Translational bioengineering strategies for peripheral nerve regeneration:opportunities,challenges,and novel concepts

Peripheral nerve injuries remain a challenging problem in need of better treatment strategies.Despite best efforts at surgical reconstruction and postoperative rehabilitation,patients are often left with persistent,debilitating motor and sensory deficits.There are currently no therapeutic strategies proven to enhance the regenerative process in humans.A clinical need exists for the development of technologies to promote nerve regeneration and improve functional outcomes.Recent advances in the fields of tissue engineering and nanotechnology have enabled biomaterial scaffolds to modulate the host response to tissue repair through tailored mechanical,chemical,and conductive cues.New bioengineered approaches have enabled targeted,sustained delivery of protein therapeutics with the capacity to unlock the clinical potential of a myriad of neurotrophic growth factors that have demonstrated promise in enhancing regenerative outcomes.As such,further exploration of combinatory strategies leveraging these technological advances may offer a pathway towards clinically translatable solutions to advance the care of patients with peripheral nerve injuries.This review first presents the various emerging bioengineering strategies that can be applied for the management of nerve gap injuries.We cover the rationale and limitations for their use as an alternative to autografts,focusing on the approaches to increase the number of regenerating axons crossing the repair site,and facilitating their growth towards the distal stump.We also discuss the emerging growth factor-based therapeutic strategies designed to improve functional outcomes in a multimodal fashion,by accelerating axonal growth,improving the distal regenerative environment,and preventing end-organs atrophy.

Atomic insights into heterogeneous nucleation and growth kinetics of Al on TiB_(2) particles in undercooled Al-5Ti-1B melt

Titanium diboride(TiB_(2))is an effective grain refiner of Al alloys in the industry that facilitates casting processes by forming uniformly refined microstructures.Although our understanding of the underlying refinement mechanisms has advanced,the atomic kinetics of heterogeneous nucleation of Al on TiB2 remains unknown.Here,we report atomic-scale observations of the heterogeneous nucleation and growth kinetics of Al on self-formed TiB_(2) particles by in situ heating of undercooled Al-5Ti-1B films.We demonstrate that an ordered Al monolayer forms on the Ti-terminated{0001}TiB_(2) surface;then,the surrounding Al atoms are initiated to form an island-shaped Al nucleus with face-centered cubic{111}stacking without the assistance of a Ti-rich buffer layer.The interfacial lattice mismatch between{111}Al and{0001}TiB_(2) causes remarkable out-of-plane strain that decreases gradually with Al nucleus layers increasing to 6 atomic layers.The elastic strain energy originating from this interfacial strain increases the free energy of the Al/TiB2 heterostructure,hence impeding the rapid growth of the Al nucleus.We found that TiB2 particles stabilize the Al nuclei rather than activating their free growth into grains when the experimental undercoolingΔT is lower than the onset undercoolingΔT fg in Greer's free growth model.Our findings provide an atomic-scale physical image of the heterogeneous nucleation and growth mechanisms of Al with inoculator participation and elucidate the strain-dependent growth kinetics of Al nuclei.

Ultrafast shape change and joining of small-volume materials using nanoscale electrical discharge

Using nanoscale electrical-discharge-induced rapid Joule heating, we developed a method for ultrafast shape change and joining of small-volume materials. Shape change is dominated by surface-tension-driven convection in the transient liquid melt, giving an extremely high strain rate of N106 s-1. In addition, the heat can be dissipated in small volumes within a few microseconds through thermal conduction, quenching the melt back to the solid state with cooling rates up to 108 K.s-1. We demonstrate that this approach can be utilized for the ultrafast welding of small-volume crystalline Mo （a refractory metal） and amorphous Cu49Zr51 without introducing obvious microstructural changes, distinguishing the process from bulk welding.

Congratulations to Academician Changxu Shi on the Occasion of His Winning the 2010 Chinese Science & Technology Grand Prize

On behalf of friends and colleagues at MIT and Johns Hopkins University,as well as students and faculty at the Center for Advancing Materials Performance from the Nanoscale(CAMP-Nano) at Xi'an Jiaotong University,we would like to express our hearty congratulations to Academician Shi on the occasion of his winning the highest science and technology award of China.This very well deserved 'Life Achievement Award' recognizes the brilliant scientific career of Dr.Shi.The entire disciplines of materials science and nanosciences in China should also feel much gratified by this prize,for it was Dr.Shi who had

半导体热电材料BI_(1-X)SB_X薄膜的电化学制备

B I1-XSBX半导体合金是性能优异的热电和磁电功能材料,为制备固态电制冷器件、温差发电器件和磁电器件的重要材料。电化学沉积薄膜技术工艺设备简单成本低,在半导体薄膜制备方面有很好的应用前景。系统研究了高浓度盐酸(2.4 MOL/L)的B I和SB盐酸溶液,成份从纯铋逐步变化到纯锑的B I1-XSBX合金半导体薄膜电化学沉积特性。测试了沉积过程I-V循环曲线和和沉积电荷效率等电化学参数。结果表明在所有成份范围内都可以得到典型的B I1-XSBX固溶体结构的高质量薄膜。薄膜生长为典型的溶液扩散控制过程,具有高的沉积电荷效率。薄膜沉积和溶解之间的电位差随溶液中SB(Ⅲ)离子浓度增加而增大,生长的薄膜越来越稳定。在30%SB浓度附近,电化学过程、薄膜结构和性能发生明显的突变。应用X射线衍射和电子显微镜研究薄膜结构,发现薄膜具有明显的(012)择优取向,薄膜晶粒尺寸也随SB浓度的增加而变化。

Machine learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications

Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability,biocompatibility,and impressive mechanical characteristics.However,their rapid in-vivo degradation presents challenges,notably in upholding mechanical integrity over time.This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy,ZX10.Utilizing rapid,cost-efficient characterization methods like X-ray diffraction and optical microscopy,we swiftly examine microstructural changes post-thermal treatment.Employing Pearson correlation coefficient analysis,we unveil the relationship between microstructural properties and critical targets(properties):hardness and corrosion resistance.Additionally,leveraging the least absolute shrinkage and selection operator(LASSO),we pinpoint the dominant microstructural factors among closely correlated variables.Our findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca_(2)Mg_(6)Zn_(3)phase in corrosion behavior.This suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary phases.This thorough investigation furnishes valuable insights into the intricate interplay of processing,structure,and properties in magnesium alloys,thereby advancing the development of superior biodegradable implant materials.

金属玻璃中程有序结构的FEM技术表征

采用fluctuation electron microscopy(FEM)对Al_(85)Ni_5Y_(10-x)Co_x(x=0,2)和Cu_(46)Zr_(54-x)Al_x(x=0,7)金属玻璃的微观结构进行了表征,研究了合金成分变化对金属玻璃微观结构的影响.结果表明,2种金属玻璃体系中均存在较强的中程有序结构;少量元素的添加提高了合金微观结构的均匀性.金属玻璃中程有序结构均匀性的增加有利于改善合金的玻璃形成能力.

Environmental Analysis with 2D Transition-Metal Dichalcogenide-Based Field-Effect Transistors

Field-effect transistors(FETs)present highly sensitive,rapid,and in situ detection capability in chemical and biological analysis.Recently,two-dimensional(2D)transition-metal dichalcogenides(TMDCs)attract significant attention as FET channel due to their unique structures and outstanding properties.With the booming of studies on TMDC FETs,we aim to give a timely review on TMDCbased FET sensors for environmental analysis in different media.First,theoretical basics on TMDC and FET sensor are introduced.Then,recent advances of TMDC FET sensor for pollutant detection in gaseous and aqueous media are,respectively,discussed.At last,future perspectives and challenges in practical application and commercialization are given for TMDC FET sensors.This article provides an overview on TMDC sensors for a wide variety of analytes with an emphasize on the increasing demand of advanced sensing technologies in environmental analysis.

An index for deformation controllability of small-volume materials

Mechanical tests on small-volume materials show that in addition to the usual attributes of strength and ductility,the controllability of deformation would be crucial for the purpose of precise plastic shaping.In our present work,a"mechanical controllability index"(MCI)has been proposed to assess the controllability of mechanical deformation quantitatively.The index allows quantitative evaluation of the relative fraction of the controllable plastic strain out of the total strain.MCI=0 means completely uncontrollable plastic deformation,MCI=∞means perfectly controllable plastic shaping.The application of the index is demonstrated here by comparing two example cases:0.273 to 0.429 for single crystal Al nanopillars that exhibit obvious strain bursts,versus 3.17 to 4.2 for polycrystalline Al nanopillars of similar size for which the stress-strain curve is smoother.

Nanoscale fluctuation of stacking fault energy strengthens multi-principal element alloys

Chemical randomness and the associated energy fluctuation are essential features of multi-principal ele-ment alloys(MPEAs).Due to these features,nanoscale stacking fault energy(SFE)fluctuation is a natural and independent contribution to strengthening MPEAs.However,existing models for conventional alloys(i.e.,alloys with one principal element)cannot be applied to MPEAs.The extreme values of SFEs required by such models are unknown for MPEAs,which need to calculate the nanoscale volume relevant to the SFE fluctuation.In the present work,we developed an analytic model to evaluate the strengthening ef-fect through the SFE fluctuation,profuse in MPEAs.The model has no adjustable parameters,and all parameters can be determined from experiments and ab initio calculations.This model explains available experimental observations and provides insightful guidance for designing new MPEAs based on the SFE fluctuation.It generally applies to MPEAs in random states and with chemical short-range order.

Closed-loop superconducting materials discovery

Discovery of novel materials is slow but necessary for societal progress.Here,we demonstrate a closed-loop machine learning(ML)approach to rapidly explore a large materials search space,accelerating the intentional discovery of superconducting compounds.By experimentally validating the results of the ML-generated superconductivity predictions and feeding those data back into the ML model to refine,we demonstrate that success rates for superconductor discovery can be more than doubled.Through four closed-loop cycles,we report discovery of a superconductor in the Zr-In-Ni system,re-discovery of five superconductors unknown in the training datasets,and identification of two additional phase diagrams of interest for new superconducting materials.Our work demonstrates the critical role experimental feedback provides in ML-driven discovery,and provides a blueprint for how to accelerate materials progress.

Bioinspired nanocomposites with self-adaptive mechanical properties

Conventional synthetic materials have fixed mechanical properties and suffer defects,damage,and degradation over time.This makes them unable to adapt to changing environments and leads to limited lifecycles.Recently,self-adaptive materials inspired by natural materials have emerged as a solution to address these problems.With the ability to change their mechanical properties based on changing mechanical environments,repairing defects,and maintaining their mechanical properties,these materials can lead to improved performance while decreasing waste.In this review,we explore self-adaptive phenomena found in nature that have inspired the development of synthetic self-adaptive materials,and the mechanisms that have been employed to create the next generation of materials.The potential applications of these materials,the challenges that existing approaches face,and future research opportunities are also discussed.

Machine-learned multi-system surrogate models for materials prediction

Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational cost.We demonstrate surrogate models that simultaneously interpolate energies of different materials on a dataset of 10 binary alloys(AgCu,AlFe,AlMg,AlNi,AlTi,CoNi,CuFe,CuNi,FeV,and NbNi)with 10 different species and all possible fcc,bcc,and hcp structures up to eight atoms in the unit cell,15,950 structures in total.We find that the deviation of prediction errors when increasing the number of simultaneously modeled alloys is<1 meV/atom.Several state-of-the-art materials representations and learning algorithms were found to qualitatively agree on the prediction errors of formation enthalpy with relative errors of<2.5% for all systems.

Computational modeling sheds light on structural evolution in metallic glasses and supercooled liquids

This article presents an overview of three challenging issues that are currently being debated in the community researching on the evolution of amorphous structures in metallic glasses and their parent supercooled liquids.Our emphasis is on the valuable insights acquired in recent computational analyses that have supplemented experimental investigations.The first idea is to use the local structural order developed,and in particular its evolution during undercooling,as a signature indicator to rationalize the experimentally observed temperature-dependence of viscosity,hence suggesting a possible structural origin of liquid fragility.The second issue concerns with the claim that the average nearest-neighbor distance in metallic melts contracts rather than expands upon heating,concurrent with a reduced coordination number.This postulate is,however,based on the shift of the first peak maximum in the pair distribution function and an average bond length determined from nearest neighbors designated using a distance cutoff.These can instead be a result of increasing skewness of the broad first peak,upon thermally exacerbated asymmetric distribution of neighboring atoms activated to shorter and longer distances under the anharmonic interatomic interaction potential.The third topic deals with crystal-like peak positions in the pair distribution function of metallic glasses.These peak locations can be explained using various connection schemes of coordination polyhedra,and found to be present already in high-temperature liquids without hidden crystal order.We also present an outlook to invite more in-depth computational research to fully settle these issues in future,and to establish more robust structure-property relations in amorphous alloys.

In-situ surface transformation of magnesium to protect against oxidation at elevated temperatures

The native oxide thin scale on magnesium(Mg)surface appears continuous and crack-free,but cannot protect the Mg matrix from further oxidation,especially at elevated temperatures.This thermal oxidation process is witnessed in its entirety using a home-made in-situ heating device inside an environmental electron transmission microscope.We proposed,and verified with real-time experimental evidence,that transforming the native oxide scale into a thin continuous surface layer with high vacancy formation energy(low vacancy concentration),for example MgCO3,can effectively protect Mg from high-temperature oxidation and raise the threshold oxidation temperature by at least two hundred degrees.

Non-invasive delivery of levodopa-loaded nanoparticles to the brain via lymphatic vasculature to enhance treatment of Parkinson’s disease

Levodopa(L-DOPA),a precursor of dopamine,is commonly prescribed for the treatment of the Parkinson’s disease(PD).However,oral administration of levodopa results in a high level of homocysteine in the peripheral circulation,thereby elevating the risk of cardiovascular disease,and limiting its clinical application.Here,we report a non-invasive method to deliver levodopa to the brain by delivering L-DOPA-loaded sub-50 nm nanoparticles via brain-lymphatic vasculature.The hydrophilic L-DOPA was successfully encapsulated into nanoparticles of tannic acid(TA)/polyvinyl alcohol(PVA)via hydrogen bonding using the flash nanocomplexation(FNC)process,resulting in a high L-DOPA-loading capacity and uniform size in a scalable manner.Pharmacodynamics analysis in a PD rat model demonstrated that the levels of dopamine and tyrosine hydroxylase,which indicate the dopaminergic neuron functions,were increased by 2-and 4-fold,respectively.Movement disorders and cerebral oxidative stress of the rats were significantly improved.This formulation exhibited a high degree of biocompatibility as evidenced by lack of induced inflammation or other pathological changes in major organs.This antioxidative and drug-delivery platform administered through the brain-lymphatic vasculature shows promise for clinical treatment of the PD.

Fast,accurate,and transferable many-body interatomic potentials by symbolic regression

The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties.In recent years there has been great progress in the use of machine-learning algorithms to develop fast and accurate interatomic potential models,but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales.To address this challenge,we have developed a machine-learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate,computationally efficient many-body potential models.The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy,speed,and simplicity.The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well.Our algorithm was validated by rediscovering an exact Lennard-Jones potential and a Sutton-Chen embedded-atom method potential from training data generated using these models.By using training data generated from density functional theory calculations,we found potential models for elemental copper that are simple,as fast as embedded-atom models,and capable of accurately predicting properties outside of their training set.Our approach requires relatively small sets of training data,making it possible to generate training data using highly accurate methods at a reasonable computational cost.We present our approach,the forms of the discovered models,and assessments of their transferability,accuracy and speed.

In situ study of the mechanical properties of airborne haze particles

Particulate pollution has raised serious concerns regarding its potential impacts on human health in developing countries. However, much less attention has been paid to the threat of haze particles to machinery and industry. By employing a state-of-the-art in situ scanning electron microscope compression testing technique, we demonstrate that iron-rich and fly ash haze particles, which account for nearly 70% of the total micron-sized spherical haze particles, are strong enough to generate abrasive damage to most engineering alloys, and therefore can generate significant scratch damage to moving contacting surfaces in high precision machineries. Our finding calls for preventive measures to protect against haze related threat.

Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications,but predicting the stable structures of clusters can be computationally expensive.In this work,we present a procedure for rapidly predicting low-energy structures of nanoclusters by combining a genetic algorithm with interatomic potentials actively learned on-the-fly.Applying this approach to aluminum clusters with 21 to 55 atoms,we have identified structures with lower energy than any reported in the literature for 25 out of the 35 sizes.Our benchmarks indicate that the active learning procedure accelerated the average search speed by about an order of magnitude relative to genetic algorithm searches using only density functional calculations.This work demonstrates a feasible way to systematically discover stable structures for large nanoclusters and provides insights into the transferability of machine-learned interatomic potentials for nanoclusters.

3D Nanoporous Graphene Based Single-Atom Electrocatalysts for Energy Conversion and Storage

CONSPECTUS:This Account will provide an overview and analysis on recent research of 3D nanoporous graphene based single-atom electrocatalysts for energy conversion and storage applications.In order to meet the increasing energy demands and assist in the transition from a global economy that relies heavily on fossil fuels to one that utilizes more renewable energy sources,there is urgent need to develop highperforming electrocatalysts toward renewable energy related reactions.These catalysts are expected to have low overpotentials,high reaction selectivity,long cycling stability,and,importantly,lower materials costs to address the challenges of traditional nanoparticulate noble metal catalysts.