Multi-Objective Bayesian Optimization for Laminate-Inspired Mechanically Reinforced Piezoelectric Self-Powered Sensing Yarns.

Piezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF-TrFE) yarn structures are efficiently optimized. By leveraging a multi-objective Bayesian optimization-based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high-performance piezoelectric P(VDF-TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self-powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real-time signal detection.

Deep generative spatiotemporal learning for integrating fracture mechanics in composite materials: inverse design, discovery, and optimization.

The trade-off between strength and toughness presents a fundamental challenge in engineering material design. Composite materials (CMs) can strategically arrange different materials to enhance both strength and toughness by optimizing the distribution of loads and increasing resistance to crack propagation. However, current data-driven computational modeling approaches for CM configuration optimization suffer from limitations of "substantial computational cost" and "poor predictive power over extrapolation spaces", making it difficult to integrate with global optimization algorithms, and ultimately limiting the discovery of materials with optimal tradeoffs. As a breakthrough, we propose a data-driven design framework with a multi-task DL architecture capable of accurately predicting local fields' spatiotemporal behavior, including stress evolution and crack propagation, alongside homogenized mechanical properties. Our model, trained on datasets generated from crack phase fields simulations of random configurations, demonstrated exceptional predictive performance even for unseen configurations with well organized patterns exploiting nature-inspired morphological features. Importantly, solely from composite material (CM) configurations, our model effectively predicts long-term spatiotemporal fields with an accuracy comparable to FEM but with a substantial reduction in computational time. By coupling the model's predictive power with genetic optimization algorithms, we demonstrated the framework's applicability in two representative inverse design tasks: devising CM configurations with mechanical properties beyond the training set and guiding desired crack pattern formation. Our research highlights the potential of artificial intelligence as a feasible alternative to conventional computational approaches for straightforward configurational and structural optimization.

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra.

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.

Ultrastrong and Deformable Aluminum-Based Composite Nanolaminates with Transformable Binary Intergranular Films.

Nanostructured metals with conventional grain boundaries or interfaces exhibit high strength yet usually poor ductility. Here we report an interface engineering strategy that breaks the strength-ductility dilemma via externally incorporating graphene oxide at lamella boundaries of aluminum (Al) nanolaminates. By forming the binary intergranular films where graphene oxide was sandwiched between two amorphous alumina layers, the Al-based composite nanolaminates achieved ultrahigh compressive strength (over 1 GPa) while retaining excellent plastic deformability. Complementing experimental results with molecular dynamics simulation efforts, the ultrahigh strength was interpreted by the strong blocking effect of the binary intergranular films on dislocation nucleation and propagation, and the excellent plasticity was found to originate from the stress/strain-induced crystalline-to-amorphous transition of graphene oxide and the synergistic deformation between Al nanolamellas and the binary intergranular films.

On-Demand Crack Formation on DNA Film via Organic Solvent-Induced Dehydration.

Crack is found on the soil when severe drought comes, which inspires the idea to rationalize patterning applications using dried deoxyribonucleic acid (DNA) film. DNA is one of the massively produced biomaterials in nature, showing the lyotropic liquid crystal (LC) phase in highly concentrated conditions. DNA nanostructures in the hydrated condition can be orientation controlled, which can be extended to make dryinginduced cracks. The controlled crack generation in oriented DNA films by inducing mechanical fracture through organic solvent-induced dehydration (OSID) using tetrahydrofuran (THF) is explored. The corresponding simulations show a strong correlation between the long axis of DNA due to the shrinkage during the dehydration and in the direction of crack propagation. The cracks are controlled by simple brushing and a 3D printing method. This facile way of aligning cracks will be used in potential patterning applications.

Highly Sensitive Soft Pressure Sensors for Wearable Applications Based on Composite Films with Curved 3D Carbon Nanotube Structures.

Soft pressure sensors based on 3D microstructures exhibit high sensitivity in the low-pressure range, which is crucial for various wearable and soft touch applications. However, it is still a challenge to manufacture soft pressure sensors with sufficient sensitivity under small mechanical stimuli for wearable applications. This work presents a novel strategy for extremely sensitive pressure sensors based on the composite film with local changes in curved 3D carbon nanotube (CNT) structure via expandable microspheres. The sensitivity is significantly enhanced by the synergetic effects of heterogeneous contact of the microdome structure and changes of percolation network within the curved 3D CNT structure. The finite-element method simulation is used to comprehend the relationships between the sensitivity and mechanical/electrical behavior of microdome structure under the applied pressure. The sensor shows an excellent sensitivity (571.64 kPa-1 ) with fast response time (85 ms), great repeatability, and long-term stability. Using the developed sensor, a wireless wearable health monitoring system to avoid carpel tunnel syndrome is built, and a multi-array pressure sensor for realizing a variety of movements in real-time is demonstrated.

Simple and Multiplexed Detection of Nucleic Acid Targets Based on Fluorescent Ring Patterns and Deep Learning Analysis.

Simple diagnostic tests for nucleic acid targets can provide great advantages for applications such as rapid pathogen detection. Here, we developed a membrane assay for multiplexed detection of nucleic acid targets based on the visualization of two-dimensional fluorescent ring patterns. A droplet of the assay solution is applied to a cellulose nitrate membrane, and upon radial chromatographic flow and evaporation of the solvent, fluorescent patterns appear under UV irradiation. The target nucleic acid is isothermally amplified and is immediately hybridized with fluorescent oligonucleotide probes in a one-pot reaction. We established the fluorescent ring assay integrated with isothermal amplification (iFluor-RFA = isothermal fluorescent ring-based radial flow assay), and feasibility was tested using nucleic acid targets of the receptor binding domain (RBD) and RNA-dependent RNA polymerase (RdRp) genes of SARS-CoV-2. We demonstrate that the iFluor-RFA method is capable of specific and sensitive detection in the subpicomole range, as well as multiplexed detection even in complex solutions. Furthermore, we applied deep learning analysis of the fluorescence images, showing that patterns could be classified as positive or negative and that quantitative amounts of the target could be predicted. The current technique, which is a membrane pattern-based nucleic acid assay combined with deep learning analysis, provides a novel approach in diagnostic platform development that can be versatilely applied for the rapid detection of infectious pathogens.

Multimodal E-Textile Enabled by One-Step Maskless Patterning of Femtosecond-Laser-Induced Graphene on Nonwoven, Knit, and Woven Textiles.

Personal wearable devices are considered important in advanced healthcare, military, and sports applications. Among them, e-textiles are the best candidates because of their intrinsic conformability without any additional device installation. However, e-textile manufacturing to date has a high process complexity and low design flexibility. Here, we report the direct laser writing of e-textiles by converting raw Kevlar textiles to electrically conductive laser-induced graphene (LIG) via femtosecond laser pulses in ambient air. The resulting LIG has high electrical conductivity and chemical reliability with a low sheet resistance of 2.86 Ω/□. Wearable multimodal e-textile sensors and supercapacitors are realized on different types of Kevlar textiles, including nonwoven, knit, and woven structures, by considering their structural textile characteristics. The nonwoven textile exhibits high mechanical stability, making it suitable for applications in temperature sensors and micro-supercapacitors. On the other hand, the knit textile possesses inherent spring-like stretchability, enabling its use in the fabrication of strain sensors for human motion detection. Additionally, the woven textile offers special sensitive pressure-sensing networks between the warp and weft parts, making it suitable for the fabrication of bending sensors used in detecting human voices. This direct laser synthesis of arbitrarily patterned LIGs from various textile structures could result in the facile realization of wearable electronic sensors and energy storage.

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review.

In the last few decades, the influence of machine learning has permeated many areas of science and technology, including the field of materials science. This toolkit of data driven methods accelerated the discovery and production of new materials by accurately predicting the complicated physical processes and mechanisms that are not fully described by existing materials theories. However, the availability of a growing number of increasingly complex machine learning models confronts us with the question of "which machine learning algorithm to employ". In this review, we provide a comprehensive review of common machine learning algorithms used for materials design, as well as a guideline for selecting the most appropriate model considering the nature of the design problem. To this end, we classify the material design problems into four categories of: (i) the training data set being sufficiently large to capture the trend of design space (interpolation problem), (ii) a vast design space that cannot be explored thoroughly with the initial training data set alone (extrapolation problem), (iii) multi-fidelity datasets (small accurate dataset and large approximate dataset), and (iv) only a small dataset available. The most successful machine learning-based surrogate models and design approaches will be discussed for each case along with pertinent literature. This review focuses mostly on the use of ML algorithms for the inverse design of complicated composite structures, a topic that has received a lot of attention recently with the rise of additive manufacturing.

Multi-objective Bayesian optimization for the design of nacre-inspired composites: optimizing and understanding biomimetics through AI.

The hierarchical structures found in biological materials lead to an outstanding balance of multiple material properties, and numerous research studies have been initiated to emulate the key concepts for the designing of engineering materials, the so-called bioinspired composites. However, the optimization of bioinspired composites has long been difficult as it usually falls into the category of 'black-box problem', the objective functions not being available in a functional form. Also, bioinspired composites possess multiple material properties that are in a trade-off relationship, making it impossible to reach a unique optimal design solution. As a breakthrough, we propose a data-driven material design framework which can generate bioinspired composite designs with an optimal balance of material properties. In this study, a nacre-inspired composite is chosen as the subject of study and the optimization framework is applied to determine the designs that have an optimal balance of strength, toughness, and specific volume. Gaussian process regression was adopted for the modeling of a complex input-output relationship, and the model was trained with the data generated from the crack phase-field simulation. Then, multi-objective Bayesian optimization was carried out to determine pareto-optimal composite designs. As a result, the proposed data-driven algorithm could generate a 3D pareto surface of optimal composite design solutions, from which a user can choose a design that suits his/her requirement. To validate the result, several pareto-optimal designs are built using a PolyJet 3D printer, and their tensile test results show that each of the characteristic designs is well optimized for its specific target objective.

Configurable Crack Wall Conduction in a Complex Oxide.

Mobile defects in solid-state materials play a significant role in memristive switching and energy-efficient neuromorphic computation. Techniques for confining and manipulating point defects may have great promise for low-dimensional memories. Here, we report the spontaneous gathering of oxygen vacancies at strain-relaxed crack walls in SrTiO3 thin films grown on DyScO3 substrates as a result of flexoelectricity. We found that electronic conductance at the crack walls was enhanced compared to the crack-free region, by a factor of 104. A switchable asymmetric diode-like feature was also observed, and the mechanism is discussed, based on the electrical migration of oxygen vacancy donors in the background of Sr-deficient acceptors forming n+-n or n-n+ junctions. By tracing the temporal relaxations of surface potential and lattice expansion of a formed region, we determine the diffusivity of mobile defects in crack walls to be 1.4 × 10-16 cm2/s, which is consistent with oxygen vacancy kinetics.

Universal assembly of liquid metal particles in polymers enables elastic printed circuit board.

An elastic printed circuit board (E-PCB) is a conductive framework used for the facile assembly of system-level stretchable electronics. E-PCBs require elastic conductors that have high conductivity, high stretchability, tough adhesion to various components, and imperceptible resistance changes even under large strain. We present a liquid metal particle network (LMPNet) assembled by applying an acoustic field to a solid-state insulating liquid metal particle composite as the elastic conductor. The LMPNet conductor satisfies all the aforementioned requirements and enables the fabrication of a multilayered high-density E-PCB, in which numerous electronic components are intimately integrated to create highly stretchable skin electronics. Furthermore, we could generate the LMPNet in various polymer matrices, including hydrogels, self-healing elastomers, and photoresists, thus showing their potential for use in soft electronics.

Deep neural network-based structural health monitoring technique for real-time crack detection and localization using strain gauge sensors.

Structural health monitoring (SHM) techniques often require a large number of sensors to evaluate and monitor the structural health. In this paper, we propose a deep neural network (DNN)-based SHM method for accurate crack detection and localization in real time using a small number of strain gauge sensors and confirm its feasibility based on experimental data. The proposed method combines a DNN model with principal component analysis (PCA) to predict the strain field based on the local strains measured by strain gauge sensors located rather sparsely. We demonstrate the potential of the proposed technique via a cyclic 4-point bending test performed on a composite material specimen without cracks and seven specimens with different lengths of cracks. A dataset containing local strains measured with 12 strain gauge sensors and strain field measured with a digital image correlation (DIC) device was prepared. The strain field dataset from DIC is converted to a smaller dimension latent space with a few eigen basis via PCA, and a DNN model is trained to predict principal component values of each image with 12 strain gauge sensor measurements as input. The proposed method turns out to accurately predict the strain field for all specimens considered in the study.

Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures.

Nanomaterials with core-shell architectures are prominent examples of strain-engineered materials. The lattice mismatch between the core and shell materials can cause strong interface strain, which affects the surface structures. Therefore, surface functional properties such as catalytic activities can be designed by fine-tuning the misfit strain at the interface. To precisely control the core-shell effect, it is essential to understand how the surface and interface strains are related at the atomic scale. Here, we elucidate the surface-interface strain relations by determining the full 3D atomic structure of Pd@Pt core-shell nanoparticles at the single-atom level via atomic electron tomography. Full 3D displacement fields and strain profiles of core-shell nanoparticles were obtained, which revealed a direct correlation between the surface and interface strain. The strain distributions show a strong shape-dependent anisotropy, whose nature was further corroborated by molecular statics simulations. From the observed surface strains, the surface oxygen reduction reaction activities were predicted. These findings give a deep understanding of structure-property relationships in strain-engineerable core-shell systems, which can lead to direct control over the resulting catalytic properties.

Human-muscle-inspired single fibre actuator with reversible percolation.

Artificial muscles are indispensable components for next-generation robotics capable of mimicking sophisticated movements of living systems. However, an optimal combination of actuation parameters, including strain, stress, energy density and high mechanical strength, is required for their practical applications. Here we report mammalian-skeletal-muscle-inspired single fibres and bundles with large and strong contractive actuation. The use of exfoliated graphene fillers within a uniaxial liquid crystalline matrix enables photothermal actuation with large work capacity and rapid response. Moreover, the reversible percolation of graphene fillers induced by the thermodynamic conformational transition of mesoscale structures can be in situ monitored by electrical switching. Such a dynamic percolation behaviour effectively strengthens the mechanical properties of the actuator fibres, particularly in the contracted actuation state, enabling mammalian-muscle-like reliable reversible actuation. Taking advantage of a mechanically compliant fibre structure, smart actuators are readily integrated into strong bundles as well as high-power soft robotics with light-driven remote control.

Improving the Sensitivity of the Mechanoluminescence Composite through Functionalization for Structural Health Monitoring.

Over the past few years, considerable effort has been directed toward the development and improvement of mechanoluminescence (ML)-based stress sensing as an efficient nondestructive inspection technique. One of the challenges in ML stress sensing is the limited luminescent intensity and sensitivity of the ML-epoxy composite film to the local stress field. Herein, we present a novel approach for increasing the sensitivity of ML composites made of an epoxy resin matrix and SrAl2O4:Eu2+, Dy3+ particles functionalized with (3-aminopropyl)triethoxysilane. We performed a tensile test on an epoxy/ML composite specimen to investigate the effect of surface modification of ML particles on the luminescent sensitivity. A series of characterization analyses were performed on the modified surface to investigate the interfacial bonding. In addition, we applied the modified ML/epoxy paint to one side of the tensile specimen with an artificial invisible notch on the other side to visualize the stress field via light intensity (LI) distribution and then compared the results through a finite-element analysis (FEA). Surface modification of ML particles increased the sensitivity and introduced new chemical bonds, corresponding to a larger stress transfer through interfacial bonding rather than mere mechanical locking. In addition, the applied ML film on the notched specimen could visualize the specific pattern of LI reflecting the presence of a crack, which was confirmed by the FEA simulation. This implies that the proposed method of enhancing the ML film is promising for nondestructively predicting the presence, shape, and residual life of a crack in a specimen.

Ambient Humidity-Induced Phase Separation for Fiber Morphology Engineering toward Piezoelectric Self-Powered Sensing.

Electrospun polymeric piezoelectric fibers have a considerable potential for shape-adaptive mechanical energy harvesting and self-powered sensing in biomedical, wearable, and industrial applications. However, their unsatisfactory piezoelectric performance remains an issue to be overcome. While strategies for increasing the crystallinity of electroactive ß phases have thus far been the major focus in realizing enhanced piezoelectric performance, tailoring the fiber morphology can also be a promising alternative. Herein, a design strategy that combines the nonsolvent-induced phase separation of a polymer/solvent/water ternary system and electrospinning for fabricating piezoelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE) fibers with surface porosity under ambient humidity is presented. Notably, electrospun P(VDF-TrFE) fibers with higher surface porosity outperform their smooth-surfaced counterparts with a higher ß phase content in terms of output voltage and power generation. Theoretical and numerical studies also underpin the contribution of the structural porosity to the harvesting performance, which is attributable to local stress concentration and reduced dielectric constant due to the air in the pores. This porous fiber design can broaden the application prospects of shape-adaptive energy harvesting and self-powered sensing based on piezoelectric polymer fibers with enhanced voltage and power performance, as successfully demonstrated in this work by developing a communication system based on self-powered motion sensing.

Direct Observation of Three-Dimensional Atomic Structure of Twinned Metallic Nanoparticles and Their Catalytic Properties.

We determined a full 3D atomic structure of a dumbbell-shaped Pt nanoparticle formed by a coalescence of two nanoclusters using deep learning assisted atomic electron tomography. Formation of a double twin boundary was clearly observed at the interface, while substantial anisotropy and disorder were also found throughout the nanodumbbell. This suggests that the diffusion of interfacial atoms mainly governed the coalescence process, but other dynamic processes such as surface restructuring and plastic deformation were also involved. A full 3D strain tensor was clearly mapped, which allows direct calculation of the oxygen reduction reaction activity at the surface. Strong tensile strain was found at the protruded region of the nanodumbbell, which results in an improved catalytic activity on {100} facets. This work provides important clues regarding the coalescence mechanism and the relation between the atomic structure and catalytic property at the single-atom level.