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Noise in existing quantum processors only enables an approximation to ideal quantum computation. However, these approximations can be vastly improved by error mitigation, for the computation of expectation values, as shown by small-scale experimental demonstrations. However, the practical scaling of these methods to larger system sizes remains unknown. Here, we demonstrate the utility of zero-noise extrapolation for relevant quantum circuits using up to 26 qubits, circuit depths of 60, and 1080 CNOT gates. We study the scaling of the method for canonical examples of product states and entangling Clifford circuits of increasing size, and extend it to the quench dynamics of 2-D Ising spin lattices with varying couplings. We show that the efficacy of the error mitigation is greatly enhanced by additional error suppression techniques and native gate decomposition that reduce the circuit time. By combining these methods, we demonstrate an accuracy in the approximate quantum simulation of the quench dynamics that surpasses the classical approximations obtained from a state-of-the-art 2-D tensor network method. These results reveal a path to a relevant quantum advantage with noisy, digital, quantum processors., 7 pages, 3 figures

Quantum error correction offers a promising path for performing high fidelity quantum computations. Although fully fault-tolerant executions of algorithms remain unrealized, recent improvements in control electronics and quantum hardware enable increasingly advanced demonstrations of the necessary operations for error correction. Here, we perform quantum error correction on superconducting qubits connected in a heavy-hexagon lattice. We encode a logical qubit with distance three and perform several rounds of fault-tolerant syndrome measurements that allow for the correction of any single fault in the circuitry. Using real-time feedback, we reset syndrome and flag qubits conditionally after each syndrome extraction cycle. We report decoder dependent logical error, with average logical error per syndrome measurement in Z(X)-basis of ~0.040 (~0.088) and ~0.037 (~0.087) for matching and maximum likelihood decoders, respectively, on leakage post-selected data.

Recent advances in monocular 3D detection leverage a depth estimation network explicitly as an intermediate stage of the 3D detection network. Depth map approaches yield more accurate depth to objects than other methods thanks to the depth estimation network trained on a large-scale dataset. However, depth map approaches can be limited by the accuracy of the depth map, and sequentially using two separated networks for depth estimation and 3D detection significantly increases computation cost and inference time. In this work, we propose a method to boost the RGB image-based 3D detector by jointly training the detection network with a depth prediction loss analogous to the depth estimation task. In this way, our 3D detection network can be supervised by more depth supervision from raw LiDAR points, which does not require any human annotation cost, to estimate accurate depth without explicitly predicting the depth map. Our novel object-centric depth prediction loss focuses on depth around foreground objects, which is important for 3D object detection, to leverage pixel-wise depth supervision in an object-centric manner. Our depth regression model is further trained to predict the uncertainty of depth to represent the 3D confidence of objects. To effectively train the 3D detector with raw LiDAR points and to enable end-to-end training, we revisit the regression target of 3D objects and design a network architecture. Extensive experiments on KITTI and nuScenes benchmarks show that our method can significantly boost the monocular image-based 3D detector to outperform depth map approaches while maintaining the real-time inference speed., Comment: Accepted by IEEE Transaction on Intelligent Transportation System (T-ITS)

Natural products and their derivatives historically represent alternatives to conventional synthetic molecules for pharmacotherapy, ranging from cancer chemotherapeutics to cosmetic ingredients that exert anti-aging activities. Cellular senescence is considered a main driver of skin aging, yet natural products that target skin senescence in a specific manner are not thoroughly explored. Here, we performed a focused compound screen to identify natural products that exert anti-senescence effects. We found that

Metal-oxide semiconductors are considered promising alternative materials in the field of flat panel display industry due to their advantages, such as high mobility, transparency, uniformity, low production cost, and large-area processability.

We demonstrate a modified stochastic gradient (Tiki-Taka v2 or TTv2) algorithm for deep learning network training in a cross-bar array architecture based on ReRAM cells. There have been limited discussions on cross-bar arrays for training applications due to the challenges in the switching behavior of nonvolatile memory materials. TTv2 algorithm is known to overcome the device non-idealities for deep learning training. We demonstrate the feasibility of the algorithm for a linear regression task using 1R and 1T1R ReRAM devices. Using the measured device properties, we project the performance of a long short-term memory (LSTM) network with 78 K parameters. We show that TTv2 algorithm relaxes the criteria for symmetric device update response. In addition, further optimization of the algorithm increases noise robustness and significantly reduces the required number of states, thereby drastically improving the model accuracy even with non-ideal devices and achieving the test error close to that of the conventional learning algorithm with an ideal device.

We demonstrate a novel process for building a Resistive RAM (ReRAM) stack which reduces the forming voltage ( $\text{V}_{\textit {form}}$ ) and increases the switching resistance, both characteristics that are important ingredients for the use of ReRAM in scalable analog compute for AI. Utilizing this process, we explore analog switching characteristics above 100k $\Omega $ and demonstrate 4-bit programming at Rmax $=1\text{M}\Omega $ . Utilizing the same writing characteristics, CIFAR-10 inference simulation shows 90% accuracy, comparable to the full precision model accuracy.

Despite the importance of structure and properties in organic mixed conductors, there exist very few material systems where the effects of relative crystallinity, crystallite size, and doping conce...

Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the promising deep learning architectures that shows a significant computing gain over the conventional processors. To investigate the feasibility of the architecture, we examine non-idealities and their impact on the performance. Specifically, we study the impact of failed cells due to the initialization process of the resistive memory based cross-bar array. Unlike the conventional memory array, individual memory elements cannot be rerouted and, thus, may have a critical impact on model accuracy. We categorize the possible failures and propose hardware implementation that minimizes catastrophic failures. Such hardware optimization bounds the possible logical value of the failed cells and gives us opportunities to compensate for the loss of accuracy via off-line training. By introducing the random weight defects during the training, we show that the model becomes more resilient on the device initialization failures, therefore, less prone to degrade the inference performance due to the failed devices. Our study sheds light on the hardware and software co-optimization procedure to cope with potentially catastrophic failures in the cross-bar array., 17 pages, 7 figures

A brewers’ spent grain (BSG) based dielectric material was used in organic field-effect transistors (OFETs). The fabricated devices showed a maximum hole mobility of 2.30 cm2 V−1 s−1.

Owing to outstanding electrical/electrochemical performance, operational stability, mechanical flexibility, and decent biocompatibility, organic mixed ionic-electronic conductors have shown great potential as implantable electrodes for neural recording/stimulation and as active channels for signal switching/amplifying transistors. Nonetheless, no studies exist on the general design rule for high-performance electrochemical diodes, which are essential for highly functional circuit architectures. Herein, we report on generalizable electrochemical diodes with very high current density over 30 kAcm-2 by introducing an asymmetric active layer based on organic mixed ionic-electronic conductors. The underlying mechanism on polarity-sensitive balanced ionic doping/dedoping is elucidated by numerical device analysis and in operando spectroelectrochemical potential mapping, while the general material requirements for electrochemical diode operation are deduced using various types of conjugated polymers. In parallel, analog signal rectification and digital logic processing circuits are successfully demonstrated to show the broad impact of organic electrochemical diode-incorporated circuits. We expect that organic electrochemical diodes will play vital roles in realizing multifunctional soft bioelectronic circuitry in combination with organic electrochemical transistors. This article is protected by copyright. All rights reserved.

Arbitrarily long quantum computations require quantum memories that can be repeatedly measured without being corrupted. Here, we preserve the state of a quantum memory, notably with the additional use of flagged error events. All error events were extracted using fast, mid-circuit measurements and resets of the physical qubits. Among the error decoders we considered, we introduce a perfect matching decoder that was calibrated from measurements containing up to size-4 correlated events. To compare the decoders, we used a partial post-selection scheme shown to retain ten times more data than full post-selection. We observed logical errors per round of $2.2\pm0.1\times10^{-2}$ (decoded without post-selection) and $5.1\pm0.7\times10^{-4}$ (full post-selection), which was less than the physical measurement error of $7\times10^{-3}$ and therefore surpasses a pseudo-threshold for repeated logical measurements., 16 pages, 14 figures, 5 tables, for peer-review

관계가 행동에 미치는 영향의 중요성을 인식한 본 연구는 사회관계망 분석을 적용하여 청소년의 또래관계를 측정한 다음 또래관계와 음주‧흡연 경험 간의 관계를 분석하였다. 사회관계망분석은 청소년이 실제로 또래들과 맺는 관계의 유형과 또래들 사이에서 차지하는 위치에 관한 정보를 제공해준다. 본 연구에는 수도권에 소재한 중학교 2개교 1학년에 재학 중인 443명의 학생이 참여하였다. 또래관계에 대한 사회관계망 분석을 실시하여 4가지 중심성을 계산하였다. 연결 중심성은 청소년과 직접 연결되어 있는 친구의 수를, 근접 중심성은 관계망 내 다른 청소년과 직, 간접적으로 근접한 정도를, 매개 중심성은 친구들을 연결해 주는 역할을 수행하는 정도를, 고유벡터 중심성은 영향력 있는 친구들과 연결되어 있는 정도를 측정한다. 로지스틱 회귀분석에서 이들 중심성 지표 중 외향 연결 중심성과 외향 근접 중심성이 청소년 음주 또는 흡연 경험과 유의미한 관계를 보였다. 외향 연결 중심성은 음주 또는 흡연 경험과 부적관계를 외향 근접 중심성은 음주 또는 흡연 경험과 정적 관계를 보였다. 다시 말해서 자신과 친한 친구를 더 많이 지목한 청소년일수록 음주 또는 흡연 경험이 적었고 또래관계망 내 친구들과 가까운 청소년일수록 음주 또는 흡연 경험이 더 많았다. 본 연구결과를 토대로 청소년 음주 또는 흡연의 예방을 위해서 사회관계망 분석 결과를 활용하는 방안을 제시하였다.

Despite possible toxicity issues, chemical reduction or non-polarizable electrodes incorporated with highly reactive chemical species have been utilized to control the operational characteristics of organic electrochemical transistors (OECTs) for bioelectronic interfacing applications. In this study, we demonstrate that crosslinking between highly conductive poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and biocompatible nonconductive polyvinyl alcohol (PVA) effectively modulates the mixed conducting properties of PEDOT:PSS. The PEDOT:PSS–PVA films prepared by simple PEDOT:PSS and PVA blending at various ratios, spin-coating, crosslinking, and sulfuric acid treatment were comprehensively studied using optical spectroscopy, x-ray diffraction, atomic force microscopy, and electrical/electrochemical device characterizations. With PVA contents up to 20 wt.%, the resultant PEDOT:PSS–PVA-based OECTs showed a linear threshold voltage shift with a marginal loss of transconductance, suggesting an effective modulation of the peak transconductance gate voltage. Furthermore, the PVA content also affects the ion transport dynamics, which is related to the crystallite size and ionic functional group density in the PEDOT:PSS–PVA system. The detailed mechanism of delicately controlled mixed conduction in the PEDOT:PSS–PVA system is proposed from the perspective of structure-property relations.

In this paper we demonstrate a novel methodology to electrically test and characterize resistive random-access memory (RRAM) single bit devices for deep learning application. We extract critical device performance metrics for validating and optimizing fabrication processes which feed into yield learning. We adopt the algorithm-based bias condition search methodology and extract forming and switching voltage parameters without overdriving the devices. This test methodology can be used for Technology Development Learning Cycle in a research and development environment.

Predicting the frost depth of soils in pavement design is critical to the sustainability of the pavement because of its mechanical vulnerability to frozen-thawed soil. The reliable prediction of frost depth can be challenging due to the high uncertainty of frost depth and the unavailability of geotechnical properties needed to use the available empirical- and analytical-based equations in literature. Therefore, this study proposed a new framework to predict the frost depth of soil below the pavement using eight machine learning (ML) algorithms (five single ML algorithms and three ensemble learning algorithms) without geotechnical properties. Among eight ML models, the hyperparameter-tuned gradient boosting model showed the best performance with the coefficient of determination (R2) = 0.919. Furthermore, it was also shown that the developed ML model can be utilized in the prediction of several levels of frost depth and assessing the sensitivity of pavement-related predictors for predicting the frost depth of soils.

Two new glycolated semiconducting polymers PgBT(F)2gT and PgBT(F)2gTT of differing backbone curvatures were designed and synthesised for application as p‐type accumulation mode organic electrochemical transistor (OECT) materials. Both polymers demonstrated stable and reversible oxidation, accessible within the aqueous electrochemical window, to generate polaronic charge carriers. OECTs fabricated from PgBT(F)2gT featuring a curved backbone geometry attained a higher volumetric capacitance of 170 F cm−3. However, PgBT(F)2gTT with a linear backbone displayed overall superior OECT performance with a normalised peak transconductance of 3.00×104 mS cm−1, owing to its enhanced order, expediting the charge mobility to 0.931 cm2 V−1 s−1., The targeted design of backbone geometries is shown to influence the p‐type accumulation mode organic electrochemical transistor (OECT) performance of two glycolated donor–acceptor conjugated polymers, PgBT(F)2gT and PgBT(F)2gTT.

The continuously growing demands in high-density memories drive the rapid development of advanced memory technologies. In this work, we investigate the HfOx-based resistive switching memory (ReRAM) stack structure at nanoscale by high resolution TEM (HRTEM) and energy dispersive X-ray spectroscopy (EDX) before and after the forming process. Two identical ReRAM devices under different electrical test conditions are investigated. For the ReRAM device tested under a regular voltage bias, material redistribution and better bottom electrode contact are observed. In contrast, for the ReRAM device tested under an opposite voltage bias, different microstructure change occurs. Finite element simulations are performed to study the temperature distributions of the ReRAM cell with filaments formed at various locations relative to the bottom electrode. The applied electric field as well as the thermal heat are the driving forces for the microstructure and chemical modifications of the bottom electrode in ReRAM deceives.

Construction in the extreme cold regions becomes one of the important businesses in the world. Geophysical surveys, which are effective in providing consecutive ground information, have been widely applied to the civil engineering and environmental sectors. The permafrost area covers about 14 percent of the world’s land area and the global construction market for such area is rapidly expanding. Where developed countries have already recognized the need for research of the coldest places and therefore have invested heavily in technology development, the non-arctic countries are still developing and related research has rarely been performed. The purpose of this study is to design the GIS (Geophysics Information System) platform for a construction environment with a 2,000 km-class pipeline (overland) operated under the permafrost temperature conditions (−40°C∼+20°C). The GIS platform when subjected to geotechnical engineering provides an understanding of the frozen ground and information about the geo-environment of the extreme cold regions. This paper shows the characteristics and procedures of Environmental Impact Assessment (EIA) for developing Arctic resources, and reviews how to construct a system for the systematic management of the necessary information to consider building the GIS platform. In addition, this study introduces the GIS algorithm to build the route for the pipeline path, and factors which should be considered first are safety, environment, economic feasibility and technical feasibility.

Cold regions, such as Alaska, Russia and Canada, get attention from many countries due to the tremendous amount of natural resources which are buried there. An accurate evaluation of the frozen ground is very important because the behavior of the active layer is greatly affected by the soil characteristics and water content in the active layer. It is necessary for developing a construction technology for pipelines in cold regions. This study has two objectives: 1) First one is to evaluate the characteristics of a newly-produced insulated aggregate and 2) the other one is to check the applicability of insulated aggregate. A series of laboratory experiments (specific gravity test, sieve analysis test, direct shear test, test for abrasion of coarse and aggregates by use of the Los Angeles machine) were performed to estimate the characteristics of the newly-produced insulated aggregate. In addition, the laboratory chamber tests were carried out to evaluate the applicability of frozen soil behavior using the newly-produced insulated aggregate. The chamber tests were conducted to check the laboratory model surrounded by soil mixing the insulated aggregate and ordinary soil in order to prevent the damage of structures such as pipelines due to the ground being frozen. For the laboratory chamber tests, the extreme cold engineering laboratory was built within the Yeon Cheon SOC Demonstration Research Center, of the Korea Institute of Construction Technology. The performance of the frozen ground which was installed with the insulated aggregate using vinyl was evaluated through monitoring the time-dependent distribution of temperature and earth-pressure.

Senescent fibroblasts progressively deteriorate the functional properties of skin tissue. Senescent cells secrete senescence-associated secretory phenotype (SASP) factor, which causes the aging of surrounding non-senescent cells and accelerates aging in the individuals. Recent findings suggested the senomorphic targeting of the SASP regulation as a new generation of effective therapeutics. We investigated whether Isatis tinctoria L. leaf extract (ITE) inhibited senescence biomarkers p53, p21CDKN1A, and p16INK4A gene expression, and SASP secretions by inhibiting cellular senescence in the replicative senescent human dermal fibroblast (RS-HDF). ITE has been demonstrated to inhibit the secretion of SASP factors in several senomorphic types by regulating the MAPK/NF-κB pathway via its inhibitory effect on mTOR. ITE suppressed the inflammatory response by inhibiting mTOR, MAPK, and IκBα phosphorylation, and blocking the nuclear translocation of NF-κB. In addition, we observed that autophagy pathway was related to inhibitory effect of ITE on cellular senescence. From these results, we concluded that ITE can prevent and restore senescence by blocking the activation and secretion of senescence-related factors generated from RS-HDFs through mTOR-NF-κB regulation.

Despite the great potential of polymer microfibers in human-friendly wearable electronics, most previous polymeric electronics have been limited to thin-film-based devices due to practical difficulties in fabricating microfibrillar devices, as well as defining the active channel dimensions in a reproducible manner. Herein, we report on conducting polymer microfiber-based organic electrochemical transistors (OECTs) and their application in single-strand fiber-type wearable ion concentration sensors. We developed a simple wet-spinning process to form very conductive poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) microfibers using aqueous sulfuric acid solutions and carefully examined their electrical/electrochemical properties. In conjunction with fabricating substrate-free PEDOT:PSS microfiber-based OECT devices, the proposed novel characterization method demonstrated that the current variation ratio can be a reliable method for evaluating the device performance for sensing ion concentrations, regardless of the actual channel dimensions. Finally, we developed single-strand fiber-type skin-mountable OECTs by introducing a source-gate hybrid electrode and demonstrated that the resultant microfiber sensors can perform real-time repetitive measurements of the ion concentration in human sweat. A wearable device that analyses sweat and could help athletes optimize their intake of fluids and electrolytes has been developed by researchers in South Korea. Wearable electronics provide a way to monitor the body around the clock, and even deliver simple healthcare solutions. Such devices need to be light, robust, and flexible enough to adapt to the wearer’s movement. Sanghyun Ju from Kyonggi University in Suwon, Myung-Han Yoon from Gwangju Institute of Science and Technology and their colleagues have made a wearable device that can measure the ion concentration in human sweat. Previous materials used in such devices required a substrate, which limited their mechanical flexibility. Instead, Ju, Yoon and the team created highly conductive microfibers, which they used to fabricate electrochemical transistors. The current through the transistor varied with ion concentration, enabling real-time measurements. In textile electronics, micro to millimeter-scaled misalignment is commonly occurred during the high-throughput and bulk-scaled textile manufacturing process, thus the exact performance control of the fiber-based active devices is very difficult in low-cost wearable electronics. In this research, we developed novel single-strand organic electrochemical transistors and proposed dimension-independent characterization method (i.e., the current variation ratio in variation of logarithmic concentration of electrolyte) for ion concentration sensing. Furthermore, we demonstrated the pseudo two-terminal transistor operation by incorporating electrochemical gate electrode onto the surface of the source electrode, leading to single-strand fiber device platform.

Due to the trade-off between their electrical/electrochemical performance and underwater stability, realizing polymer-based, high-performance direct cellular interfaces for electrical stimulation and recording has been very challenging. Herein, we developed transparent and conductive direct cellular interfaces based on a water-stable, high-performance poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) film via solvent-assisted crystallization. The crystallized PEDOT:PSS on a polyethylene terephthalate (PET) substrate exhibited excellent electrical/electrochemical/optical characteristics, long-term underwater stability without film dissolution/delamination, and good viability for primarily cultured cardiomyocytes and neurons over several weeks. Furthermore, the highly crystallized, nanofibrillar PEDOT:PSS networks enabled dramatically enlarged surface areas and electrochemical activities, which were successfully employed to modulate cardiomyocyte beating via direct electrical stimulation. Finally, the high-performance PEDOT:PSS layer was seamlessly incorporated into transparent microelectrode arrays for efficient, real-time recording of cardiomyocyte action potentials with a high signal fidelity. All these results demonstrate the strong potential of crystallized PEDOT:PSS as a crucial component for a variety of versatile bioelectronic interfaces. Cardiomyocyte cells can be cultured and made to pulse on demand using transparent polymers with good stability. Conductive thin films formed from poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) have low impedances, making them ideal for bioelectronic interfaces. But they suffer from severe fragility in aqueous environments. Myung-Han Yoon from Korea’s Gwangju Institute of Science and Technology and colleagues have made PEDOT:PSS films that show no degradation up to three weeks underwater. They achieved this by immersing the films in concentrated sulfuric acid to initiate solvent-assisted crystallization. The crystalline films had improved electrical/electrochemical properties and biocompatibility over approaches such as polymer cross-linking, and supported photolithographic patterning into microelectrode arrays. Using cardiac cells as a model, the researchers demonstrated the feasibility of modulating beating frequencies with direct electrical stimulation under 1V while simultaneously capturing real-time action potentials and calcium signals. The high performance polymer-based conductive cellular interface was developed by a solvent-assisted crystallization of PEDOT:PSS. The crystallized PEDOT:PSS(c-PEDOT:PSS) exhibited mechanical and electrical robustness over 21days as well as excellent electrical conductivity and electrochemical activities. Thanks to such advantageous properties for the cellular interfaces, the beating rates of cardiomyocytes cultured on c-PEDOT:PSS were successfully modulated through pulsed direct stimulation under 1 V. In addition, c-PEDOT:PSS incorporated Multielectrode arrays (MEAs) recorded real-time action potentials originated from cardiomyocytes with high signal fidelity. we expect c-PEDOT:PSS with high-performance and high-stability to be a promising candidate for long-term bioelectronic interface development.

Emerging memory technologies such as Resistive Memory (RRAM) have gained a lot of attention to meet the requirements of a potential analog computing element, due to its non-volatile characteristics, scalability and energy efficiency. An RRAM device typically consists of a resistive switching layer (e.g. HfO2) sandwiched between two metal electrodes. Since oxygen vacancies are critical to the functioning of the device, it is desirable to achieve residue free etching using oxygen-less plasmas, and preferably minimize exposure to ambient environment. In this work, we discuss the RRAM patterning challenges and their impact on the device characteristics including the switching/forming voltage.

To realize thermoelectric textiles that can convert body heat to electricity, fibers with excellent mechanical and thermoelectric properties are needed. Although poly(3,4‐ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is among the most promising organic thermoelectric materials, reports that explore its use for thermoelectric fibers are all but absent. Herein, the mechanical and thermoelectric properties of wet‐spun PEDOT:PSS fibers are reported, and their use in energy‐harvesting textiles is discussed. Wet‐spinning into sulfuric acid results in water‐stable semicrystalline fibers with a Young's modulus of up to 1.9 GPa, an electrical conductivity of 830 S cm−1, and a thermoelectric power factor of 30 μV m−1 K−2. Stretching beyond the yield point as well as repeated tensile deformation and bending leave the electrical properties of these fibers almost unaffected. The mechanical robustness/durability and excellent underwater stability of semicrystalline PEDOT:PSS fibers, combined with a promising thermoelectric performance, opens up their use in practical energy‐harvesting textiles, as illustrated by an embroidered thermoelectric fabric module.

[Image: see text] Because of their attractive mechanical properties, conducting polymers are widely perceived as materials of choice for wearable electronics and electronic textiles. However, most state-of-the-art conducting polymers contain harmful dopants and are only processable from solution but not in bulk, restricting the design possibilities for applications that require conducting micro-to-millimeter scale structures, such as textile fibers or thermoelectric modules. In this work, we present a strategy based on melt processing that enables the fabrication of nonhazardous, all-polymer conducting bulk structures composed of poly(3,4-ethylenedioxythiophene) (PEDOT) polymerized within a Nafion template. Importantly, we employ classical polymer processing techniques including melt extrusion followed by fiber spinning or fused filament 3D printing, which cannot be implemented with the majority of doped polymers. To demonstrate the versatility of our approach, we fabricated melt-spun PEDOT:Nafion fibers, which are highly flexible, retain their conductivity of about 3 S cm(–1) upon stretching to 100% elongation, and can be used to construct organic electrochemical transistors (OECTs). Furthermore, we demonstrate the precise 3D printing of complex conducting structures from OECTs to centimeter-sized PEDOT:Nafion figurines and millimeter-thick 100-leg thermoelectric modules on textile substrates. Thus, our strategy opens up new possibilities for the design of conducting, all-polymer bulk structures and the development of wearable electronics and electronic textiles.

Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric empirical Bayes methods. Maximum likelihood of mixture proportions has traditionally been solved using the expectation maximization (EM) algorithm, but recent work by Koenker & Mizera shows that modern convex optimization techniques -- in particular, interior point methods -- are substantially faster and more accurate than EM. Here, we develop a new solution based on sequential quadratic programming (SQP). It is substantially faster than the interior point method, and just as accurate. Our approach combines several ideas: first, it solves a reformulation of the original problem; second, it uses an SQP approach to make the best use of the expensive gradient and Hessian computations; third, the SQP iterations are implemented using an active set method to exploit the sparse nature of the quadratic subproblems; fourth, it uses accurate low-rank approximations for more efficient gradient and Hessian computations. We illustrate the benefits of our approach in experiments on synthetic data sets as well as a large genetic association data set. In large data sets (n = 1,000,000 observations, m = 1,000 mixture components), our implementation achieves at least 100-fold reduction in runtime compared with a state-of-the-art interior point solver. Our methods are implemented in Julia, and in an R package available on CRAN (see https://CRAN.R-project.org/package=mixsqp)., 28 pages, 6 figures

In this work, we investigate the statistical evolution from nano-conducting path generation to the switching-filament formation in ReRAM devices. We demonstrate the Gumbel statistics, a maxima-value distribution for switching-filament conductance as opposed to the minima-value Weibull model. In contrast to Poisson distribution for nano-path generation, we show the underlying spatial statistics is controlled by a binomial distribution as a result of filament-formation. The reorganization and conglomeration of interacting nano-paths or individual vacancies eventually leads to area-dependent single-filament formation, consistent with the Gumbel statistics. Our methodology provides the foundation for RRAM scaling as well as an essential tool for the development of RRAM technology.

Lightweight nano/microscale wearable devices that are directly attached to or worn on the human body require enhanced flexibility so that they can facilitate body movement and overall improved wearability. In the present study, a flexible poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) fiber-based sensor is proposed, which can accurately measure the amount of salt (i.e., sodium chloride) ions in sweat released from the human body or in specific solutions. This can be performed using one single strand of hair-like conducting polymer fiber. The fabrication process involves the introduction of an aqueous PEDOT:PSS solution into a sulfuric acid coagulation bath. This is a repeatable and inexpensive process for producing monolithic fibers, with a simple geometry and tunable electrical characteristics, easily woven into clothing fabrics or wristbands. The conductivity of the PEDOT:PSS fiber increases in pure water, whereas it decreases in sweat. In particular, the conductivity of a PEDOT:PSS fiber changes linearly according to the concentration of sodium chloride in liquid. The results of our study suggest the possibility of PEDOT:PSS fiber-based wearable sensors serving as the foundation of future research and development in skin-attachable next-generation healthcare devices, which can reproducibly determine the physiological condition of a human subject by measuring the sodium chloride concentration in sweat.

Insulin plays a key role in glucose homeostasis and is hence used to treat hyperglycemia, the main characteristic of diabetes mellitus. Annulohypoxylon annulatum is an inedible ball-shaped wood-rotting fungus, and hypoxylon F is one of the major compounds of A. annulatum. The aim of this study is to evaluate the effects of hypoxylonol F isolated from A. annulatum on insulin secretion in INS-1 pancreatic β-cells and demonstrate the molecular mechanisms involved. Glucose-stimulated insulin secretion (GSIS) values were evaluated using a rat insulin ELISA kit. Moreover, the expression of proteins related to pancreatic β-cell metabolism and insulin secretion was evaluated using Western blotting. Hypoxylonol F isolated from A. annulatum was found to significantly enhance glucose-stimulated insulin secretion without inducing cytotoxicity. Additionally, hypoxylonol F enhanced insulin receptor substrate-2 (IRS-2) levels and activated the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) pathway. Interestingly, it also modulated the expression of peroxisome proliferator-activated receptor γ (PPARγ) and pancreatic and duodenal homeobox 1 (PDX-1). Our findings showed that A. annulatum and its bioactive compounds are capable of improving insulin secretion by pancreatic β-cells. This suggests that A. annulatum can be used as a therapeutic agent to treat diabetes.

Topological insulators (TIs) host spin-momentum locked surface states that are inherently susceptible to magnetic proximity modulations, making them promising for nano-electronic, spintronic, and quantum computing applications. While much effort has been devoted to studying (quantum) anomalous Hall effects in magnetic magnetically doped TIs, the inherent magnetoresistance (MR) properties in magnetic proximity-coupled surface states remain largely unexplored. Here, we directly exfoliate Bi2Se3 TI flakes onto a magnetic insulator, yttrium iron garnet, and measure the MR at various temperatures. We experimentally observe an anisotropic magnetoresistance that is consistent with a magnetized surface state. Our results indicate that the TI has magnetic anisotropy out of the sample plane, which opens an energy gap between the surface states. By applying a magnetic field along any in-plane orientation, the magnetization of the TI rotates toward the plane and the gap closes. Consequently, we observe a large (∼6.5%) MR signal that is attributed to an interplay between coherent rotation of magnetization within a topological insulator and abrupt switching of magnetization in the underlying magnetic insulator.

Neuromorphic computing represents a potential paradigm shift from conventional von Neumann computing architecture and shows promise for achieving massive parallelism and power efficiency for such data-centric tasks as image recognition and language processing. Based on the concept of synaptic plasticity, human-like machine learning can be potentially realized by use of arrays of electronic synapses that function in an analogous manner to biological neurons. One of the key features of this type of computing is the ability to control synaptic weights in an analog-like fashion for use in both inference and training applications. A number of existing device technologies in non-volatile memory systems exhibit attractive characteristics for such synaptic devices.[1,2] In particular, resistive switching devices (resistive random-access memory or ReRAM) can change and store their conductance value (G) in response to electrical stimuli making them potentially enabling for deep learning applications involving synaptic weights. For ReRAM devices, HfO2-based thin films can be utilized for filamentary oxide ReRAM and are an attractive option due to their fab-friendly processing and current implementation in high-volume manufacturing. In this study, we evaluated atomic layer deposition (ALD) for the growth of HfO2 for integration in both front-end-of-line (FEOL) and back-end-of-line (BEOL) compatible test structures on 300 mm wafers in order to optimize electrical performance for use as synaptic device elements in neuromorphic architectures. The effect of oxidant in the ALD process was evaluated and it was shown that H2O outperformed O3 in terms of better uniformity and lower forming voltage. By utilizing a hydrogen-based plasma either after the deposition or inserted as an intermediate step during deposition we were able to further decrease forming voltage for a fixed dielectric thickness. Reducing deposition temperature to 200°C in conjunction with the hydrogen-based plasma treatment offered an additional tuning knob to further reduce forming voltage. Stable high-resistance switching (> 100 kΩ) with analog behavior in scaled BEOL devices was also obtained using this optimized HfO2-based ReRAM. Additionally, a tight distribution of forming voltage was obtained ensuring that 99.9999% devices in a 14 nm ReRAM module can be formed below the targeted voltage. References Kuzum et al., Nanotechnology, 24, 382001 (2013) W. Burr et al., Advanced in Physics:X, 2, 89 (2016)