Materials Science Research Articles

Gem-Difluoroalkylation of Cyclic Ethers Enabled by Cobalt-Catalyzed C(sp3)-H Oxidation under Mild Conditions.

Fluorinated rings have emerged as privileged structural modules in the fields of drug discovery and materials research. The incorporation of fluorine atoms into aromatic rings or heterocycles can lead to significant improvements in the physicochemical and biological properties of small molecules, making them valuable components in the design of new drugs and functional materials. Herein, we presented a cobalt-catalyzed C-H oxidation/gem-difluorination cascade reaction of readily available cyclic ethers with difluoroenoxysilanes, affording a series of gem-difluorinated analogues with moderate to high yields. The obtained products as versatile fluoroalkyl building blocks were showcased through divergent-oriented transformations.

Acousto-dielectric tweezers enable independent manipulation of multiple particles.

Acoustic tweezers have gained substantial interest in biology, engineering, and materials science for their label-free, precise, contactless, and programmable manipulation of small objects. However, acoustic tweezers cannot independently manipulate multiple microparticles simultaneously. This study introduces acousto-dielectric tweezers capable of independently manipulating multiple microparticles and precise control over intercellular distances and cyclical cell pairing and separation for detailed cell-cell interaction analysis. Our acousto-dielectric tweezers leverage the competition between acoustic radiation forces, generated by standing surface acoustic waves (SAWs), and dielectrophoretic (DEP) forces, induced by gradient electric fields. Modulating these fields allows for the precise positioning of individual microparticles at points where acoustic radiation and DEP forces are in equilibrium. This mechanism enables the simultaneous movement of multiple microparticles along specified paths as well as cyclical cell pairing and separation. We anticipate our acousto-dielectric tweezers to have enormous potential in colloidal assembly, cell-cell interaction studies, disease diagnostics, and tissue engineering.

Bilateral Unsymmetrical Disulfurating Reagent Design for Polysulfide Construction.

Polysulfides are significant compounds in life science, pharmaceutical science, and materials science. Therefore, polysulfide construction is in great demand. The controllable sequential installation of groups on both ends of a S-S motif faces an enormous challenge owing to the reversible nature of the covalent S-S bond. A library was established with two divergent mask groups for bilateral unsymmetrical disulfurating reagents (R1O-SS-SO2R2). Sequential coupling with preferential activation of the S-SO2 bond (37.6 kcal/mol) and controllable activation of the S-O bond (54.8 kcal/mol) in the presence of the S-S bond (62.0 kcal/mol) enabled successive reactions at each end of the S-S motif to afford unsymmetrical disulfides and trisulfides, even for the cross-linkage of natural products, pharmaceuticals, peptides, and a protein (bovine serum albumin).

Supramolecular Complex Surfactants and Structured Liquids Enabled by Cation-π and Charge-Transfer Interactions.

Cation-π and charge-transfer (CT) interactions are pervasive with significant implications in the fields of chemistry, materials science, and biology. However, much less is known about the construction of interfacial assemblies based on the two interactions. Here, by combining cation-π and CT interactions between an acceptor molecule, dicationic naphthalenediimide, and an aromatic donor, pyrene-terminated poly-l-lactic acid, we report the generation of supramolecular complex surfactants (SCSs) in situ at the toluene-water interface. The utilization of SCSs as building blocks enables the fabrication of interfacial assemblies including 2D films, emulsions, and structured liquids. By modification of the redox state of the acceptor molecules under chemical stimulus, the association/assembly and dissociation/disassembly of SCSs can be precisely regulated, imparting intriguing redox-responsive properties to the resulting assemblies.

A Connected Convolutional Neutral Network Protocol for Design of Two-Dimensional Materials Based on Modified Graphdiyne.

In materials science, doping plays a crucial role in manipulating the electronic properties of materials. Conventional screening via a trial-and-error strategy is challenging owing to the enormous chemical space. We proposed a connected convolutional neutral network (CCNN) for quick screening of boron nitrogen (B-N) codoped graphdiyne in terms of band gap. A paired-atomic localized matrix (PALM) descriptor was designed to describe the local chemical environment of materials with the matrix form adapted to a neutral network. An attribution analysis was conducted, and a quantitative relationship between structure and band gap is proposed, which reveals more significant influence of B-N doping at sp2 hybridized sites than at sp hybridized sites on broadening of the band gap of GDY. The accuracy and efficiency of the proposed approach implicate its potential in promoting the design of graphdiyne-based optoelectronic devices and catalysts with expected electronic properties, opening a new avenue for rational design of novel materials.

Advances in medical polyesters for vascular tissue engineering.

Blood vessels are highly dynamic and complex structures with a variety of physiological functions, including the transport of oxygen, nutrients, and metabolic wastes. Their normal functioning involves the close and coordinated cooperation of a variety of cells. However, adverse internal and external environmental factors can lead to vascular damage and the induction of various vascular diseases, including atherosclerosis and thrombosis. This can have serious consequences for patients, and there is an urgent need for innovative techniques to repair damaged blood vessels. Polyesters have been extensively researched and used in the treatment of vascular disease and repair of blood vessels due to their excellent mechanical properties, adjustable biodegradation time, and excellent biocompatibility. Given the high complexity of vascular tissues, it is still challenging to optimize the utilization of polyesters for repairing damaged blood vessels. Nevertheless, they have considerable potential for vascular tissue engineering in a range of applications. This summary reviews the physicochemical properties of polyhydroxyalkanoate (PHA), polycaprolactone (PCL), poly-lactic acid (PLA), and poly(lactide-co-glycolide) (PLGA), focusing on their unique applications in vascular tissue engineering. Polyesters can be prepared not only as 3D scaffolds to repair damage as an alternative to vascular grafts, but also in various forms such as microspheres, fibrous membranes, and nanoparticles to deliver drugs or bioactive ingredients to damaged vessels. Finally, it is anticipated that further developments in polyesters will occur in the near future, with the potential to facilitate the wider application of these materials in vascular tissue engineering.

A first-time fusion of TiNbWMoZrOx high entropy oxide (HEO) with zinc-tellurite glass: Toward superior physical properties

While numerous oxide additives have traditionally been employed to enhance the radiation shielding capabilities of glasses, the unique attributes of high-entropy oxides (HEOs), a group of materials acclaimed in contemporary material science for their distinctive properties have remained unexamined in this specific area. This novel study explores the enhancement of radiation shielding properties in zinc-tellurite glasses through the integration of TiNbWMoZrOx High Entropy Oxides (HEO). Utilizing advanced synthesis techniques, including mechanical alloying and oxidation, the research successfully incorporates HEOs into glass matrices, aiming to improve gamma-ray and neutron attenuation. Characterization through X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) confirms the uniform distribution and structural integrity of the HEOs within the glasses. The synthesis of glass samples with a base structure suitable for the molar composition of 25ZnO.75TeO2 (mol%) and glass samples doped with TiNbWMoZrOx (HEO) was carried out using the traditional high-temperature melting and annealing method. The outcomes demonstrate a concentration-dependent increase in shielding efficacy, particularly highlighting the superior performance of glasses doped with 4 mol% of TiNbWMoZrOx (HEC2–4), which exhibit significantly enhanced mass attenuation coefficients, lower half-value layers, and higher effective atomic numbers. This indicates the effective role of HEOs in boosting radiation protection capabilities. Comparative analysis with traditional shielding materials showcases the HEC2–4 glasses' competitive advantage, underlining their potential as a versatile shielding solution. It can be concluded that incorporating TiNbWMoZrOx high entropy oxides into zinc-tellurite glasses significantly augments their radiation shielding properties, offering a novel approach for enhancing protection against gamma-ray and neutron in various applications.

Continuous polyamorphic transition in high-entropy metallic glass

Polyamorphic transition (PT) is a compelling and pivotal physical phenomenon in the field of glass and materials science. Understanding this transition is of scientific and technological significance, as it offers an important pathway for effectively tuning the structure and property of glasses. In contrast to the PT observed in conventional metallic glasses (MGs), which typically exhibit a pronounced first-order nature, herein we report a continuous PT (CPT) without first-order characteristics in high-entropy MGs (HEMGs) upon heating. This CPT behavior is featured by the continuous structural evolution at the atomic level and an increasing chemical concentration gradient with temperature, but no abrupt reduction in volume and energy. The continuous transformation is associated with the absence of local favorable structures and chemical heterogeneity caused by the high configurational entropy, which limits the distance and frequency of atomic diffusion. As a result of the CPT, numerous glass states can be generated, which provides an opportunity to understand the nature, atomic packing, formability, and properties of MGs. Moreover, this discovery highlights the implication of configurational entropy in exploring polyamorphic glasses with an identical composition but highly tunable structures and properties.

Cobalt-Catalyzed Chemo- and Stereoselective Transfer Semihydrogenation of 1,3-Dienes with Water as a Hydrogen Source.

(Z)-1,2-Disubstituted, trisubstituted, and tetrasubstituted alkenes are not only important units in medicinal chemistry, natural product synthesis, and material science but also useful intermediates in organic synthesis. Development of catalytic stereoselective transformations to access multisubstituted alkenes with various substitution patterns from easily accessible modular starting materials and readily available catalysts is a crucial goal in the field of catalysis. Water is an ideal hydrogen source for catalytic transfer hydrogenation despite of the high difficulty to activate water. Here, we report a cobalt-catalyzed protocol for regio- and stereoselective transfer semihydrogenation of 1,3-dienes to construct a broad scope of (Z)-1,2-disubstituted, (Z)-, (E)-trisubstituted, and tetrasubstituted alkenes in high stereoselectivity with H2O as the hydrogen source. Mechanistic studies revealed that the reactions proceeded through a unique Co(I)/Co(III) cycle and involved a 1,4-cobalt shift process, which is an unprecedented reaction pathway, providing a new platform for modular synthesis of multisubstituted alkenes as well as opportunities for designing novel reaction modes and pushing forward the advancement in organocobalt chemistry.

Machine Learning Interatomic Potentials for Catalysis.

Atomistic modeling can provide insights into the design of novel catalysts in modern industries of chemistry, materials science, and biology. Classical force fields andab initiocalculations have been widely adopted in molecular simulations. How- ever, these methods suffer from the drawbacks of either low accuracy or high cost. Recently, the development of machine learning interatomic potentials (MLIPs) has become more and more popular as they can tackle the problems in question and can deliver rather accurate results at significantly lower computational cost. In this review, the atomistic modeling of catalytic systems with the aid of MLIPs is discussed, showcasing recently developed MLIP models and selected applications for the modeling of catalytic systems. We also highlight the best practices, and challenges for MLIPs and give an outlook for future works on MLIPs in the field of catalysis.

Supramolecular Electronics: Monolayer Assembly of Nonamphiphilic Molecules via Water Surface-Assisted Molecular Deposition.

Utilizing softly confined self-assembly at the water surface represents a promising approach for the fabrication of two-dimensional molecular monolayers (2D MMs), which have predominantly been concentrated on amphiphilic organic compounds before. Herein, we introduce a straightforward method termed "water surface-assisted molecular deposition (WSAMD)" to organize nonamphiphilic molecules into dense monolayers with high reproducibility. To underscore the versatility and merit of this methodology in the field of supramolecular electronics, we have successfully fabricated a range of defect-free, uniform semiconducting polymer monolayers, featuring a thickness reflective of molecular architectures. The charge carrier mobility could reach 0.05 cm2 V-1 s-1 for holes and 3.5 × 10-4 cm2 V-1 s-1 for electrons, respectively, in p-type and n-type polymeric monolayers when tested as the active layer in field-effect transistors. Furthermore, in situ polymerization reactions can be exploited to generate conductive monolayers of macromolecules such as polybenzylaniline (PBnANI) and polypyrrole (PPy), where PBnANI monolayers exhibit channel length-dependent conductivity, up to 0.37 S cm-1. The advent of the WSAMD method heralds a significant leap forward in the advancement of molecular 2D materials, catalyzing new avenues of exploration within material chemistry.

Emergence of Debye Scaling in the Density of States of Liquids under Nanoconfinement.

In the realm of nanoscience, the dynamic behaviors of liquids at scales beyond the conventional structural relaxation time, τ, unfold a fascinating blend of solid-like characteristics, including the propagation of collective shear waves and the emergence of elasticity. However, in classical bulk liquids, where τ is typically of the order of 1 ps or less, this solid-like behavior remains elusive in the low-frequency region of the density of states (DOS). Here, we provide evidence for the emergent solid-like nature of liquids at short distances through inelastic neutron scattering measurements of the low-frequency DOS in liquid water and glycerol confined within graphene oxide membranes. In particular, upon increasing the strength of confinement, we observe a transition from a liquid-like DOS (linear in the frequency ω) to a solid-like behavior (Debye law, ∼ω2) in the range of 1-4 meV. Molecular dynamics simulations confirm these findings and reveal additional solid-like features, including propagating collective shear waves and a reduction in the self-diffusion constant. Finally, we show that the onset of solid-like dynamics is pushed toward low frequency along with the slowing-down of the relaxation processes upon confinement. This nanoconfinement-induced transition, aligning with k-gap theory, underscores the potential of leveraging liquid nanoconfinement in advancing nanoscale science and technology, building more connections between fluid dynamics and materials engineering.

GelMA loaded with platelet lysate promotes skin regeneration and angiogenesis in pressure ulcers by activating STAT3

Pressure ulcers (PU) are caused by persistent long-term pressure, which compromises the integrity of the epidermis, dermis, and subcutaneous adipose tissue layer by layer, making it difficult to heal. Platelet products such as platelet lysate (PL) can promote tissue regeneration by secreting numerous growth factors based on clinical studies on skin wound healing. However, the components of PL are difficult to retain in wounds. Gelatin methacrylate (GelMA) is a photopolymerizable hydrogel that has lately emerged as a promising material for tissue engineering and regenerative medicine. The PL liquid was extracted, flow cytometrically detected for CD41a markers, and evenly dispersed in the GelMA hydrogel to produce a surplus growth factor hydrogel system (PL@GM). The microstructure of the hydrogel system was observed under a scanning electron microscope, and its sustained release efficiency and biological safety were tested in vitro. Cell viability and migration of human dermal fibroblasts, and tube formation assays of human umbilical vein endothelial cells were applied to evaluate the ability of PL to promote wound healing and regeneration in vitro. Real-time polymerase chain reaction (PCR) and western blot analyses were performed to elucidate the skin regeneration mechanism of PL. We verified PL’s therapeutic effectiveness and histological analysis on the PU model. PL promoted cell viability, migration, wound healing and angiogenesis in vitro. Real-time PCR and western blot indicated PL suppressed inflammation and promoted collagen I synthesis by activating STAT3. PL@GM hydrogel system demonstrated optimal biocompatibility and favorable effects on essential cells for wound healing. PL@GM also significantly stimulated PU healing, skin regeneration, and the formation of subcutaneous collagen and blood vessels. PL@GM could accelerate PU healing by promoting fibroblasts to migrate and secrete collagen and endothelial cells to vascularize. PL@GM promises to be an effective and convenient treatment modality for PU, like chronic wound treatment.

Characterization, impact and wear properties of treated and as-received graphene nanosheets reinforcement in epoxy resin composites

PurposeThis study aims to explore the enhancement of mechanical properties in epoxy resin composites through the incorporation of graphene nanoparticles, focusing on their impact and wear resistance. It investigates the role of graphene, both treated and untreated, as a reinforcing agent in composites, highlighting the significance of nanoparticle dispersion and surfactant treatment in optimizing mechanical performance.Design/methodology/approachEmploying a novel dispersion technique using a drawing brush, this research contrasts with traditional methods by examining the effects of graphene nanoparticle concentrations treated with surfactants – Polyvinylpyrrolidone (PVP) and Sulphonated Naphthalene Formaldehyde (SNF) – on the mechanical properties of epoxy resin composites. The methodology includes conducting a series of impact and wear tests to assess the influence of graphene reinforcement on the composites' performance.FindingsThe findings reveal a marked enhancement in the composites impact resistance and energy absorption capabilities, which escalate with an increase in graphene content. Additionally, the study demonstrates a significant improvement in wear resistance, attributed to the superior mechanical properties, robust interface adhesion and effective dispersion of graphene. The use of surfactants for graphene treatment is identified as a crucial factor in these advancements, offering profound insights into the development of advanced composite materials for diverse industrial uses.Originality/valueThis study introduces a unique dispersion technique for graphene in epoxy composites, setting it apart from conventional methods. By focusing on the critical role of surfactant treatment in enhancing the mechanical properties of graphene-reinforced composites, it provides a novel insight into the optimization of impact and wear resistance.

Encapsulation of Sn Sub-nanoclusters in Multichannel Carbon Matrix for High-Performance Potassium-ion Batteries.

Sub-nanoclusters with ultra-small particle sizes are particularly significant to create advanced energy storage materials. Herein, Sn sub-nanoclusters encapsulated in nitrogen-doped multichannel carbon matrix (denoted as Sn-SCs@MCNF) are designed by a facile and controllable route as flexible anode for high-performance potassium ion batteries (PIBs). The uniformly dispersed Sn sub-nanoclusters in multichannel carbon matrix can be precisely identified, which ensure us to clarify the size influence on the electrochemical performance. The sub-nanoscale effect of Sn-SCs@MCNF restrains electrode pulverization and enhances the K+ diffusion kinetics, leading to the superior cycling stability and rate performance. As freestanding anode in PIBs, Sn-SCs@MCNF manifests superior K+ storage properties, such as exceptional cycling stability (331 mAh g-1 after 150 cycles at 100 mA g-1) and rate capability. Especially, the Sn-SCs@MCNF||KFe[Fe(CN)6] full cell demonstrates impressive reversible capacity of 167 mAh g-1 at 0.4 A g-1 even after 200 cycles. Theoretical calculations clarify that the ultrafine Sn sub-nanoclusters are beneficial for electron transfer and contribute to the lower energy barriers of the intermediates, thereby resulting in promising electrochemical performance. Comprehensive investigation for the intrinsic K+ storage process of Sn-SCs@MCNF is revealed by in situ analysis. This work provides vital guidance to design sub-nanoscale functional materials for high-performance energy-storage devices.

A Redox-Active Iron-Organic Framework Cathodes for Sustainable Magnesium Metal Batteries.

Rechargeable magnesium metal batteries (RMBs) have shown promising prospects in sustainable energy storage due to the high crustal abundance, safety, and potentially large specific capacity of magnesium. However, their development is constrained by the lack of effective cathode materials that can achieve high capacity and stable magnesium storage at a practically reasonable rate. Herein, we construct a three-dimensional (3D) iron(III)-dihydroxy-benzoquinone (Fe2(DHBQ)3) metal-organic framework (MOF) material with dual redox centers of Fe3+ cations and DHBQ2- anions for reversible storage of Mg2+ in RMBs. Spectroscopic analysis and density functional theory (DFT) calculations reveal the redox chemistry of both Fe3+ ions and carbonyls from DHBQ ligands during electrochemical processes. Benefiting from the rational structure, the Fe2(DHBQ)3∥Mg cells exhibit a high reversible capacity of 395.3 mAh/g, large energy density of 463.5 Wh/kg, and high power density of 2456.0 W/kg. Moreover, the high electronic conductivity (8.35 × 10-5 S/cm) and favorable diffusion path of Mg2+ in Fe2(DHBQ)3 endow the cells with exceptional cycling stability and rate capability with a long life of 5000 cycles at 2000 mA/g. The dual redox-active MOF demonstrates a category of advanced cathode materials for high-performance RMBs.

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases. This study emphasizes the importance of feature analysis and selection in ML modeling and demonstrates the efficacy of our gradient-boosted statistical feature-selection workflow for training predictive models. The models are fine-tuned through Bayesian optimization, using features derived solely from the chemical compositions of the materials data, before the model predictions are evaluated against literature values. We have collated ca. 35,000 Tc values and the performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our methodology is superior to the majority of alternative methods. In a 10-fold cross-validation, our regression model realized an R2 of (0.92 ± 0.01), an MAE of (40.8 ± 1.9) K, and an RMSE of (80.0 ± 5.0) K. We demonstrate the utility of our ML model through case studies that forecast Tc values for rare-earth intermetallic compounds and generate magnetic phase diagrams for various chemical systems. These case studies highlight the importance of a systematic approach to feature analysis and selection in enhancing both the predictive capability and interpretability of ML models, while being devoid of potential human bias. They demonstrate the advantages of such an approach over a mere reliance on algorithmic complexity and a black-box treatment in ML-based modeling within the domain of computational materials science.

Managers’ sociocognitive conflicts in collaborative learning

ABSTRACT Despite extensive research on collaborative learning and sociocognitive conflict, its impact on executive education and managers’ learning is less understood. This study addresses this gap by identifying the manifestations and topics of verbalised sociocognitive conflicts and reflective collaborative discussions in managers’ collaborative small-group learning settings. The research material comprises approximately 40.5 hours of video recordings from three separate small groups. This video material was analysed qualitatively using thematic analysis, guided by prior collaborative learning research, focusing on the role of sociocognitive conflict. Various forms of challenging as manifestations of verbalised sociocognitive conflicts were identified in the small-group setting at both the group and structural and individual and peer levels. The study demonstrates how managers’ discussions evolve from sociocognitive conflict to reflective conversations fostering collaborative learning, highlighting the crucial role of these conflicts in managers’ learning. Theoretical implications provide insights into how sociocognitive conflict evolves into collaborative learning in managers’ education, suggesting insights for further research. This study has practical implications for developing collaborative learning and executive education for managers by leveraging sociocognitive conflicts.

Discovery of Novel Materials Through Machine Learning.

Experimental exploration of new materials relies heavily on a laborious trial-and-error approach. In addition to substantial time and resource requirements, traditional experiments and computational modelling are typically limited in finding target materials within the enormous chemical space. Therefore, creating innovative techniques to expedite material discovery becomes essential. Recently, machine learning has emerged as a valuable tool for material discovery, garnering significant attention due to its remarkable advancements in prediction accuracy and time efficiency. This rapidly developing computational technique accelerates the search and optimization process and enables the prediction of material properties at a minimal computational cost, thereby facilitating the discovery of novel materials. We provide a comprehensive overview of recent studies on discovering new materials by predicting materials and their properties using machine learning techniques. Beginning with an introduction of the fundamental principles of machine learning methods, we subsequently examine the current research landscape on the applications of machine learning in predicting material properties that lead to the discovery of novel materials. Finally, we discuss challenges in employing machine learning within materials science, propose potential solutions, and outline future research directions.

Molecular dynamics simulation of salt diffusion in constituting phosphazene-based polymer electrolyte

A growing demand to visualize polymer models in liquid poses a computational challenge in molecular dynamics (MD) simulation, as this requires emerging models under suitable force fields (FFs) to capture the underlying molecular behaviour accurately. In our present study, we have employed TIP3P potential on water and all atomistic optimized potentials for liquid simulations FFs to study the liquid electrolyte behavior of phosphazene-based polymer by considering its potential use in lithium-ion polymer batteries. We have explored the polymer's local structure, chain packing, wettability, and hydrophobic tendencies against the silicon surface using a combination of a pseudocontinuum model in MD simulation, and surface-sensitive sum frequency generation (SFG) vibrational spectroscopy. The finding yields invaluable insights into the molecular architecture of phosphazene. This approach identifies the importance of hydrophobic interactions with air and hydrophilic units with water molecules in understanding the behavior and properties of phosphazene-based polymers at interfaces, contributing to its advancements in materials science. The MD study uniquely captures traces of the polymer-ion linkage, which is observed to become more pronounced with the increase in polymer weight fraction. The theoretical observation of this linkage's influence on lithium-ion diffusion motion offers valuable insights into the fundamental physics governing the behavior of atoms and molecules within phosphazene-based polymer electrolytes in aqueous environments. Further these predictions are corroborated in the molecular-level depiction at the air-aqueous interface, as evidenced from the OH-oscillator strength variation measured by the SFG spectroscopy.The fundamental findings from this study open new avenues for utilizing MD simulation as a versatile methodology to gain profound insights into intermolecular interactions of polymer. It could be useful in the application of biomedical and energy-related research, such as polymer lithium-ion batteries, fuel cells, and organic solar cells.