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6061 aluminum alloys with different aging states were prepared by rotational friction extrusion （RFE）. The effect of RFE on the microstructure and mechanical properties of 6061 aluminum alloys with different aging states was investigated. The results show that the refractory AlFeMnSi phase of 6061 aluminum alloys can be broken by using RFE， resulting in the fine and uniform distribution of the AlFeMnSi phase in the alloy. Under the combined effects of frictional heat and deformation during RFE processing， the pre-precipitated coarse Mg2Si phase is dissolved and re-precipitated， resulting in that the pre-precipitated coarse Mg2Si phase has no effect on the particle stimulated nucleation （PSN） mechanism in the dynamic recrystallization of the material. Therefore， the size of grain exhibits negligible change by RFE. RFE reduces the strength of as-quenched 6061 aluminum alloys， while the strength of 6061 aluminum alloys with a long-term aging state can be increased by RFE. However， there are holes inside the material processed by RFE， which reduce the elongation of the alloys.

With the rapid development of China’s manufacturing industry and the emergence of difficult-to-machine materials， there is an urgent need for cutting technology to continuously update and iterate， and the development of cutting tools has become a key factor in improving processing efficiency. In recent years， coating technology has become an effective way to improve the cutting performance of cutting tools. In particular， TiAlN coating has been widely used in cutting tools due to its good wear resistance and stability. However， the traditional TiAlN coated tools have made it difficult to bear the harsh conditions of thermo-mechanically-chemo multiple coupling. How to form solid solution strengthening and fine grain strengthening by element doping， for improving the cutting performance of TiAlN coated tools conveniently and efficiently， has become a research hotspot in this field. Given this problem， the action mechanism and preparation method of TiAlN coating were summarized， the microstructure and phase structure of the coatings obtained by different methods were analyzed， the limitations of TiAlN coating due to oxidation and phase transformation in high temperature were described， and the effects of doping Si， C， Cr， B and V elements on the microstructure， hardness， wear resistance， oxidation resistance and tool life of TiAlN coating were reviewed. Finally， the future development of TiAlN coated tools is summarized.

At present， most smart wearable devices are smart watches， wristbands， etc.， which have the problems of high rigidity， poor comfort and frequent charging， making it difficult to meet the requirements of ergonomics and clothing comfort. They cannot be worn for a long time to achieve all-weather monitoring. Textile triboelectric nanogenerator （T-TENG） can be integrated into shoes and clothing as flexible power sources and self-powered sensors， making it an ideal wearable device for active human health monitoring and enforcement. However， most of the reported T-TENG need to be packaged and then integrated into clothing before use， resulting in a reduced air permeability of clothing. In addition， most of the current studies are in the laboratory stage， and the properties of T-TENG such as durability， sensitivity and stability in actual use are not fully considered. In this paper， the basic working mode， material selection， manufacturing method， integrated footwear and clothing mode， and application scenarios of T-TENG were reviewed. The preparation methods of T-TENG for nanofiber membrane and textile composite materials， fiber/yarn-based T-TENG， and fabric-based T-TENG were mainly discussed. New strategies for the development and integration of comfortable T-TENG in the future were proposed， including the large-scale preparation of T-TENG， the integration of T-TENG and traditional clothing， the compatibility of T-TENG monitoring accuracy and comfort， and the durability and stability of T-TENG.

Polyacrylonitrile pre-oxidized fiber（PANOF）is an emerging flame retardant material with low cost and high performance，showing a promising application potential as thermal protection materials in the aerospace field.Pretreated PANOF（t-PANOF）was obtained by the process of oil removing with acetone and surface modification using silane coupling agent KH570 in turn.Scanning electron microscopy（SEM），Fourier transform infrared spectroscopy（FT-IR）and X-ray photoelectron spectroscopy（XPS）were employed to figure out the changes of morphology and chemical structure of the fibers before and after pretreatment and their interfacial properties with silicone rubber（SR）.Effects of t-PANOF on the mechanical and thermal performances of SR were further investigated. With the presence of ceramic forming agent（CFA），the ablation responsive behaviors of CFA/t-PANOF/SR ternary composites were researched as well.The results show that the level of interfacial adhesion between fibers and rubber is significantly enhanced contributed from the pretreatment of PANOF，and the tensile strength of the composites is improved accordingly.However，with the increasing content of t-PANOF，the comprehensive mechanical properties of t-PANOF/SR composites show the trend that increase first and then decrease，indicating that the optimal dosage of the fibers is 5 to 10 phr.t-PANOF skeleton helps to not only enhance the dimensional stability of SR in high temperature and oxygen-rich environment，but also improve the ablation performance of CFA/t-PANOF/SR composites.The antioxidation ceramic layer with fiber skeleton as the main structure is formed on the surface of CFA/t-PANOF/SR composites under the high temperature aerobic gas with the heat flux density of 2 MW/m2，the corresponding linear and mass ablation rate are only 0.02 mm/s and 0.02 g/s respectively，which is expected to be utilized as an ablation material to satisfy the demands of the new generation of aerospace vehicles.

Wearable devices have good portability and imperceptibility， realizing expected functions after being worn. Drug delivery refers to delivering drugs into the body with suitable carriers or techniques to generate therapeutic effects to improve the stability and bioavailability of drugs.The application of wearable devices in drug delivery can rationally release drugs when receiving disease signals or user orders and monitor in-vivo drug concentrations， reducing the dependency of doctors or hospitals， and achieving optimal disease treatment timing and outcomes. Wearable drug delivery systems can be worn directly on the body surface and are non-invasive and self-administered. Microneedle-skin patches， wound healing patches， and smart contact lenses are popular wearable devices explored in drug delivery. This review is a summary of the application of wearable devices in disease treatment， such as diabetes， wound healing， eye diseases， cancer， and other diseases， and drug delivery in recent five years， summarizing the challenges for the development of wearable drug delivery systems， and prospecting its development direction.

The surface strengthening treatment of 304 stainless steel was carried out by using ultrasonic nanocrystal surface modification （UNSM） technology， and the effect of UNSM on the microstructure and mechanical performance of stainless steel was studied. The microstructure evolution and plastic deformation mechanism before and after UNSM treatment were analyzed by transmission electron microscopy （TEM）， electron backscattered diffraction （EBSD） and other characterization methods. The effects of UNSM on surface roughness， microhardness and residual stress of materials were studied by using white light interferometry and microhardness testing. The results show that after UNSM treatment， 304 stainless steel undergoes severe plastic deformation on the surface， resulting in an increase in dislocation density and significant grain refinement， and nanocrystals are generated on the near-surface region， forming a hardened layer about 300 μm deep. The deformation induces austenite-to-martensite transformation and introduces the residual compressive stress of about 900 MPa on the surface. The microhardness of the material increases from 238HV to 511HV， and the surface integrity is significantly improved.

The low-cycle fatigue （LCF） behavior of Ti-10Mo-xFe （x=1， 2， 3，mass fraction/%） alloys with different plastic deformation modes were investigated by OM， SEM， EBSD and electro-hydraulic fatigue test machine. The effects of the strain amplitudes （Δεt/2=0.5%， 1.0% and 1.5%） and Fe content on the mechanical response， microstructures and fatigue crack propagation behavior were analyzed. The results show that the low-cycle fatigue performance of the alloys decreases with the increase of strain amplitude and Fe content. The cyclic stress response behaviors generally exhibit an initial cyclic hardening and then tend towards a cyclic stability or slight cyclic softening until fracture. The plastic deformation mode of Ti-10Mo-1Fe alloy is dominated by ｛332｝〈113〉 twinning， and changes to dislocation slip with the increase of Fe content. A few twins are formed in the fatigue initiation region of Ti-10Mo-1Fe alloy under low strain amplitude， and the twin area fraction increases gradually along the crack propagation direction， while a large number of twins are activated near the fracture area under high strain amplitude. The activation and intersection of abundant twins in Ti-10Mo-1Fe alloy divide the grain into network microstructures， which effectively release the stress concentration and delay the initiation of fatigue crack due to the dynamic microstructure refinement effect. At the same time， the abundant twin boundaries significantly extend the fatigue crack propagation path because the micro-crack deflects along the twin boundaries.

Chitosan hydrogel with excellent degradability and biocompatibility has become an important material for constructing flexible strain sensors. Flexible strain sensors based on chitosan conductive hydrogels have superb environmental adaptability and are widely used in biomedical fields such as health monitoring and implantable devices. The preparation method and conductive mechanism of chitosan-based conductive hydrogels were reviewed. The current status of chitosan-based conductive hydrogels in low temperature resistant， self-repairing， and self-adhesive functional flexible strain sensors was summarized. Finally， it is pointed out that the optimization of the preparation process， the application of new materials， and the use of artificial intelligence are the key research directions for the future of chitosan-based conductive hydrogels for flexible strain sensors， aiming to provide theoretical foundations and practical guidance for the further development of multifunctional applications of flexible strain sensors.

Copper matrix composites reinforced with different mass fractions of TiC particles were prepared by spark plasma sintering （SPS） technology. The microstructures of the copper matrix composites with different sintering temperatures and TiC contents were investigated. The effects of TiC content on the electrical conductivity， nano-mechanical properties， microhardness， tensile properties， and tribological behaviors of the composites were studied. Results show that the TiC/Cu composites obtained by sintering at 850 ℃ have a dense structure， with a good bonding between the reinforcements and the matrix. On the one hand， as the TiC content increases， the grains of the copper matrix phase in the TiC/Cu composites are continuously refined and the grain boundary density increases. This leads to an increase in the hardness and yield strength of the composites. On the other hand， the increase in TiC content gradually leads to the development of pores and cracks at the bonding interface between the reinforcement and matrix. When bearing or transmitting load， the pores and cracks will become the preferred location for stress concentration and crack formation， resulting in a reduction in the conductivity， tensile strength， and elongation of the composite. With TiC content of 5%-15%（mass fraction，the same below）， the composites have higher tensile strength， modulus of elasticity and yield strength， appropriate hardness， and better overall mechanical properties. When the TiC content exceeds 20%， the yield strength of TiC/Cu composites decreases significantly and the composite fracture mechanism changes from ductile fracture to brittle fracture. The TiC reinforcements significantly improve the tribological properties of the copper matrix composites. The friction coefficient and wear of the composites during friction increase linearly with increasing load and tend to decrease with increasing TiC content. At a TiC content of 20%， the composite has the highest wear resistance and lowest coefficient of friction. The wear mechanism is dominated by abrasive wear， accompanied by slight adhesive wear and oxidative wear.

Wearable materials and devices are developing toward flexibility， lightness， imperceptibility， intelligence， and long-term wearability to meet the personalized needs such as physiological and psychological demands of human body. This trend brought innovation to the field of sports and health monitoring， receiving extensive attention from the academic and industrial communities. However， while meeting the individualized development needs of the human body， wearable flexible materials and devices also face challenges in terms of mechanical robustness， signal stability， soft-hard interface connection， and biocompatibility. Therefore， this review aims to discuss the materials， structures， and fabrication processes for constructing wearable flexible materials and devices from the perspective of practical sports and health monitoring needs，and proposes the major challenging factors and their solutions in terms of mechanical， electrical， and biological performance. Finally， this paper predicts the future development directions of wearable flexible electronic materials and devices， including fully flexible integration， enhanced mechanical robustness， high-precision signal decoupling and recognition， stability，and sensitivity of monitoring， rapid responsiveness， ultra-thin imperceptible design， multimodal signal processing， as well as intelligent adaptive feedback.

The applications of wearable sensors in sports， medicine， rehabilitation， and other fields， have greatly facilitated the capture and monitoring of human movement index signals effectively avoiding sports injuries， reducing the frequency of medical treatment， and even saving many lives. With the application and popularization of wearable sensors， suitable flexible energy supply systems are the key to its development. In recent years， researchers have studied and designed a variety of flexible energy supply systems based on different energy release methods， among which flexible Zn-ion batteries stand out due to their high energy density， high elastic modulus， high cycle stability， and high safety. We reviewed the research progress in flexible Zn-ion batteries for wearable sensors， mainly introducing and summarizing the batteries components （such as current collector， electrode （cathode and anode）， separator， electrolyte， and packaging） and the application of wearable sensors. Finally， the current problems and challenges of flexible Zn-ion batteries are discussed.

Electronic skin is a novel type of flexible and wearable sensor that mimics human skin perception， exhibiting characteristics such as lightweight， softness， and flexibility. It has the capability to convert external stimuli into diverse output signals， showing substantial potential in health monitoring and human-computer interaction. This article provides a comprehensive review from the perspective of intelligent materials for constructing electronic skin， focusing on commonly used substrates， conductive fillers， and their geometric structures. It discusses the requirements for biocompatibility， adhesion， self-healing， and self-powering performance of electronic skin based on the complex environmental conditions it faces in applications. It points out that in the research process， there are still issues such as poor comprehensive perception of human skin， complex and expensive fabrication processes， and delayed response to sensory signals. By optimizing materials and structures to enhance the basic performance of electronic skin， the development trend is to build outstanding performance， multifunctionality， and simultaneous detection of various external stimuli. Electronic skin shows great potential in medical diagnosis， soft robotics， smart prosthetics， and human-machine interaction.

Aiming at the new control method of filling unbalance in the flow channel by using convection mixing， the influence of micro-scale polymer melt flow factors on the control characteristics of melt temperature distribution in the flow channel was studied by numerical simulation. Taking the "H" flow channel as the research object， the convection mixing device was arranged in the flow channel. The effects of melt temperature， convective heat transfer coefficient and flow channel size on melt filling unbalance were studied by single factor experiment. The numerical simulation results show that， the increase in melt temperature is accompanied by the decrease of melt self-viscosity under adiabatic boundary conditions. The temperature difference between symmetric points decreases， which improves the filling imbalance. Under the convective heat transfer boundary， the increase of melt temperature increases the heat loss of melt through the wall， and the increase of temperature difference between symmetric points leads to the more obvious filling imbalance. The lower the convective heat transfer coefficient， the lower the thermal conductivity efficiency between melt and wall， and the higher the average melt temperature， which increases the temperature difference between symmetric points and intensifies the filling imbalance. With the decrease in the size of the flow path， the micro-scale effect is weakened， the temperature difference between symmetric points can be reduced， and the phenomenon of filling imbalance is better improved. The convective heat transfer coefficient has the greatest influence on the filling unbalance， followed by the flow channel size， and finally the melt temperature.

The hot deformation behavior and dynamic recrystallization of Al-11.1Zn-2.3Mg-2.0Cu-0.05Sc alloy was explored in the isothermal hot compression test with a temperature range of 653-733 K at strain rate of 10-3 s-1. The microstructural evolution of the studied alloy at different temperatures was analyzed by EBSD technology. The dynamic recrystallization（DRX） critical strain， critical stress， fraction， and average grain size equations were established by linear regression. The results show that the fitted equations can accurately describe the DRX characteristics of the studied alloy during hot deformation.The increase in the deformation temperature enlarges the misorientation angle inside the grains， which facilitates the DRX progress. The distributions of crystal orientation tend to be more homogeneous with the increase in temperatures. Moreover， dynamic recovery is the dominant dynamic softening mechanism. With the temperature change， there are three types of recrystallization mechanisms in the microstructure of aluminum alloy， including discontinuous dynamic recrystallization， continuous dynamic recrystallization， and geometric dynamic recrystallization.

To enhance the reliability of Ti2AlNb alloys as an aerospace engine component in marine environments， the hot salt corrosion behavior of Ti2AlNb alloy with nominal compositions of Ti-22Al-23Nb-1Mo-1Zr（atom fraction/%） coated with Na2SO4/NaCl（95/5，mass ratio）at temperatures of 550， 650 ℃ and 750 ℃ was investigated.The corrosion mass gain curves of alloys at different temperatures were drawn. The surface morphologies， cross-section structures， and corrosion products of the alloy after hot salt corrosion were examined and analyzed by X-ray diffraction（XRD）， scanning electron microscopy（SEM）， and energy dispersive X-ray spectroscopy（EDS）. The results show that the corrosion rate at 550 ℃ is lower and the oxide scales are straight， dense and intact， exhibiting excellent resistance to hot salt corrosion. The corrosion mass gain curves of the alloy follow a parabolic law at 650℃ and the oxide scales exhibit small-scale peeling and internal cracks， with a slightly layered structure. The alloy exhibits good resistance to hot salt corrosion at 650 ℃ in general. A significant mass loss is observed during the corrosion process at 750 ℃， with the oxide scales cracking and peeling severely. Furthermore， within the oxide scales emerges a distinct layered structure where two layers of corrosion products alternately exist： the main layer is mainly composed of TiO2 and contains various corrosion products including NbAlO4， while the other layer is mainly composed of NaNbO3. The resistance to hot salt corrosion of the alloy decreases seriously at 750 ℃.

Flexible pressure sensors can be attached to the human skin to sense external pressure signals， and have the characteristics of wide sensing range， short response time， high sensitivity， and durability. Therefore， they are widely used in the fields of electronic skin and human-computer interaction. Flexible pressure sensors are usually composed of flexible substrates， active materials and conductive electrodes. Among them， one or more of the active materials form a sensing material by compounding with a flexible substrate， and its deformation under external excitation will cause changes in parameters such as resistance， thereby achieving sensing function.In addition， by introducing microstructure， the compressibility and sensitivity to small pressure of the sensing material can be increased， and its sensing performance can be improved. In this paper， the research progress in flexible pressure sensors doped with carbon-based， metal-based， and black phosphorus-based active materials on film and fabric substrates was reviewed. The preparation methods， electromechanical properties and application scenarios of different sensors were discussed， and the advantages and disadvantages of various sensors were summarized. On this basis， the research on how to achieve wide-range pressure detection， commercialization， non-toxicity of the production process and long-term biocompatibility experiments of smart wearable flexible pressure sensors in the future is prospected.

Nano-TiCp/GH3536 composite powders were prepared by mechanical alloying （MA） and thermal plasma spheroidization （TPS） methods. The flowability， bulk density， particle size distribution， sphericity， and microstructure of the composite powders before and after MA-TPS treatment were studied by Hall flow meter，Scott volumeter， XRD， particle size analyzer， scanning electron microscope， and transmission electron microscope. The results show that the preformed powders obtained by MA-5 h treatment are irregular shape powders with non-uniform adhesion of nano-TiC particles on the surface of the GH3536 substrate. Compared with the original GH3536 powders， the number of equiaxed grains in the preformed powders increases and the crystal grains are refined， but the flowability and bulk density decrease.The nano-TiCp/GH3536 composite powders obtained by the second TPS treatment are spherical， and the original irregular nano-TiC particles are spheroidized and uniformly distributed inside the GH3536 substrate. After removing the submicron particles with an average particle size of 183.27 nm， the flowability， bulk density， and sphericity of TPS composite powders are further improved.The TEM images of submicron particles show that the nanoscale GH3536 microspheres are uniformly coated with a layer of TiC， the structure is similar to the core-shell， and spherical TiC particles are found in the powder. The average spherical degree， average particle size， flowability， and bulk density of the nano-TiCp/GH3536 composite powders prepared by the two-step method are 0.965，33.65 μm，17.1 s/50 g，and 4.184 g/cm3， respectively.

Triboelectrification （TE） is a physical phenomenon that exists on the surface of almost all materials， and the TE of semiconductor materials is different from the electrification of dielectric materials.At the dynamic interface between one semiconductor and another semiconductor or a metal， mechanical friction causes the continuous breaking and rebuilding of chemical bonding between atoms at the interfaces， which could release a quantized energy that is the binding energy of two atoms （or called as bindington） to excite non-equilibrium electron-hole pairs.These electrons and holes can be separated by the built-in electric field formed at the p-n junctions （or Schottky junction， semiconductor heterojunction）， so as to output direct-current （DC） electricity in the external circuits. This phenomenon is defined as the tribovoltaic effect. It is similar to the photovoltaic effect， but the difference is the exciting energy sources. In the tribovoltaic effect， electron-hole pairs are excited by the energy released by the instantaneous transition of atoms at the interface or the energy released when new bonds are formed at the interface； while the photovoltaic effect is excited by photo energy.This article reviews the research progress of DC nanogenerators based on the tribovoltaic effect， including mechanism research， material and device design， surface modification enhancement strategies， etc. We also discuss the possible applications of tribovoltaic devices as flexible DC power sources for wearable electronics， with emphases on their device design， performance optimization，and potential application scenarios.

To improve the proton conductivity of the sulfonated polyether ether ketone/ionic liquid （SPEEK/IL） membrane and reduce the loss rate of IL in the membrane， Cd@Co-MOF-74/Phosphate-4-phenylimidazole ionic liquid （［IM2］［H2PO4］）/SPEEK ternary composite membranes were prepared by the solution casting method. Results show that due to the formation of hydrogen bonds between the imidazole ring in ionic liquids and the —OH or carboxylic acid groups in MOF， imidazole molecules are anchored on the pore walls of MOF. The proton conductivity of the ternary composite membrane at 120 ℃ is the highest， reaching 26.93 mS·cm-1， when the mass fraction of the Cd@Co-MOF-74 doping is 1.5%. The IL loss rate of bimetallic MOF/IL/SPEEK ternary composite membranes with different contents is generally between 20% and 25%. Doping bimetallic MOFs in SPEEK/IL membranes ensures high proton conductivity while reducing the swelling rate of SPEEK/IL composite membranes， thereby increasing their service life.

Achieving the best wideband stealth performance under the limited weight constraint is the goal pursued in stealth aircraft radar absorbing coating（RAC） application schemes design. To meet the need for the integrated application of various kinds of RACs on stealth aircraft， an automatic optimization method for the application programme of RAC was established. Firstly， the electromagnetic fields of the model under different frequencies， azimuths and polarizations were simulated based on the finite difference time domain method. The total electromagnetic field was obtained by normalization and weighted calculation of the electromagnetic fields.Then， the total electromagnetic field was divided according to the specific direction and quantity. Each partition was loaded with the corresponding RAC. The reflection coefficient of the RAC was calculated by the transfer matrix method to superimpose the effect of the RAC on the electromagnetic field. The near-far field transformation was performed on the total electromagnetic field after loading with RAC. In this way， fast computation of radar cross section （RCS） was implemented. Based on the improved simplex method and Karush-Kuhn-Tucker condition， an automatic optimization method is established. The objective function was reducing RCS of the target or reducing weight of the absorbing coating. Finally， taking traveling wave plate as an example， the application plan of three thicknesses of RACs was optimized. The goal of optimization was to reduce total weight of the coating. Compared with the 1.5 mm coating plan， the optimization program can reduce the weight by 50%， with the RCS of traveling wave plate increasing by on more than 1 dB. The optimization effect is remarkable.

Abstract It’s imperative to create a more ideal biological scaffold for bone defect repair. Calcium phosphate bone cements (CPC) could be used as a scaffold. Some ingredients and osteogenic factors could be added to improve its poor mechanical properties and biological activity. As a macromolecule extracted from traditional Chinese medicine, Hedysarum polysaccharides (HPS) would significantly promote the osteogenic activity of bone biomaterials. Zirconium oxide and starch were added to the solid phase and citric acid was added to the liquid phase to optimize CPC. HPS was loaded onto the scaffold as an osteogenic factor, and the prepared CPS + HPS was characterized. Further, the cytocompatibility of CPS + HPS was assessed according to activity, differentiation, and calcification in neonatal rat calvarial osteoblasts, and the biosafety of CPS + HPS was evaluated according to acute toxicity, pyrogen, sensitization, and hemolysis. The success of CPS + HPS in repairing bone defects was evaluated by using a rabbit femur implantation experiment. After optimization, CPS-20-CA-5 containing 10% starch and 5% citric acid displayed the highest mechanical strength of 28.96 ± 0.03 MPa. HPS-50 was demonstrated to exert the best osteogenic effect. The combination of CPS + HPS achieved HPS-loaded CPC. Material characterization, cytocompatibility, biosafety, and femoral implantation experiments indicated that CPS + HPS possessed better pressure resistance and improved osteogenic ability in bone defect repair.CPS + HPS demonstrated effective pressure resistance and superior osteogenic ability, which may be of great significance for bone defects and bone tissue engineering to promote bone regeneration and repair. Graphical Abstract The composite CPS loaded with HPS-50 is an excellent scaffold with fine load-bearing ability and sustained release of osteogenic factors to repair bone defects.

Abstract The human head can sometimes experience impact loads that result in skull fractures or other injuries, leading to the need for a craniectomy. Cranioplasty is a procedure that involves replacing the removed portion with either autologous bone or alloplastic material. While titanium has traditionally been the preferred material for cranial implants due to its excellent properties and biocompatibility, its limitations have prompted the search for alternative materials. This research aimed to explore alternative materials to titanium for cranial implants in order to address the limitations of titanium implants and improve the performance of the cranioplasty process. A 3D model of a defective skull was reconstructed with a cranial implant, and the implant was simulated using various stiff and soft materials (such as alumina, zirconia, hydroxyapatite, zirconia-reinforced PMMA, and PMMA) as alternatives to titanium under 2000N impact forces. Alumina and zirconia implants were found to reduce stresses and strains on the skull and brain compared to titanium implants. However, PMMA implants showed potential for causing skull damage under current loading conditions. Additionally, PMMA and hydroxyapatite implants were prone to fracture. Despite these findings, none of the implants exceeded the limits for tensile and compressive stresses and strains on the brain. Zirconia-reinforced PMMA implants were also shown to reduce stresses and strains on the skull and brain compared to PMMA implants. Alumina and zirconia show promise as alternatives to titanium for the production of cranial implants. The use of alternative implant materials to titanium has the potential to enhance the success of cranial reconstruction by overcoming the limitations associated with titanium implants. Graphical Abstract

Abstract The objective of the present study was to develop a novel molybdenum disulfide/iron oxide/gold nanorods (MoS2/Fe3O4/GNR) nanocomposite (MFG) with different concentrations of AgNO3 solution (MFG1, MFG2, and MFG3) for topical doxorubicin (DOX) drug delivery. Then, these nanocomposites were synthesized and characterized by Fourier transform infrared (FTIR), Transmission electron microscopy (TEM), Dynamic light scattering (DLS), and Ultraviolet-visible (UV–Vis) spectroscopies to confirm their structural and optical properties. Cytotoxicity of samples on Hela cell was determined using MTT assay. Results indicated that nanocomposites possess little cytotoxicity without NIR laser irradiation. Also, the relative viabilities of Hela cells decreased when the concentration of AgNO3 solution increased in this nanocomposite. Using NIR irradiation, the relative viabilities of Hela cells decreased when the concentration of samples increased. Acridine orange/propidium iodide (PI) staining, flow cytometry were recruited to evaluate the effect of these nanocomposites on apoptosis of Hela cells. Finally, results revealed when DOX loading increased in nanocomposite, then cell viability was decreased in it. Therefore, these properties make MFG3 nanocomposite a good candidate for photothermal therapy and drug loading. Graphical Abstract

Abstract Diamond-like Carbon (DLC) has been used as a coating material of choice for a variety of technological applications owing to its favorable bio-tribo-thermo-mechanical characteristics. Here, the possibility of bringing DLC into orthopedic joint implants is examined. With ever increasing number of patients suffering from osteoarthritis as well as with the ingress of the osteoarthritic joints’ malaise into younger and more active demographics, there is a pressing need to augment the performance and integrity of conventional total joint replacements (TJRs). Contemporary joint replacement devices use metal-on-polymer articulations to restore function to worn, damaged or diseased cartilage. The wear of polymeric components has been addressed using crosslinking and antioxidants; however, in the context of the metallic components, complications pertaining to corrosion and metal ion release inside the body still persist. Through this review article, we explore the use of DLC coatings on metallic bearing surfaces and elucidate why this technology might be a viable solution for ongoing electrochemical challenges in orthopedics. The different characteristics of DLC coatings and their feasibility in TJRs are examined through assessment of tribo-material characterization methods. A holistic characterization of the coating-substrate interface and the wear performance of such systems are discussed. As with all biomaterials used in TJRs, we need mindful consideration of potential in-vivo challenges. We present a few caveats for DLC coatings including delamination, hydrophobicity, and other conflicting as well as outdating findings in the literature. We recommend prudently exploring DLC films as potential coatings on metallic TJR components to solve the problems pertaining to wear, metal ion release, and corrosion. Ultimately, we advise bringing DLC into clinical use only after addressing all challenges and concerns outlined in this article. Graphical Abstract

Abstract Full-Heusler alloys with earth-abundant elements exhibit high mechanical strength and favorable electrical transport behavior, but their high intrinsic lattice thermal conductivity limits potential thermoelectric application. Here, the thermoelectric transport properties of Fe-based Full-Heusler Fe2MAl (M = V, Nb, Ta) alloys are comprehensively investigated utilizing density functional theory. The results suggest that Fe2NbAl exhibits exceptionally low lattice thermal conductivity due to low phonon velocities and weakly bound Nb atoms. In Fe2NbAl, the underbonding of the Nb atoms leads large Grüneisen parameters and high anharmonic scattering rates of low-frequency acoustic phonon. Meanwhile, the high band degeneracy and large electrical conductivity lead to a maximum p-type power factor of 255.6 μW·K−2·cm−1 at 900 K. The combination of low lattice thermal conductivity and favorable electrical transport properties leads a maximum p-type dimensionless figure of merit of 1.7. Our work indicates Fe2NbAl, as a low-cost, environmentally friendly, is a potential high-performance p-type thermoelectric material.

Abstract The recently discovered high-T c superconductor La3Ni2O7 has sparked renewed interest in unconventional superconductivity. Here we study superconductivity in pressurized La3Ni2O7 based on a bilayer two-orbital t−J model, using the renormalized mean-field theory. Our results reveal a robust s ±-wave pairing driven by the inter-layer $${d}_{{z}^{2}}$$ d z 2 magnetic coupling, which exhibits a transition temperature within the same order of magnitude as the experimentally observed T c ~ 80 K. We establish a comprehensive superconducting phase diagram in the doping plane. Notably, the La3Ni2O7 under pressure is found to be situated roughly in the optimal doping regime of the phase diagram. When the $${d}_{{x}^{2}-{y}^{2}}$$ d x 2 − y 2 orbital becomes close to half-filling, d-wave and d + i s pairing can emerge from the system. We discuss the interplay between Fermi surface topology and different pairing symmetries. The stability of the s ±-wave pairing against Hund’s coupling and other magnetic exchange couplings is discussed.

Abstract We present the evolution of the electronic structure of CrSBr from its antiferromagnetic ground state to the paramagnetic phase above T N = 132 K, in both experiment and theory. Low-temperature angle-resolved photoemission spectroscopy (ARPES) results are obtained using a novel method to overcome sample charging issues, revealing quasi-2D valence bands in the ground state. The results are very well reproduced by our $${\rm{QSG}}\hat{{\rm{W}}}$$ QSG W ̂ calculations, which further identify certain bands at the X points to be exchange-split pairs of states with mainly Br and S character. By tracing band positions as a function of temperature, we show the splitting disappears above T N . The energy splitting is interpreted as an effective exchange splitting in individual layers in which the Cr moments all align, within the so-called A-type antiferromagnetic arrangement. Our results lay firm foundations for the interpretation of the many other intriguing physical and optical properties of CrSBr.

Abstract The transfer of two-dimensional materials from their growth substrate onto application wafers is a critical bottleneck in scaling-up devices based on such nanomaterials. Here, we present an innovative approach to achieve the automated and simultaneous transfer of arrays of graphene ribbons, with precise control over their orientation and alignment onto patterned wafers. The transfer is performed in a simple, yet efficient apparatus consisting of an array of glass columns, strategically shaped to control ribbon orientation and arranged to match the destination wafer, coupled to a dual inflow/outflow pumping system. This apparatus enables the transfer of a custom array of parallel graphene ribbons in a fraction of the time required with traditional methods. The quality of the transferred graphene was evaluated using optical imaging, scanning electron microscopy, hyperspectral Raman imaging, and electrical transport: all consistently indicating that the transferred graphene exhibits excellent quality, comparable to a manual transfer by an expert user. The proposed apparatus offers several competitive advantages, including ease of use, high transfer throughput, and reduced nanomaterial consumption. Moreover, it can be used repeatedly on the same wafer to assemble arrays of overlayed materials with controlled relative orientations. This approach thus opens promising opportunities for the large-scale fabrication of various heterostructures and devices based on vertical assemblies of 2D nanomaterials.

Abstract Radiotherapy (RT) is a widely used treatment with strong therapeutic effects, but overcoming challenges related to hypoxia-induced tumor resistance and ineffective antitumor immune responses is crucial for optimal outcomes. In this study, we developed a versatile nanosystem using mesoporous silica nanoparticles (MSNs), R837, and a small quantity of manganese peroxide (Mn/ZnO2). The synthesized MSN@R837-Mn/ZnO2 nanoparticles exhibited precise tumor targeting and accumulation, controlled drug release under acidic conditions, and increased sensitivity in magnetic resonance imaging. These attributes collectively augmented the therapeutic efficacy of RT by alleviating hypoxia and immunosuppression. Tumor cells treated with RT combined with these nanoparticles displayed reduced oxidative stress, alleviated hypoxia, and normalized blood vessel formation. Notably, all mice in the RT + PD-1 + MSN@R837-Mn/ZnO2 group achieved complete tumor regression with extended survival. Safety assessments confirmed the absence of MSN@R837-Mn/ZnO2 toxicity, highlighting its potential as a promising approach with dual functionality for the diagnostic imaging and treatment of cancer.

Abstract It is a time-consuming and costly process to develop affordable and high-performance organic photovoltaic materials. Computational methods are essential for accelerating the material discovery process by predicting power conversion efficiencies (PCE). In this study, we propose a deep learning-based framework (DeepAcceptor) to design and discover highly efficient small molecule acceptor materials. Specifically, an experimental dataset is constructed by collecting acceptor data from publications. Then, a deep learning-based model is customized to predict PCEs by applying graph representation learning to Bidirectional Encoder Representations from Transformers (BERT), with the atom, bond, and connection information in acceptor molecular structures as the input (abcBERT). The computational dataset derived from density functional theory (DFT) calculations and the experimental dataset from literature are used to pre-train and fine-tune the model, respectively. The abcBERT model outperforms other state-of-the-art models for the PCE prediction with MAE = 1.78 and R 2 = 0.67 on the test set. A molecular generation and screening process is built to find new high-performance acceptors for PM6. Three discovered candidates are further validated by experiment, and the best PCE reaches 14.61%. The released user-friendly interface of DeepAcceptor greatly boosts the accessibility and efficiency of designing and discovering high-performance acceptors. Altogether, the DeepAcceptor framework with abcBERT is promising to predict the PCE and accelerate the discovery of high-performance acceptor materials.

Abstract Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new spaces. To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. Additionally, this multi-task scheme capitalizes on known physics and interrelated properties, such as gas diffusivity and solubility, both of which are closely tied to permeability. By amalgamating high throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration, with markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. This strategy underscores the potential of coupling high-throughput classical simulations with data fusion methodologies to yield state-of-the-art property predictors, especially when experimental data for targeted properties is scarce.

Abstract An ab-initio computational methodology for interrogating the phonon contribution to polaron formation in real materials is developed that can be directly compared to experiment. Using LiF as an example, we show that the recent ab-initio theory of Sio et al.1 makes predictions of the momentum- and branch dependent phonon amplitudes in polaron quasiparticles that are testable using ultrafast electron diffuse scattering (UEDS) and related techniques. The large electron polaron in LiF has UEDS signatures that are qualitatively similar to those expected from a simple isotropic strain field model, but the small hole polaron exhibits a profoundly anisotropic UEDS pattern that is in poor agreement with an isotropic strain field. We also show that these polaron diffuse scattering signatures are directly emblematic of the underlying polaron wavefunction. The combination of new time and momentum resolved experimental probes of nonequilibrium phonons with novel computational methods promises to complement the qualitative results obtained via model Hamiltonians with a first principles, material-specific quantitative understanding of polarons and their properties.

Abstract Point defect qubits in semiconductors have demonstrated their outstanding capabilities for high spatial resolution sensing generating broad multidisciplinary interest. Hexagonal boron nitride (hBN) hosting point defect qubits have recently opened up new horizons for quantum sensing by implementing sensing foils. The sensitivity of point defect sensors in hBN is currently limited by the linewidth of the magnetic resonance signal, which is broadened due to strong hyperfine couplings. Here, we report on a vacancy-related spin qubit with an inherently low symmetry configuration, the VB2 center, giving rise to a reduced magnetic resonance linewidth at zero magnetic fields. The VB2 center is also equipped with a classical memory that can be utilized for storing population information. Using scanning transmission electron microscopy imaging, we confirm the existence of the VB2 configuration in free-standing monolayer hBN.

Abstract Two-dimensional materials possessing intrinsic multiferroic properties have long been sought to harness the magnetoelectric coupling in nanoelectronic devices. Here, we report the achievement of robust type I multiferroic order in single-layer chromium trihalides by decorating transition metal atoms. The out-of-plane ferroelectric polarization exhibits strong atomic selectivity, where 12 of 84 single-layer transition metal-based multiferroic materials possess out-of-plane ferroelectric or antiferroelectric polarization. Group theory reveals that this phenomenon is strongly dependent on p–d coupling and crystal field splitting. Cu decoration enhances the intrinsic ferromagnetism of trihalides and increases the ferromagnetic transition temperature. Moreover, both ferroelectric and antiferroelectric phases are obtained, providing opportunities for electrical control of magnetism and energy storage and conversion applications. Furthermore, the transport properties of Cu(CrBr3)2 devices are calculated based on the non-equilibrium Green’s function, and the results demonstrate outstanding spin-filtering properties and a low-bias negative differential resistance (NDR) effect for low power consumption.

Abstract The multi-tier G W+EDMFT scheme is an ab-initio method for calculating the electronic structure of correlated materials. While the approach is free from ad-hoc parameters, it requires a selection of appropriate energy windows for describing low-energy and strongly correlated physics. In this study, we test the consistency of the multi-tier description by considering different low-energy windows for a series of cubic SrXO3 (X = V, Cr, Mn) perovskites. Specifically, we compare the 3-orbital t 2g model, the 5-orbital t 2g + e g model, the 12-orbital t 2g + O p model, and (in the case of SrVO3) the 14-orbital t 2g + e g + O p model and compare the results to available photoemission and X-ray absorption measurements. The multi-tier method yields consistent results for the t 2g and t 2g + e g low-energy windows, while the models with O p states produce stronger correlation effects and mostly agree well with experiment, especially in the unoccupied part of the spectrum. We also discuss the consistency between the fermionic and bosonic spectral functions and the physical origin of satellite features, and present momentum-resolved charge susceptibilities.

Abstract The second law of thermodynamics asserts that energy diffuses from hot to cold. The resulting temperature gradients drive the efficiencies and failures in a plethora of technologies. However, as the dimensionalities of materials shrink to the nanoscale regime, proper heat dissipation strategies become more challenging since the mean free paths of phonons become larger than the characteristic length scales. This leads to temperature gradients that are dependent on interfaces and boundaries, which ultimately can lead to severe thermal bottlenecks. Herein, we uncover a phenomenon which we refer to as ‘phonon funneling’, that allows the control of phonon transport to preferentially direct phonon energy away from geometrically confined interfacial thermal bottlenecks and into localized colder regions. This phenomenon supersedes heat diffusion based on the macroscale temperature gradients, thus introducing a nanoscale regime in which boundary scattering increases the phonon thermal conductivity of thin films, an opposite effect than what is traditionally realized. This work advances the fundamental understanding of phonon transport at the nanoscale and the role of efficient scattering methods for enhancing thermal transport.

Abstract Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenomena from the data, such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs. Several case studies of VAE applications for toy models and experimental datasets in Scanning Transmission Electron Microscopy are discussed, emphasizing the deep connection between VAE and basic physical principles. Python codes and datasets discussed in this article are available at https://github.com/saimani5/VAE-tutorials and can be used by researchers as an application guide when applying these to their own datasets.

Abstract Tungsten-bronze-type material Ba6-3x RE 8+2x Ti18O54, (RE = rare earth elements) is an important microwave dielectric that has shown great promises for future miniaturization of microwave devices because of its high dielectric constant, low loss, and tunabilities, and there is still much room for improvement. With their proven predictive power, first-principles calculations may greatly help accelerate materials optimization by reducing or eliminating the expensive and time-consuming experimental trial-and-error process. However, microwave dielectrics such as the tungsten-bronze-type materials are rather complex systems with unit cells containing hundreds or thousands of atoms, making ab initio calculations prohibitively expensive. In this work, we propose an elemental-unit decomposition (EUD) technique that can drastically reduce the computational effort of predicting the properties of complex microwave dielectrics and demonstrate its accuracy and efficiency. Our approach facilitates first-principles prediction and design of complex microwave dielectric materials that would otherwise be extremely difficult.

Abstract Recent advancements in machine learning (ML) have revolutionized the field of high-performance materials design. However, developing robust ML models to decipher intricate structure-property relationships in materials remains challenging, primarily due to the limited availability of labeled datasets with well-characterized crystal structures. This is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic symmetry. We introduce a self-supervised probabilistic model (SSPM) that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures, utilizing solely the existing crystal structure data from materials databases. SSPM significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal structure. We showcase SSPM’s capability by discovering shape memory alloys (SMAs). Amongst the top 50 predictions, 23 have been confirmed as SMAs either experimentally or theoretically, and a previously unknown SMA candidate, MgAu, has been identified.

Abstract The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

Abstract We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to describe a crystal by more than an order of magnitude compared to the full crystal graph representation. We construct machine learning models using the crystal graph singular value representations in order to predict the volume, enthalpy per atom, and metal versus semiconductor/insulator phase of DFT-relaxed organic salt crystals based on randomly generated unrelaxed crystal structures. Initial base models are trained to relate 89,949 randomly generated structures of salts formed by varying ratios of 1,3,5-triazine and HCl with the corresponding volumes, enthalpies per atom, and phase of the DFT-relaxed structures. We further demonstrate that the base model is able to be extended to related chemical systems (isomers, pyridine, thiophene and piperidine) with the inclusion of 2000 to 10,000 crystal structures from the additional system. After training a single model with a large number of data points, extension can be done at significantly lower cost. The constructed machine learning models can be used to rapidly screen large sets of randomly generated organic salt crystal structures and efficiently downselect the structures most likely to be experimentally realizable. The models can be used as a stand-alone crystal structure predictor, but may serve CSP efforts best as a filtering step in more sophisticated workflows.

Abstract The Mott insulator-to-metal transition (IMT) driven by electron correlations has been among the main research topics in materials science over the past decades. The complex interplay between electronic and lattice degrees of freedom leads to various transition scenarios. Of particular interest may be the case of a transition involving the formation of complex phases comprising regions that differ significantly in their physical properties within the same material. Here, we present the results that advance the understanding of the IMT phenomenon, offering the documentation of a pure site-selective mechanism that is not complicated by any structural and spin transformation. Combining XRD, resistivity, Mössbauer and Raman spectroscopy measurements, we provide evidence for a pure pressure-induced Mott transition in α-LiFe5O8, characterized by site-selective delocalization of electrons, leading to the formation, above ~65 GPa, of a site-selective Mott phase consisting of metallic and insulating sublattices. We note that the electron delocalization in the partially disordered octahedral sublattice cannot be understood purely in terms of a Mott transition, the Anderson-Mott transition picture seems more adequate.

Abstract Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a “magnetism-hidden” machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from spin-polarized DFT calculations. The DP-Ni model exhibits excellent transferability and representability for a wide-range of FCC and HCP properties, including (finite-temperature) lattice parameters, elastic constants, phonon spectra, and many defects. As an example of its applicability, we investigate the Ni FCC-HCP allotropic phase transition under (high-stress) uniaxial tensile loading. The high accurate DP model for magnetic Ni facilitates accurate large-scale atomistic simulations for complex phase transformation behavior and may serve as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys.

Abstract The interaction of magnetic order and spontaneous polarization is a fundamental coupling with the prospect for the control of electronic properties and magnetism. The connection among magnetic order, charge localization and associated metal-insulator transition (MIT) are cornerstones for materials control. Materials that combine both effects are therefore of great interest for testing models that claim the occurrence of spontaneous polarization from magnetic and charge order. One class of materials proposed to combine these functionalities is the family of RNiO3 (R: Lanthanide or Yttrium), whose members show a clear MIT and an antiferromagnetic ground state and for which an electric polarization has been predicted. Here, using resonant magnetic x-ray scattering with circular polarization and an applied electric field we show that YNiO3 possess a magnetic structure containing domains of spin-rotations that are consistent with an electric polarization. We show a reversal of the magnetic structure with the applied electric field confirming that charge ordered RNiO3 are magnetoelectric type II multiferroics with a MIT.

Abstract Superconducting quantum bits (qubits) rely on ultra-thin, amorphous oxide tunneling barriers that can have significant inhomogeneities and defects as grown. This can result in relatively large uncertainties and deleterious effects in the circuits, limiting the scalability. Finding a robust solution to the junction reproducibility problem has been a long-standing goal in the field. Here, we demonstrate a transformational technique for controllably tuning the electrical properties of aluminum-oxide tunnel junctions. This is accomplished using a low-voltage, alternating-bias applied individually to the tunnel junctions, with which resistance tuning by more than 70% can be achieved. The data indicates an improvement of coherence and reduction of two-level system defects. Transmission electron microscopy shows that the treated junctions are predominantly amorphous, albeit with a more uniform distribution of alumina coordination across the barrier. This technique is expected to be useful for other devices based on ionic amorphous materials.

Abstract The global concern over environmental consequences of petrochemical-derived plastics underscores the urgent need for sustainable and biodegradable polymers. In this context, elastin-like polypeptides emerge as a promising solution, offering multiple advantages, including remarkable mechanical properties, biocompatibility, customizable functionalities, and renewable sourcing through biosynthetic production in microbes, making them a compelling choice for various applications. We previously demonstrated accelerated engineering of a new class of elastin-like polypeptide‐based materials through hybrid biomimetic‐de novo predictive molecular design. The resulting variants exhibited enhanced molecular stability compared to their natural counterparts, catering to a range of technical applications that involve harsh downstream processing conditions. Here, we showcase the use of some of these previously discovered hybrid variants and illustrate the effective translation of the predicted molecular designs in structural and functional materials in several high-added-value applications. This includes multiscale drug-encapsulating vehicles with controlled release, multifunctional wound coverings, and all-aqueous-based biobased photoresists for creating 2D/3D microstructures.

Abstract Hole spins in Ge/SiGe heterostructures have emerged as an interesting qubit platform with favourable properties such as fast electrical control and noise-resilient operation at sweet spots. However, commonly observed gate-induced electrostatic disorder, drifts, and hysteresis hinder reproducible tune-up of SiGe-based quantum dot arrays. Here, we study Hall bar and quantum dot devices fabricated on Ge/SiGe heterostructures and present a consistent model for the origin of gate hysteresis and its impact on transport metrics and charge noise. As we push the accumulation voltages more negative, we observe non-monotonous changes in the low-density transport metrics, attributed to the induced gradual filling of a spatially varying density of charge traps at the SiGe-oxide interface. With each gate voltage push, we find local activation of a transient low-frequency charge noise component that completely vanishes again after 30 hours. Our results highlight the resilience of the SiGe material platform to interface-trap-induced disorder and noise and pave the way for reproducible tuning of larger multi-dot systems.

Abstract Wearable sensors have evolved significantly, making personalized medicine and real-time disease management possible. However, current digital healthcare is limited to only certain diseases, such as diabetes, due to the lack of mature technologies that can detect small biomolecules. In particular, despite the early detection of chronic kidney disease (CKD) being significant in preventing life-threatening end-stage kidney disease, the development of wearable sensors for CKD monitoring is still in the early stages. In this Perspective, we propose a wearable digital healthcare concept for non-invasive, continuous CKD monitoring, discuss optimal biofluids, biomarkers and bioreceptors to create a wearable CKD sensing platform, and provide insight into potential challenges faced by the technology as well as opportunities.

Abstract Pressing environmental challenges require focused research on sustainable solutions in the domains of energy, water, food, land, and climate. The pigment eumelanin has recently been positioned as a promising candidate for solving issues in health, sensors, and energy storage. However, the low solubility of eumelanin in aqueous solvents, difficult film processibility, and high cost have hindered the material from wide deployment. Here, we propose melanin extracted from the black soldier fly, Hermetia illucens (Mel-BSF), as a sustainable alternative for the preparation of organic electrodes in energy storage applications. Mel-BSF displays pseudocapacitive behaviour with a high potential window, good electrochemical stability, and higher maximum capacity (91.8 mAh g−1) compared to synthetic eumelanin (17.3 mAh g−1) as the working electrode material in zinc-ion hybrid capacitors using an ionic liquid electrolyte. Structural and surface investigations reveal that additional aliphatic compounds, potentially lipids present after Mel-BSF refinement, significantly increase the film stability and redox centre availability.

Abstract Encapsulation polymers in terrestrial solar modules degrade due to ultraviolet radiation from the sun. To assess a polymer’s durability under UV light, accelerated aging tests can be conducted. A new LEDs device allows us to investigate the effects of temperature, irradiation, and UV source spectrum on the photooxidation mechanism and kinetics of two polyethylene-based commercial encapsulants, differentiated by the presence or absence of UV absorbers. The photooxidation rate of the polymer matrix increases as the temperature and irradiance increase between 62 and 82 °C, and 12 and 28 W.m−2, respectively. In the last case, the photooxidation rate is not proportional to the number of photons. Finally, we observed different distributions of degradation products under UVB radiation at 305 nm compared to those under UVA radiation at 365 nm. UVB photons enable Norrish reactions that are not possible with UVA alone. Special care is needed to maintain a balance between UVA and UVB photons to ensure representative durability tests. With a few adjustments to their emission spectrum, UV LED devices appear to be good candidates for accelerated aging of encapsulation polymers.