemistry ; Materials science ; Medicine ; Biology ; Biochemistry ; Molecule ; Cell biology ; Internal medicine ; Organic chemistry Supramolecular Self-Assembly in Materials ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Ana Palčić | Simona Moldovan | Hussein El Siblani | Aurélie Vicente | Valentin Valtchev
Source: Advanced Science (article, 2021-11-27)
Field: Chemistry — Inorganic Chemistry

Defect Sites in Zeolites: Origin and Healing This paper deals with the synthesis conditions-defect formation relationship in zeolites. Silicalite-1 (MFI-type) is used as a model material. Samples synthesized from a system with high basicity (at 100 °C), a system with moderate basicity (at 150 °C), and a fluoride-containing system in neutral medium (at 170 °C) are compared. Well-crystallized materials with sizes ≈0.1, 1-10, and 30-40 µm are obtained. The samples are analyzed by complementary methods providing information on the short- and long-range order in the zeolite framework. A strong correlation between the number of point defects in the zeolite framework and preparation conditions is established. Silicalite-1 synthesized under mild synthesis conditions from a highly basic system exhibits a larger number of framework defects and thus low hydrophobicity. Further, the calcined samples are subjected to aluminum and silicon incorporation by postsynthesis treatment. The Al/Si incorporation in the zeolite framework and its impact on the physicochemical properties is studied by XRD, TEM/SEM, solid-state NMR, FTIR, and thermogravimetric analyses. The defects healing as a function of the number of point defects in the initial material and zeolite crystal size is evaluated. The results of this study will serve for fine-tuning zeolite properties by in situ and postsynthesis methods. Zeolite ; Thermogravimetric analysis ; Calcination ; Fourier transform infrared spectroscopy ; Materials science ; Chemical engineering ; Silicon ; Aluminium ; Mineralogy ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Catalysis ; Composite material ; Metallurgy Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications ; Chemical Synthesis and Characterization
Authors: Hui Ling Chia | Carmen C. Mayorga‐Martinez | Martin Pumera
Source: Advanced Functional Materials (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

Doping and Decorating 2D Materials for Biosensing: Benefits and Drawbacks Abstract The rapid advancements in the field of materials science, especially nanoscience, have played a critical role in the advancement of sensors, in particular the development of novel transducer platforms. Sensors are devices that respond to specific phenomena with recordable and analytically useful output signals and have found their way into a myriad of applications in daily life. Some of these applications include the measurement of glucose and cholesterol levels and detection of emerging infectious diseases for biomedical purposes, environmental monitoring, and food analysis. 2D materials proffer numerous advantageous physical, chemical, electronic, and optical attributes such as large specific surface areas, excellent electrical and thermal conductivity, an abundance of catalytic sites, ease of functionalization, and tuneable electronic structures, allowing them to hold promising potential for the development of sensors with high sensitivity. Although layered materials demonstrate many beneficial attributes for the development of sensors, the properties and electronic structure of layered materials can be fine‐tuned via doping or decorating to enhance the sensing performance. This review highlights the current progress of electrical and optical sensors based upon metal‐decorated and metal‐doped 2D materials and examines the effects of decorating and doping 2D materials for sensor developments. Materials science ; Nanotechnology ; Biosensor ; Doping ; Surface modification ; Electronic materials ; Characterization (materials science) ; Transducer ; Optoelectronics ; Electrical engineering ; Mechanical engineering 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Pierfrancesco Maltoni | Tapati Sarkar | G. Barucca | Gaspare Varvaro | Federico Locardi | Davide Peddis | R. Mathieu
Source: The Journal of Physical Chemistry C (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

Tuning the Magnetic Properties of Hard–Soft SrFe<sub>12</sub>O<sub>19</sub>/CoFe<sub>2</sub>O<sub>4</sub> Nanostructures via Composition/Interphase Coupling Magnetic nanocomposites (NCs) are extremely appealing for a wide range of energy-related technological applications, specifically as building blocks for next-generation permanent magnets. The design of such nanostructures requires precise chemical synthesis methods, which will permit the fine-tuning of the magnetic properties. Here we present an in-depth structural, morphological and magnetic characterization of ferrite-based nanostructures obtained through a bottom-up sol-gel approach. The combination of the high coercivity of a hard phase SrFe12O19 (SFO) and the high saturation magnetization of a soft phase, CoFe2O4 (CFO), allowed us to develop exchange-coupled bimagnetic NCs. A symbiotic effect is observed in a SFO/CFO nanocomposite, as the unique oriented growth of SFO prevents grain growth of the CFO, thus restricting the crystallite size of both. Through X-ray powder diffraction (XRPD), transmission electron microscopy (TEM), and magnetic measurements we clarify the relationship between the distribution and size of hard/soft particles, the optimization of interfaces and the obtained uniform magnetic response. This study allowed us to establish the potentiality of hard/soft SFO/CFO nanostructures in current permanent magnet technology. Coercivity ; Materials science ; Crystallite ; Magnet ; Nanostructure ; Nanocomposite ; Grain size ; Transmission electron microscopy ; Magnetization ; Nanotechnology ; Condensed matter physics ; Metallurgy ; Magnetic field ; Electrical engineering Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Magnetic Properties of Alloys
Authors: Paulina Bolek | Justyna Zeler | Carlos D. S. Brites | Joanna Trojan–Piegza | Luís D. Carlos | Eugeniusz Zych
Source: Chemical Engineering Journal (article, 2021-04-20)
Field: Materials Science — Materials Chemistry

Ga-modified YAG:Pr3+ dual-mode tunable luminescence thermometers The temperature determination using luminescent materials is nowadays considered the perspective remote technique for temperature gauging, despite the few examples reported so far combining wide operating temperature range with satisfying relative thermal sensitivity and temperature uncertainty values. In this paper, we study the Y3(Al,Ga)5O12:0.1%Pr phosphors with controlled Ga:Al composition to deliberately affect their luminescent properties. We demonstrate that the energy barrier for thermal quenching of the 5d-4f luminescence can be effectively tailored, yielding the fine tune of the thermometric parameters of these phosphors. By exploiting time-resolved and time-integrated approaches we show that the thermometers can cover the 17–700 K temperature range with a maximum relative sensitivity up to 3.6%·K−1 and a temperature uncertainty as lower as 0.02 K. For each sample, the temperature readout of the distinct thermometric parameters is compared illustrating that the performance of the thermometers should also consider the relative temperature error between the calculated and the measured temperatures, besides relative thermal sensitivity and temperature uncertainty. Luminescence ; Atmospheric temperature range ; Sensitivity (control systems) ; Phosphor ; Temperature measurement ; Thermal ; Materials science ; Analytical Chemistry (journal) ; Quenching (fluorescence) ; Approximation error ; Thermometer ; Range (aeronautics) ; Optoelectronics ; Thermodynamics ; Optics ; Chemistry ; Physics ; Composite material ; Electronic engineering ; Fluorescence ; Statistics ; Mathematics Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Radiation Detection and Scintillator Technologies
Authors: Aliya F. Khusnuriyalova | Maria Caporali | Evamarie Hey‐Hawkins | Оleg G. Sinyashin | Dmitry G. Yakhvarov
Source: European Journal of Inorganic Chemistry (article, 2021-06-15)
Field: Chemistry — Organic Chemistry

Preparation of Cobalt Nanoparticles Abstract The development of modern chemistry is currently proceeding in several priority areas including investigations focused on the synthesis, stabilisation, and application of transition metal nanoparticles (NPs), which are widely used in physical, chemical, engineering, and biomedical processes. A special place among known transition metal NPs is occupied by cobalt nanoparticles, since they are used for highly important targets, such as creation of new catalysts, magnetic devices, composites, or carriers for drug delivery. The selective preparation of NPs is a difficult task that requires special conditions and has some limitations. In this minireview, we summarise the most successful and most efficient methods for obtaining Co NPs, including chemical and physical aspects of their preparation. Chemistry ; Cobalt ; Nanotechnology ; Nanoparticle ; Transition metal ; Drug delivery ; Magnetic nanoparticles ; Catalysis ; Organic chemistry ; Materials science Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Xu Wang | Ji Ma | Wenhao Zheng | Silvio Osella | Nicolás Arisnabarreta | Jörn Droste | Gianluca Serra | Oleksandr Ivasenko | Andrea Lucotti | David Beljonne | Mischa Bonn | Xiangyang Liu | Michael Ryan Hansen | Matteo Tommasini | Steven De Feyter | Junzhi Liu | Hai I. Wang | Xinliang Feng
Source: Journal of the American Chemical Society (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

Cove-Edged Graphene Nanoribbons with Incorporation of Periodic Zigzag-Edge Segments Structurally precision graphene nanoribbons (GNRs) are promising candidates for next-generation nanoelectronics due to their intriguing and tunable electronic structures. GNRs with hybrid edge structures often confer them unique geometries associated with exotic physicochemical properties. Herein, a novel type of cove-edged GNRs with periodic short zigzag-edge segments is demonstrated. The bandgap of this GNR family can be tuned using an interplay between the length of the zigzag segments and the distance of two adjacent cove units along the opposite edges, which can be converted from semiconducting to nearly metallic. A family member with periodic cove-zigzag edges based on <i>N</i> = 6 zigzag-edged GNR, namely <b>6-CZGNR-(2,1)</b>, is successfully synthesized in solution through the Scholl reaction of a unique snakelike polymer precursor (<b>10</b>) that is achieved by the Yamamoto coupling of a structurally flexible S-shaped phenanthrene-based monomer (<b>1</b>). The efficiency of cyclodehydrogenation of polymer <b>10</b> toward <b>6-CZGNR-(2,1)</b> is validated by FT-IR, Raman, and UV-vis spectroscopies, as well as by the study of two representative model compounds (<b>2</b> and <b>3</b>). Remarkably, the resultant <b>6-CZGNR-(2,1)</b> exhibits an extended and broad absorption in the near-infrared region with a record narrow optical bandgap of 0.99 eV among the reported solution-synthesized GNRs. Moreover, <b>6-CZGNR-(2,1)</b> exhibits a high macroscopic carrier mobility of ∼20 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> determined by terahertz spectroscopy, primarily due to the intrinsically small effective mass (<i>m</i>*<sub>e</sub> = <i>m</i>*<sub>h</sub> = 0.17 <i>m</i><sub>0</sub>), rendering this GNR a promising candidate for nanoelectronics. Zigzag ; Graphene ; Graphene nanoribbons ; Band gap ; Raman spectroscopy ; Chemistry ; Cove ; Polymer ; Nanotechnology ; Crystallography ; Optoelectronics ; Materials science ; Optics ; Geometry ; Physics ; Organic chemistry Graphene research and applications ; Molecular Junctions and Nanostructures ; Synthesis and Properties of Aromatic Compounds
Authors: Manosree Chatterjee | Nripen Chanda
Source: Materials Advances (article, 2021-12-02)
Field: Materials Science — Biomaterials

Formulation of PLGA nano-carriers: specialized modification for cancer therapeutic applications Synthesis and functionalization of PLGA nanoparticles for cancer therapeutic applications. Surface modification ; PLGA ; Cancer ; Cancer therapy ; Nanotechnology ; Nanoparticle ; Materials science ; Medicine ; Engineering ; Internal medicine ; Chemical engineering Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced Drug Delivery Systems
Authors: Ohad Suss | Leila Motiei | David Margulies
Source: Molecules (review, 2021-05-10)
Field: Materials Science — Materials Chemistry

Broad Applications of Thiazole Orange in Fluorescent Sensing of Biomolecules and Ions Fluorescent sensing of biomolecules has served as a revolutionary tool for studying and better understanding various biological systems. Therefore, it has become increasingly important to identify fluorescent building blocks that can be easily converted into sensing probes, which can detect specific targets with increasing sensitivity and accuracy. Over the past 30 years, thiazole orange (TO) has garnered great attention due to its low fluorescence background signal and remarkable 'turn-on' fluorescence response, being controlled only by its intramolecular torsional movement. These features have led to the development of numerous molecular probes that apply TO in order to sense a variety of biomolecules and metal ions. Here, we highlight the tremendous progress made in the field of TO-based sensors and demonstrate the different strategies that have enabled TO to evolve into a versatile dye for monitoring a collection of biomolecules. Biomolecule ; Fluorescence ; Nanotechnology ; Förster resonance energy transfer ; Metal ions in aqueous solution ; Chemistry ; Computer science ; Materials science ; Ion ; Physics ; Optics Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques ; Click Chemistry and Applications
Authors: Mikhail A. Soldatov | Vera V. Butova | D. M. Pashkov | Maria A. Butakova | P. V. Medvedev | А. В. Чернов | А. В. Солдатов
Source: Nanomaterials (review, 2021-03-02)
Field: Materials Science — Materials Chemistry

Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI's role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories' self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials' synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries. Computer science ; Automation ; Process (computing) ; Emerging technologies ; Data science ; Nanotechnology ; Artificial intelligence ; Engineering ; Materials science ; Mechanical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Seung-Woo Seo | You Young Song | June Yong Yang | Seohui Bae | Hankook Lee | Jinwoo Shin | Sung Ju Hwang | Eunho Yang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

GTA: Graph Truncated Attention for Retrosynthesis Retrosynthesis is the task of predicting reactant molecules from a given product molecule and is, important in organic chemistry because the identification of a synthetic path is as demanding as the discovery of new chemical compounds. Recently, the retrosynthesis task has been solved automatically without human expertise using powerful deep learning models. Recent deep models are primarily based on seq2seq or graph neural networks depending on the function of molecular representation, sequence, or graph. Current state-of-the-art models represent a molecule as a graph, but they require joint training with auxiliary prediction tasks, such as the most probable reaction template or reaction center prediction. Furthermore, they require additional labels by experienced chemists, thereby incurring additional cost. Herein, we propose a novel template-free model, i.e., Graph Truncated Attention (GTA), which leverages both sequence and graph representations by inserting graphical information into a seq2seq model. The proposed GTA model masks the self-attention layer using the adjacency matrix of product molecule in the encoder and applies a new loss using atom mapping acquired from an automated algorithm to the cross-attention layer in the decoder. Our model achieves new state-of-the-art records, i.e., exact match top-1 and top-10 accuracies of 51.1% and 81.6% on the USPTO-50k benchmark dataset, respectively, and 46.0% and 70.0% on the USPTO-full dataset, respectively, both without any reaction class information. The GTA model surpasses prior graph-based template-free models by 2% and 7% in terms of the top-1 and top-10 accuracies on the USPTO-50k dataset, respectively, and by over 6% for both the top-1 and top-10 accuracies on the USPTO-full dataset. Retrosynthetic analysis ; Computer science ; Graph ; Molecular graph ; Adjacency matrix ; Benchmark (surveying) ; Artificial intelligence ; Deep learning ; Adjacency list ; Algorithm ; Pattern recognition (psychology) ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jaehyun Kim | Donghoon Kang | Sang‐Bum Kim | Ho Won Jang
Source: ACS Materials Letters (article, 2021-07-02)
Field: Materials Science — Materials Chemistry

Catalyze Materials Science with Machine Learning Discovering and understanding new materials with desired properties are at the heart of materials science research, and machine learning (ML) has recently offered special shortcuts to the ultimate goal. Thanks to the nourishment of computer hardware and computational chemistry, the development of calculated scientific data repositories could fuel the ML models to investigate the vast materials space. At this moment, understanding this revolutionary paradigm is urgent, and this Review aims to deliver comprehensive information about the collaboration of ML with materials science. This Review summarizes recent achievements in catalysts design, which can be benefitted from ML because of the complex nature of catalytic reactions and vast candidate materials space. ML models for catalyst design could be transferred to applications in other domains and vice versa. The basic concepts of ML algorithms and practical guides to materials scientists are also demonstrated. Moreover, challenges and strategies of applying ML are discussed, which should be addressed collaboratively between materials scientists and ML communities. Ultimate integrations of ML in materials science are expected to accelerate the design, discovery, optimization, and interpretation of materials in both industry and academia, and this Review hopes to be the informative base camp for that journey. Computer science ; Space (punctuation) ; Interpretation (philosophy) ; Data science ; Nanotechnology ; Biochemical engineering ; Management science ; Engineering ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography