Design and Manufacture of Multifunctional 3-D Smart Skins with Embedded Sensor Networks for Robotic Applications.

An investigation was performed to develop a process to design and manufacture a 3-D smart skin with an embedded network of distributed sensors for non-developable (or doubly curved) surfaces. A smart skin is the sensing component of a smart structure, allowing such structures to gather data from their surrounding environments to make control and maintenance decisions. Such smart skins are desired across a wide variety of domains, particularly for those devices where their surfaces require high sensitivity to external loads or environmental changes such as human-assisting robots, medical devices, wearable health components, etc. However, the fabrication and deployment of a network of distributed sensors on non-developable surfaces faces steep challenges. These challenges include the conformal coverage of a target object without causing prohibitive stresses in the sensor interconnects and ensuring positional accuracy in the skin sensor deployment positions, as well as packaging challenges resulting from the thin, flexible form factor of the skin. In this study, novel and streamlined processes for making such 3-D smart skins were developed from the initial sensor network design to the final integrated skin assembly. Specifically, the process involved the design of the network itself (for which a physical simulation-based optimization was developed), the deployment of the network to a targeted 3D surface (for which a specialized tool was designed and implemented), and the assembly of the final skin (for which a novel process based on dip coating was developed and implemented.).

Design of a Robust Tool for Deploying Large-Area Stretchable Sensor Networks from Microscale to Macroscale.

An investigation was conducted to develop an effective automated tool to deploy micro-fabricated stretchable networks of distributed sensors onto the surface of large structures at macroscale to create "smart" structures with embedded distributed sensor networks. Integrating a large network of distributed sensors with structures has been a major challenge in the design of so-called smart structures or devices for cyber-physical applications where a large amount of usage data from structures or devices can be generated for artificial intelligence applications. Indeed, many "island-and-serpentine"-type distributed sensor networks, while promising, remain difficult to deploy. This study aims to enable such networks to be deployed in a safe, automated, and efficient way. To this end, a scissor-hinge controlled system was proposed as the basis for a deployment mechanism for such stretchable sensor networks (SSNs). A model based on a kinematic scissor-hinge mechanism was developed to simulate and design the proposed system to automatically stretch a micro-scaled square network with uniformly distributed sensor nodes. A prototype of an automatic scissor-hinge stretchable tool was constructed during the study with an array of four scissor-hinge mechanisms, each belt-driven by a single stepper motor. Two micro-fabricated SSNs from a 100 mm wafer were fabricated at the Stanford Nanofabrication Facility for this deployment study. The networks were designed to be able to cover an area 100 times their manufacturing size (from a 100 mm diameter wafer to a 1 m2 active area) once stretched. It was demonstrated that the proposed deployment tool could place sensor nodes in prescribed locations efficiently within a drastically shorter time than in current labor-intensive manual deployment methods.

Quantitative Förster Resonance Energy Transfer: Efficient Light Harvesting for Sequential Photo-Thermo-Electric Conversion.

Light is essential to all life on the earth. Thus, highly efficient light-harvesting systems with the sequential energy transfer process are significant for using solar energy in photosynthesis. For developing an efficient light-harvesting system, a liquid aggregation-induced emission (AIE) dye TPE-EA is obtained, as a donor and solvent, which can light up the aggregation caused quenching (ACQ) Nile Red (NiR, acceptor) to construct a quantitative Förster resonance energy transfer (FRET) system in NiR⊂TPE-EA. Impressively, this FRET pair shows an impressive photothermal effect, producing a peak temperature of 119 °C while excited by UV light, with 37.8% of conversion efficiency. NiR⊂TPE-EA is quite different from most other photothermal materials, which require excitation with long wavelength light (>520 nm). Therefore, NiR⊂TPE-EA firstly converts the solar into thermal energy and then into electric energy to achieve sequential photo-thermo-electric conversion. Such sequential conversion, suitable for being excited by sunlight, is anticipated to unlock new and smart approaches for capturing solar energy.

Static Tactile Sensing for a Robotic Electronic Skin via an Electromechanical Impedance-Based Approach.

Tactile sensing is paramount for robots operating in human-centered environments to help in understanding interaction with objects. To enable robots to have sophisticated tactile sensing capability, researchers have developed different kinds of electronic skins for robotic hands and arms in order to realize the 'sense of touch'. Recently, Stanford Structures and Composites Laboratory developed a robotic electronic skin based on a network of multi-modal micro-sensors. This skin was able to identify temperature profiles and detect arm strikes through embedded sensors. However, sensing for the static pressure load is yet to be investigated. In this work, an electromechanical impedance-based method is proposed to investigate the response of piezoelectric sensors under static normal pressure loads. The smart skin sample was firstly fabricated by embedding a piezoelectric sensor into the soft silicone. Then, a series of static pressure tests to the skin were conducted. Test results showed that the first peak of the real part impedance signal was sensitive to static pressure load, and by using the proposed diagnostic method, this test setup could detect a resolution of 0.5 N force. Numerical simulation methods were then performed to validate the experimental results. The results of the numerical simulation prove the validity of the experiments, as well as the robustness of the proposed method in detecting static pressure loads using the smart skin.

Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing.

This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor "islands" connected by interconnecting "serpentines." A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge (SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c) configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.

A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification.

The vibration of a wing structure in the air reflects coupled aerodynamic⁻mechanical responses under varying flight states that are defined by the angle of attack and airspeed. It is of great challenge to identify the flight state from the complex vibration signals. In this paper, a novel one-dimension convolutional neural network (CNN) is developed, which is able to automatically extract useful features from the structural vibration of a recently fabricated self-sensing wing through wind-tunnel experiments. The obtained signals are firstly decomposed into various subsignals with different frequency bands via dual-tree complex-wavelet packet transformation. Then, the reconstructed subsignals are selected to form the best combination for multichannel inputs of the CNN. A swarm-based evolutionary algorithm called grey-wolf optimizer is utilized to optimize a set of key parameters of the CNN, which saves considerable human efforts. Two case studies demonstrate the high identification accuracy and robustness of the proposed method over standard deep-learning methods in flight-state identification, thus providing new perspectives in self-awareness toward the next generation of intelligent air vehicles.

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches.

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles.

Characterization of Distributed Microfabricated Strain Gauges on Stretchable Sensor Networks for Structural Applications.

Smart structures mimic biological systems by using thousands of sensors serving as a nervous system analog. One approach to give structures this sensing ability is to develop a multifunctional sensor network. Previous work has demonstrated stretchable sensor networks consisting of temperature sensors and impact detectors for monitoring external environments and interacting with other objects. The objective of this work is to develop distributed, robust and reliable strain gauges for obtaining the strain distribution of a designated region on the target structure. Here, we report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices and 8 piezoelectric transducers symmetrically distributed over an area of 150 × 150 mm to map and quantify multiple physical stimuli with a spatial resolution of 2.5 × 2.5 mm. We performed computational modeling of the network stretching process to improve measurement accuracy and conducted experimental characterizations of the microfabricated strain gauges to verify their gauge factor and temperature coefficient. Collectively, the results represent a robust and reliable sensing system that is able to generate a distributed strain profile of a common structure. The reported strain gauge network may find a wide range of applications in morphing wings, smart buildings, autonomous cars and intelligent robots.

New Nanoconfined Galvanic Replacement Synthesis of Hollow Sb@C Yolk-Shell Spheres Constituting a Stable Anode for High-Rate Li/Na-Ion Batteries.

In the current research project, we have prepared a novel Sb@C nanosphere anode with biomimetic yolk-shell structure for Li/Na-ion batteries via a nanoconfined galvanic replacement route. The yolk-shell microstructure consists of Sb hollow yolk completely protected by a well-conductive carbon thin shell. The substantial void space in the these hollow Sb@C yolk-shell particles allows for the full volume expansion of inner Sb while maintaining the framework of the Sb@C anode and developing a stable SEI film on the outside carbon shell. As for Li-ion battery anode, they displayed a large specific capacity (634 mAh g-1), high rate capability (specific capabilities of 622, 557, 496, 439, and 384 mAh g-1 at 100, 200, 500, 1000, and 2000 mA g-1, respectively) and stable cycling performance (a specific capacity of 405 mAh g-1 after long 300 cycles at 1000 mA g-1). As for Na-ion storage, these yolk-shell Sb@C particles also maintained a reversible capacity of approximate 280 mAh g-1 at 1000 mA g-1 after 200 cycles.

Bio-Inspired Stretchable Absolute Pressure Sensor Network.

A bio-inspired absolute pressure sensor network has been developed. Absolute pressure sensors, distributed on multiple silicon islands, are connected as a network by stretchable polyimide wires. This sensor network, made on a 4'' wafer, has 77 nodes and can be mounted on various curved surfaces to cover an area up to 0.64 m × 0.64 m, which is 100 times larger than its original size. Due to Micro Electro-Mechanical system (MEMS) surface micromachining technology, ultrathin sensing nodes can be realized with thicknesses of less than 100 µm. Additionally, good linearity and high sensitivity (~14 mV/V/bar) have been achieved. Since the MEMS sensor process has also been well integrated with a flexible polymer substrate process, the entire sensor network can be fabricated in a time-efficient and cost-effective manner. Moreover, an accurate pressure contour can be obtained from the sensor network. Therefore, this absolute pressure sensor network holds significant promise for smart vehicle applications, especially for unmanned aerial vehicles.

Full-Color Circularly Polarized Luminescence of Supramolecular Polymers with Handedness Inversion Regulated by Anion and Temperature.

Constructing full-color circularly polarized luminescence (CPL) materials with switchable handedness in the solid state is an appealing yet considerably challenging task, especially for supramolecular polymer films assembled from homochiral monomers. Herein, supramolecular polymers with full-color CPL and inverted handedness are realized through the coassembly of a homochiral cholesterol derivative (PVPCC), metal ions (Zn2+), and achiral fluorescent dyes. The obtained coassembled systems show anion-directed supramolecular chirality inversion by exchanging the anions of NO3-, ClO4-, BF4-, and Cl-. For instance, the negative CD and right-handed CPL are detected in the PVPCC/Zn(NO3)2 aggregates, which convert into positive CD and left-handed CPL after introducing Cl-, corresponding to the transformation from nanorods to nanofibers. Furthermore, the tunable CPL color and handedness inversion of the coassembly system of PVPCC/Zn(NO3)2 and achiral fluorescent dyes can be established by alternately changing the assembling temperature of 298 and 273 K. Importantly, the full-color CPL polymeric materials are then constructed by doping the PVPCC/Zn(NO3)2/dyes complexes into poly(methyl methacrylate) (PMMA) film, which maintains the handedness inversion and shows the enhanced CPL performance. The work not only deepens the understanding of chirality inversion in supramolecular chemistry but also helps to construct full-color CPL materials with switchable handedness from homochiral building blocks in materials science.

Multicolor circularly polarized luminescence inversion of metal-organic supramolecular polymers.

Metal-organic supramolecular polymers (MOSPs) with multicolor circularly polarized luminescence (CPL) and handedness inversion were constructed from the coordination-driven assembly of pyridine-cyanostilbene-cholesterol and metal salts by modulating the treatment modes, solvents, and metal ions.

Solvent-Directed Hierarchical Self-Assembly of Tetraphenylpyrazine-Cholesterol with Amplified Circularly Polarized Luminescence.

It is important to identify the effect of assembly and aggregation on the chirality transfer and energy transmission in supramolecular polymer system, since the unordered aggregation is insufficient to promote luminescence enhancement and chirality transfer, even causing the negative effect. Another key issue is to identify the solvent effect on hierarchically chiral self-assembly. Herein, we designed an AIE-core based building block, tetraphenylpyrazine-cholesterol (TPP-Chol), to explore how the solvent component influences chirality transfer and energy transmission of its aggregates and/or assemblies. Interestingly, the hierarchical assembly behavior was realized in the mixture of MeOH/CHCl3 highly dependent on the MeOH content. During the solvent-directed hierarchical assembly, the morphologic transformations, such as nanoribbons with a width of 150 nm, twisted nanoribbons with helical pitch of 420 nm, nanoribbon clusters, and microflowers with an average diameter of 5.5 µm, were realized with obvious chirality amplification for both circular dichroism (CD) and circularly polarized luminescence (CPL) measurements. The hierarchical assembly of TPP-Chol was also demonstrated by a time-dependent CD test. The work points out the complexity and dynamic of hierarchically chiral self-assembly regulated by the solvent effect, which would be helpful for the development of supramolecular materials with enhanced CPL performance and dynamic chirality.

Metallophilic Interaction-Mediated Hierarchical Assembly and Temporal-Controlled Dynamic Chirality Inversion of Metal-Organic Supramolecular Polymers.

The study of dynamic supramolecular chirality inversion (SMCI) not only helps to deepen the understanding of chiral transfer and amplification in both living organizations and artificially chemical self-assembly systems but also is useful for the development of smart chiral nanomaterials. However, it is still challenging to achieve the dynamic SMCI of the self-aggregation of metal-organic supramolecular polymers with great potential in asymmetric synthesis, chiroptical switches, and circular polarized luminescence. Here, we successfully developed a hierarchical coassembly system based on the mPAzPCC and various metal ions with effective chirality transfer and temporal-controlled SMCI. Due to the dynamic self-assembly and hierarchical chirality transfer of the Ag+/mPAzPCC complex driven by metallophilic interaction and coordination, morphological transition with nanoribbons, helical nanoribbons, and chiral nanotubules was successively obtained. Interestingly, the SMCI of chiral nanoaggregates was precisely regulated by solvents and metal ions in the Cu2+/mPAzPCC and Mn2+/mPAzPCC system. Besides, temporal-controlled dynamic SMCI switching from helix to bundled helix was clearly revealed in the aggregation of Cu2+/mPAzPCC, Mn2+/mPAzPCC, and Bi3+/mPAzPCC systems. This work provides a metallophilic interaction-mediated helical assembly pathway to dynamically modulate the chirality of metal-organic complex-based assemblies and deepen the understanding of the hierarchically dynamic self-assembly process, which would be of great potential in metal ion-mediated supramolecular asymmetric catalysis and bioinspired chiral sensing.

Boosting Oxygen-Evolving Activity via Atom-Stepped Interfaces Architected with Kinetic Frustration.

A highly active interface is extremely critical for the catalytic efficiency of an electrocatalyst; however, facilely tailoring its atomic packing characteristics remains challenging. Herein, a simple yet effective strategy is reported to obtain copious high-energy atomic steps at the interface via controlling the solidification behavior of glass-forming metallic liquids. By adjusting the chemical composition and cooling rate, highly faceted FeNi3 nanocrystals are in situ formed in an FeNiB metallic glass (MG) matrix, leading to the creation of order/disorder interfaces. Benefiting from the catalytically active and stable atomic steps at the jagged interfaces, the resultant free-standing FeNi3 nanocrystal/MG composite exhibits a low oxygen-evolving overpotential of 214 mV at 10 mA cm-2 , a small Tafel slope of 32.4 mV dec-1 , and good stability in alkaline media, outperforming most state-of-the-art catalysts. This approach is based on the manipulation of nucleation and crystal growth of the solid-solution nanophases (e.g., FeNi3 ) in glass-forming liquids, so that the highly stepped interface architecture can be obtained due to the kinetic frustration effect in MGs upon undercooling. It is envisaged that the atomic-level stepped interface engineering via the physical metallurgy method can be easily extended to other MG systems, providing a new and generic paradigm for designing efficient yet cost-effective electrocatalysts.

Engineering Fe-N4 Electronic Structure with Adjacent Co-N2C2 and Co Nanoclusters on Carbon Nanotubes for Efficient Oxygen Electrocatalysis.

Regulating the local configuration of atomically dispersed transition-metal atom catalysts is the key to oxygen electrocatalysis performance enhancement. Unlike the previously reported single-atom or dual-atom configurations, we designed a new type of binary-atom catalyst, through engineering Fe-N4 electronic structure with adjacent Co-N2C2 and nitrogen-coordinated Co nanoclusters, as oxygen electrocatalysts. The resultant optimized electronic structure of the Fe-N4 active center favors the binding capability of intermediates and enhances oxygen reduction reaction (ORR) activity in both alkaline and acid conditions. In addition, anchoring M-N-C atomic sites on highly graphitized carbon supports guarantees of efficient charge- and mass-transports, and escorts the high bifunctional catalytic activity of the entire catalyst. Further, through the combination of electrochemical studies and in-situ X-ray absorption spectroscopy analyses, the ORR degradation mechanisms under highly oxidative conditions during oxygen evolution reaction processes were revealed. This work developed a new binary-atom catalyst and systematically investigates the effect of highly oxidative environments on ORR electrochemical behavior. It demonstrates the strategy for facilitating oxygen electrocatalytic activity and stability of the atomically dispersed M-N-C catalysts.

Association between medication adherence and disease outcomes in patients with hepatitis B-related cirrhosis: a population-based case-control study.

OBJECTIVE: To evaluate medication adherence among patients with hepatitis B-related cirrhosis who developed decompensation and mortality, and to examine the association between medication adherence and patients' disease outcomes. DESIGN: In this retrospective case-control study, patients aged over 20 years old and diagnosed with both chronic hepatitis B and cirrhosis from 2007 to 2016 are identified using a population-based medical claims database. Two prognosis endpoints (decompensation and mortality) are used, respectively, to classify subjects into two different case-control sets. Study groups are propensity-score matched. Medication possession ratio (MPR) is used as a measure of treatment adherence for oral antiviral drugs, and conditional logistic regression models are used to estimate the odds of decompensation and mortality after accounting for MPR and other covariates. RESULTS: Between decompensated and compensated patients, longer term treatment adherence is seen higher in the compensated group versus the decompensated group: 1-year MPR (0.65±0.43 vs 0.57±0.53) and 6-month MPR (0.79±0.52 vs 0.76±0.79). On the contrary, 3-month adherence is higher in the decompensated group (1.00±1.15 vs 0.96±0.79). For patients with and without mortality, drug adherence is ubiquitously higher in the alive group regardless of follow-up length: 1-year MPR (0.62±0.44 vs 0.50±0.51), 6-month MPR (0.78±0.62 vs 0.69±0.72) and 3-month MPR (0.97±0.91 vs 0.96±1.12). After accounting for confounding variables, we find that the likelihood of complicated cirrhosis is significantly lower in more adherent patients and the benefit increases with more persistent adherence (log 1-year MPR OR: 0.75, 95% CI: 0.73 to 0.77). Similar results are observed for the adjusted likelihood of mortality (log 1-year MPR OR: 0.70, 95% CI: 0.68 to 0.72). CONCLUSIONS: Long-term patient adherence to oral antiviral therapy remains inadequate in patients with hepatitis B virus-related cirrhosis. Their adherence to oral antiviral therapy appears to be inversely associated with decompensation and mortality.

Multiple chirality inversion of pyridine Schiff-base cholesterol-based metal-organic supramolecular polymers.

Based on a metal coordination driven co-assembly strategy, a metal-organic supramolecular polymer system of pyridine Schiff-base cholesterol and metal ions with multiple supramolecular chirality inversion was successfully achieved by the stoichiometry and exchange of metal ions (such as Co2+, Ni2+, Cu2+, Zn2+, and Ag+), as well as the solvent polarity.

High-content atomically distributed W(V,VI) on FeCo layered double hydroxide with high oxygen evolution reaction activity.

High-content atomically distributed W(V,VI) coordinated with O atoms as WO2 moieties anchored on FeCo layered double hydroxide (FeCo LDH) nanosheets in the structure of SAC W-FeCo LDH is obtained by a facile coprecipitation method, and it presented clearly enhanced stable OER electrocatalytic activity.

Structure and Anti-Inflammatory Activity Relationship of Ergostanes and Lanostanes in Antrodia cinnamomea.

Antrodia cinnamomea is a precious edible mushroom originating from Taiwan that has been popularly used for adjuvant hepatoprotection and anti-inflammation; however, the chemical principle for its anti-inflammatory activity has not been elucidated, which prevents the quality control of related products. Using the RAW264.7 model for the anti-inflammatory activity assay as a guide, we reported the isolation and structural elucidation of three potent anti-inflammatory compounds from isolated ergostanes (16) and lanostanes (6). Their structures were elucidated on the basis of spectroscopic data analysis including NMR and HR-QTOF-MS. Particularly, the absolute configurations of (25R)-antcin K, (25R)-antcin A, versisponic acid D, and (25R)-antcin C were determined by single crystal X-ray diffraction (XRD). The representative and most promising compound antcin A was shown to suppress pro-inflammatory biomolecule release via the down-regulation of iNOS and COX-2 expression through the NF-κB pathway while the mRNA levels of IL-1ß, TNF-α and IL-6 were also decreased. The high dependency on structural variation and activity suggests that there might be special biological targets for antcin A. Our work makes it possible to develop evidence-based dietary supplements from Antrodia cinnamomea based on anti-inflammatory constituents.