Ultrasensitive and ultrastretchable electrically self-healing conductors.

Self-healing is a bioinspired strategy to repair damaged conductors under repetitive wear and tear, thereby largely extending the life span of electronic devices. The self-healing process often demands external triggering conditions as the practical challenges for the widespread applications. Here, a compliant conductor with electrically self-healing capability is introduced by combining ultrahigh sensitivity to minor damages and reliable recovery from ultrahigh tensile deformations. Conductive features are created in a scalable and low-cost fabrication process comprising a copper layer on top of liquid metal microcapsules. The efficient rupture of microcapsules is triggered by structural damages in the copper layer under stress conditions as a result of the strong interfacial interactions. The liquid metal is selectively filled into the damaged site for the instantaneous restoration of the metallic conductivity. The unique healing mechanism is responsive to various structural degradations including microcracks under bending conditions and severe fractures upon large stretching. The compliant conductor demonstrates high conductivity of ∼12,000 S/cm, ultrahigh stretchability of up to 1,200% strain, an ultralow threshold to activate the healing actions, instantaneous electrical recovery in microseconds, and exceptional electromechanical durability. Successful implementations in a light emitting diode (LED) matrix display and a multifunctional electronic patch demonstrate the practical suitability of the electrically self-healing conductor in flexible and stretchable electronics. The developments provide a promising approach to improving the self-healing capability of compliant conductors.

Circulation in Quasi-2D Turbulence: Experimental Observation of the Area Rule and Bifractality.

We present an experimental study of the velocity circulation in a quasi-two-dimensional turbulent flow. We show that the area rule of circulation around simple loops holds in both the forward cascade enstrophy inertial range (ΩIR) and the inverse cascade energy inertial range (EIR): When the side lengths of a loop are all within the same inertial range, the circulation statistics depend on the loop area alone. It is also found that, for circulation around figure-eight loops, the area rule still holds in EIR but is not applicable in ΩIR. In ΩIR, the circulation is nonintermittent; whereas in EIR, the circulation is bifractal: space filling for moments of the order of 3 and below and a monofractal with a dimension of 1.42 for higher orders. Our results demonstrate, as in a numerical study of 3D turbulence [K. P. Iyer et al., Circulation in High Reynolds Number Isotropic Turbulence is a Bifractal, Phys. Rev. X 9, 041006 (2019).PRXHAE2160-330810.1103/PhysRevX.9.041006], that, in terms of circulation, turbulent flows exhibit a simpler behavior than velocity increments, as the latter are multifractals.

Towards Real-Time Sleep Stage Prediction and Online Calibration Based on Architecturally Switchable Deep Learning Models.

Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle both precisely offline sleep staging, and online sleep stages prediction and calibration is proposed. For offline analysis, the proposed network coordinates sequence broadening module (SBM), sequential CNN (SCNN), squeeze and excitation (SE) block, and sequence consolidation module (SCM) to balance the operational efficiency of the network and the comprehensive feature extraction. For online analysis, only SCNN and SE are involved in predicting the sleep stage within a short-time segment of the recordings. Once more than two successive segments have disparate predictions, the calibration mechanism will be triggered, and contextual information will be involved. In addition, to investigate the appropriate time of the segment that is suitable to predict a sleep stage, segments with five-second, three-second, and two-second data are analyzed. The performance of SwSleepNet is validated on two publicly available datasets Sleep-EDF Expanded and Montreal Archive of Sleep Studies (MASS), and one clinical dataset Huashan Hospital Fudan University (HSFU), with the offline accuracy of 84.5%, 86.7%, and 81.8%, respectively, which outperforms the state-of-the-art methods. Additionally, for the online sleep staging, the dedicated calibration mechanism allows SwSleepNet to achieve high accuracy over 80% on three datasets with the short-time segments, demonstrating the robustness and stability of SwSleepNet. This study presents a real-time sleep staging architecture, which is expected to pave the way for accurate sleep regulation and intervention.

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements.

Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson's disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. OBJECTIVE: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. METHODS AND PROCEDURES: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. RESULTS: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from -50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. CONCLUSION: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson's disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.

A Sequential End-to-End Neonatal Sleep Staging Model with Squeeze and Excitation Blocks and Sequential Multi-Scale Convolution Neural Networks.

Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This limits the application of these methods to multiple scenarios. In this paper, a sequential end-to-end sleep staging model, SeqEESleepNet, which is competent for parallelly processing sequential epochs and has a fast training rate to adapt to different scenarios, is proposed. SeqEESleepNet consists of a sequence epoch generation (SEG) module, a sequential multi-scale convolution neural network (SMSCNN) and squeeze and excitation (SE) blocks. The SEG module expands independent epochs into sequential signals, enabling the model to learn the temporal information between sleep stages. SMSCNN is a multi-scale convolution neural network that can extract both multi-scale features and temporal information from the signal. Subsequently, the followed SE block can reassign the weights of features through mapping and pooling. Experimental results exhibit that in a clinical dataset, the proposed method outperforms the state-of-the-art approaches, achieving an overall accuracy, F1-score, and Kappa coefficient of 71.8%, 71.8%, and 0.684 on a three-class classification task with a single channel EEG signal. Based on our overall results, we believe the proposed method could pave the way for convenient multi-scenario neonatal sleep staging methods.

The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods.

The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and clean prefrontal EEG signal were processed to obtain EOG signals coupled with different EEG signal contents. Afterwards, the coupled EOG signals were fed into a hierarchical neural network, including a convolutional neural network and recurrent neural network for automatic sleep staging. Finally, an exploration was performed using two public datasets and one clinical dataset. The results showed that using a coupled EOG signal could achieve an accuracy of 80.4%, 81.1%, and 78.9% for the three datasets, slightly better than the accuracy of sleep staging using the EOG signal without coupled EEG. Thus, an appropriate content of coupled EEG signal in an EOG signal improved the sleep staging results. This paper provides an experimental basis for sleep staging with EOG signals.

Physiological measurements for driving drowsiness: A comparative study of multi-modality feature fusion and selection.

A large number of traffic accidents were caused by drowsiness while driving. In-vehicle alert system based on physiological signals was one of the most promising solutions to monitor driving fatigue. However, different physiological modalities can be used, and many relative studies compared different modalities without considering the implementation feasibility of portable or wearable devices. Moreover, evaluations of each modality in previous studies were based on inconsistent choices of fatigue label and signal features, making it hard to compare the results of different studies. Therefore, the modality comparison and fusion for continuous drowsiness estimation while driving was still unclear. This work sought to comprehensively compare widely-used physiological modalities, including forehead electroencephalogram (EEG), electrooculogram (EOG), R-R intervals (RRI) and breath, in a hardware setting feasible for portable or wearable devices to monitor driving fatigue. Moreover, a more general conclusion on modality comparison and fusion was reached based on the regression of features or their combinations and the awake-to-drowsy transition. Finally, the feature subset of fused modalities was produced by feature selection method, to select the optimal feature combination and reduce computation consumption. Considering practical feasibility, the most effective combination with the highest correlation coefficient was using forehead EEG or EOG, along with RRI and RRI-derived breath. If more comfort and convenience was required, the combination of RRI and RRI-derived breath was also promising.

Maskless Fabrication of Highly Conductive and Ultrastretchable Liquid Metal Features through Selective Laser Activation.

In the rising field of stretchable electronics, liquid metals are ideal candidate conductors with metallic conductivity and intrinsic deformability. The complex patterning methods of liquid metal features have limited their widespread applications. In this study, we report a maskless fabrication approach for the facile and scalable patterning of liquid metal conductors on an elastomer substrate. Laser-activated patterns are employed as versatile templates to define arbitrary liquid metal patterns. The as-prepared liquid metal features show an excellent conductivity of 3.72 × 104 S/cm, a high resolution of 70 µm, ultrahigh stretchability of up to 1000% strain, and electromechanical durability. The practical suitability of liquid metal conductors is demonstrated by fabricating a stretchable light-emitting diode (LED) matrix and a smart sensing glove. The maskless fabrication technique introduced here allows versatile patterning of liquid metal conductors with affordable costs, which may stimulate a broad range of applications in stretchable electronic devices and systems.

Overview of a Sleep Monitoring Protocol for a Large Natural Population.

A standard operating procedure for studying the sleep phenotypes in a large population cohort is proposed. It is intended for academic researchers in investigating the sleep phenotypes in conjunction with the clinical sleep disorders assessment guidelines. The protocol refers to the definitive American Academy of Sleep Medicine (AASM) manual for setting polysomnography (PSG) technical specifications, scoring of sleep and associated events, etc. On this basis, it not only provides a standardized procedure of sleep interview, sleep-relevant questionnaires, and laboratory-based PSG test, but also offers a comprehensive process of sleep data analysis, phenotype extraction, and data storage. Both the objective sleep data recorded by PSG test and subjective sleep information obtained by the sleep interview and sleep questionnaires are involved in the data acquisition procedure. Subsequently, sleep phenotypes can be characterized by observable/inconspicuous physiological patterns during sleep from PSG test or can be marked by sleeping habits like sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, daytime dysfunction, etc., from sleep interview or questionnaires derived. In addition, solutions to the problems that may be encountered during the protocol are summarized and addressed. With the protocol, it can significantly improve scientific research efficiency and reduce unnecessary workload in large population cohort studies. Moreover, it is also expected to provide a valuable reference for researchers to conduct systematic sleep research.

MS-HNN: Multi-Scale Hierarchical Neural Network With Squeeze and Excitation Block for Neonatal Sleep Staging Using a Single-Channel EEG.

Most existing neonatal sleep staging appro- aches applied multiple EEG channels to obtain good performance. However, it potentially increased the computational complexity and led to an increased risk of skin disruption to neonates during data acquisition. In this paper, a multi-scale hierarchical neural network (MS-HNN) with a squeeze and excitation (SE) block for neonatal sleep staging is presented in this study on the basis of a single EEG channel. MS-HNN composes of multi-scale convolutional neural network (MSCNN), temporal information learning (TIL) module, and squeeze and excitation (SE) block. MSCNN can extract features from different scales and frequencies, and TIL module can acquire the transition information among adjacent stages. In addition, for these extracted features, SE block can selectively concentrate on informative features and weaken redundant features for achieving better performance. The proposed approach was validated on a clinical dataset involving 64 neonates from the Children's Hospital of Fudan University (CHFU). The proposed network achieves an accuracy of 75.4% and 76.5% for three-class automatic neonatal sleep staging with the single-EEG channel and the eight-EEG channels, respectively. The experimental results show that the proposed method can maintain good performance by making full use of the information in the single channel while reducing the channels to control the computational overhead.

Magnetron sputtering system for depositing boron carbide film use as neutron detection.

Boron carbide (B4C) films used as neutron conversion layers were investigated in this paper to replace the traditional 3He detectors due to their shortage. A magnetron sputtering system was developed for depositing large-size B4C films with the 1500 × 400 mm2 uniform-area. B4C films at the micron scale were deposited on aluminum (Al), float glass (SiO2), and silicon (Si) substrates with an inserting adhesion layer. The key characteristics, including surface morphology, thickness nonuniformity, purity, and neutron efficiency of B4C films, were characterized using atomic force microscopy, scanning electron microscopy, grazing incidence x-ray reflectivity, x-ray photoelectron spectroscopy, and neutron radiation metrology. The experimental results indicate that the deposition thickness nonuniformity across a 1500 × 400 mm2 area was better than ±3%. The stoichiometric ratio of boron atoms and carbon atoms (B/C) is 5.18, with 6 at. % O and 0.79 at. % N concentrations. The measured neutron detection efficiency of a 3 µm 10B4C film for 25 meV neutrons was 3.3 ± 0.3(sys)%, which is close to the simulated results (3.4%). The results show that the B4C neutron conversion layer is a promising substitute for 3He for neutron detection in the future.

A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information.

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signal variations within epoch. To address these issues, a novel deep learning architecture named segmented attention network (SAN) is proposed in this paper. The architecture can be divided into feature extraction (FE) and time sequence encoder (TSE). The FE module consists of multiple multiscale CNN (MMCNN) and residual squeeze and excitation block (SE block). The former extracts features from multiple equal-length EEG segments and the latter reinforced the features. The TSE module based on a multi-head attention mechanism could capture the temporal information in the features extracted by FE module. Noteworthy, in SAN, we replaced the RNN module with a TSE module for temporal learning and made the network faster. The evaluation of the model was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDFX, and one clinical dataset from Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed model achieved the accuracy of 85.5%, 86.4%, 82.5% on Sleep-EDFX, MASS and HSFU, respectively. The experimental results exhibited favorable performance and consistent improvements of SAN on different datasets in comparison with the state-of-the-art studies. It also proved the necessity of sleep staging by integrating the local characteristics within epochs and adjacent informative features among epochs.

MaskSleepNet: A Cross-Modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging.

Deep learning methods have become an important tool for automatic sleep staging in recent years. However, most of the existing deep learning-based approaches are sharply constrained by the input modalities, where any insertion, substitution, and deletion of input modalities would directly lead to the unusable of the model or a deterioration in the performance. To solve the modality heterogeneity problems, a novel network architecture named MaskSleepNet is proposed. It consists of a masking module, a multi-scale convolutional neural network (MSCNN), a squeezing and excitation (SE) block, and a multi-headed attention (MHA) module. The masking module consists of a modality adaptation paradigm that can cooperate with modality discrepancy. The MSCNN extracts features from multiple scales and specially designs the size of the feature concatenation layer to prevent invalid or redundant features from zero-setting channels. The SE block further optimizes the weights of the features to optimize the network learning efficiency. The MHA module outputs the prediction results by learning the temporal information between the sleeping features. The performance of the proposed model was validated on two publicly available datasets, Sleep-EDF Expanded (Sleep-EDFX) and Montreal Archive of Sleep Studies (MASS), and a clinical dataset, Huashan Hospital Fudan University (HSFU). The proposed MaskSleepNet can achieve favorable performance with input modality discrepancy, e.g. for single-channel EEG signal, it can reach 83.8%, 83.4%, 80.5%, for two-channel EEG+EOG signals it can reach 85.0%, 84.9%, 81.9% and for three-channel EEG+EOG+EMG signals, it can reach 85.7%, 87.5%, 81.1% on Sleep-EDFX, MASS, and HSFU, respectively. In contrast the accuracy of the state-of-the-art approach which fluctuated widely between 69.0% and 89.4%. The experimental results exhibit that the proposed model can maintain superior performance and robustness in handling input modality discrepancy issues.

Towards an automatic narcolepsy detection on ambiguous sleep staging and sleep transition dynamics joint model.

Objective. Mixing/dissociation of sleep stages in narcolepsy adds to the difficulty in automatic sleep staging. Moreover, automatic analytical studies for narcolepsy and multiple sleep latency test (MSLT) have only done automatic sleep staging without leveraging the sleep stage profile for further patient identification. This study aims to establish an automatic narcolepsy detection method for MSLT.Approach.We construct a two-phase model on MSLT recordings, where ambiguous sleep staging and sleep transition dynamics make joint efforts to address this issue. In phase 1, we extract representative features from electroencephalogram (EEG) and electrooculogram (EOG) signals. Then, the features are input to an EasyEnsemble classifier for automatic sleep staging. In phase 2, we investigate sleep transition dynamics, including sleep stage transitions and sleep stages, and output likelihood of narcolepsy by virtue of principal component analysis (PCA) and a logistic regression classifier. To demonstrate the proposed framework in clinical application, we conduct experiments on 24 participants from the Children's Hospital of Fudan University, considering ten patients with narcolepsy and fourteen patients with MSLT negative.Main results.Applying the two-phase leave-one-subject-out testing scheme, the model reaches an accuracy, sensitivity, and specificity of 87.5%, 80.0%, and 92.9% for narcolepsy detection. Influenced by disease pathology, accuracy of automatic sleep staging in narcolepsy appears to decrease compared to that in the non-narcoleptic population.Significance.This method can automatically and efficiently distinguish patients with narcolepsy based on MSLT. It probes into the amalgamation of automatic sleep staging and sleep transition dynamics for narcolepsy detection, which would assist clinic and neuroelectrophysiology specialists in visual interpretation and diagnosis.

The Masking Impact of Intra-Artifacts in EEG on Deep Learning-Based Sleep Staging Systems: A Comparative Study.

Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches. Whether intra-artifacts, originated from the eye movement, chin muscle firing, or heart beating, etc., in EEG signals would lead to a positive or a negative masking effect on deep learning-based sleep staging systems was investigated in this paper. We systematically analyzed several traditional pre-processing methods involving fast Independent Component Analysis (FastICA), Information Maximization (Infomax), and Second-order Blind Source Separation (SOBI). On top of these methods, a SOBI-WT method based on the joint use of the SOBI and Wavelet Transform (WT) is proposed. It offered an effective solution for suppressing artifact components while retaining residual informative data. To provide a comprehensive comparative analysis, these pre-processing methods were applied to eliminate the intra-artifacts and the processed signals were fed to two ready-to-use deep learning models, namely two-step hierarchical neural network (THNN) and SimpleSleepNet for automatic sleep staging. The evaluation was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDF Expanded, and a clinical dataset that was collected in Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed SOBI-WT method increased the accuracy from 79.0% to 81.3% on MASS, 83.3% to 85.7% on Sleep-EDF Expanded, and 75.5% to 77.1% on HSFU compared with the raw EEG signal, respectively. Experimental results demonstrate that the intra-artifacts bring out a masking negative impact on the deep learning-based sleep staging systems and the proposed SOBI-WT method has the best performance in diminishing this negative impact compared with other artifact elimination methods.

Unsupervised Domain Adaptation by Statistics Alignment for Deep Sleep Staging Networks.

Deep sleep staging networks have reached top performance on large-scale datasets. However, these models perform poorer when training and testing on small sleep cohorts due to data inefficiency. Transferring well-trained models from large-scale datasets (source domain) to small sleep cohorts (target domain) is a promising solution but still remains challenging due to the domain-shift issue. In this work, an unsupervised domain adaptation approach, domain statistics alignment (DSA), is developed to bridge the gap between the data distribution of source and target domains. DSA adapts the source models on the target domain by modulating the domain-specific statistics of deep features stored in the Batch Normalization (BN) layers. Furthermore, we have extended DSA by introducing cross-domain statistics in each BN layer to perform DSA adaptively (AdaDSA). The proposed methods merely need the well-trained source model without access to the source data, which may be proprietary and inaccessible. DSA and AdaDSA are universally applicable to various deep sleep staging networks that have BN layers. We have validated the proposed methods by extensive experiments on two state-of-the-art deep sleep staging networks, DeepSleepNet+ and U-time. The performance was evaluated by conducting various transfer tasks on six sleep databases, including two large-scale databases, MASS and SHHS, as the source domain, four small sleep databases as the target domain. Thereinto, clinical sleep records acquired in Huashan Hospital, Shanghai, were used. The results show that both DSA and AdaDSA could significantly improve the performance of source models on target domains, providing novel insights into the domain generalization problem in sleep staging tasks.

Intrinsically stretchable electronics with ultrahigh deformability to monitor dynamically moving organs.

Intrinsically stretchable electronics represent an attractive platform for next-generation implantable devices by reducing the mechanical mismatch and the immune responses with biological tissues. Despite extensive efforts, soft implantable electronic devices often exhibit an obvious trade-off between electronic performances and mechanical deformability because of limitations of commonly used compliant electronic materials. Here, we introduce a scalable approach to create intrinsically stretchable and implantable electronic devices featuring the deployment of liquid metal components for ultrahigh stretchability up to 400% tensile strain and excellent durability against repetitive deformations. The device architecture further shows long-term stability under physiological conditions, conformal attachments to internal organs, and low interfacial impedance. Successful electrophysiological mapping on rapidly beating hearts demonstrates the potential of intrinsically stretchable electronics for widespread applications in health monitoring, disease diagnosis, and medical therapies.

Multi-Objective Evolutionary Federated Learning.

Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices. However, compared with the traditional centralized approach, the federated setting consumes considerable communication resources of the clients, which is indispensable for updating global models and prevents this technique from being widely used. In this paper, we aim to optimize the structure of the neural network models in federated learning using a multi-objective evolutionary algorithm to simultaneously minimize the communication costs and the global model test errors. A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks. Experimental results on both multilayer perceptrons and convolutional neural networks indicate that the proposed optimization method is able to find optimized neural network models that can not only significantly reduce communication costs but also improve the learning performance of federated learning compared with the standard fully connected neural networks.

Maskless Patterning of Biodegradable Conductors by Selective Laser Sintering of Microparticle Inks and Its Application in Flexible Transient Electronics.

Biodegradable electronic devices are able to break down into benign residues after their service life, which may effectively alleviate the environmental impacts as a consequence of the proliferation of consumer electronic technology. The widespread adaptation to biodegradable systems is currently impeded by the lack of economic fabrication techniques for functional devices. Here, a facile approach to generate a biodegradable conductor is developed based on selective laser sintering of zinc and iron microparticle ink. The sintering process is effective to convert naturally oxidized microparticles into interconnected conductors. Arbitrary conductive features are readily created over flexible biodegradable substrates under ambient conditions, which exhibits excellent conductivity (∼2 × 106 S m-1), low sheet resistance (∼0.64 Ω â–¡ - 1), fine feature resolution (∼45 µm), and mechanical flexibility. The practical suitability is demonstrated by fabricating a miniaturized near-field communication tag with the dimension to mount on the fingernail. The methodology is further extended to create a metallic grid as a biodegradable transparent electrode with low sheet resistance (2.5 Ω â–¡-1) and high optical transmittance (96%), which is employed as an epidermal transparent heater for thermotherapy. Maskless patterning of biodegradable conductors may find a broad range of applications in environment friendly gadgets and implantable medical devices.

Bright Stretchable Electroluminescent Devices based on Silver Nanowire Electrodes and High-k Thermoplastic Elastomers.

Stretchable electroluminescent device is a compliant form of light-emitting device to expand the application areas of conventional optoelectronics on rigid wafers. Currently, practical implementations are impeded by the high operating voltage required to achieve sufficient brightness. In this study, we report the fabrication of an intrinsically stretchable electroluminescent device based on silver nanowire electrodes and high-k thermoplastic elastomers. The device exhibits a bright emission with a low driving voltage by using polar elastomer as a dielectric matrix of the electroluminescent layer. Highly stretchable silver nanowire electrodes contribute to the exceptional elasticity and durability of the device in spite of bending, stretching, twisting, puncturing, and cutting. Stretchable electroluminescent devices developed here may find potential uses in wearable displays, deformable lightings, and soft robotics.