Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring.

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO2, HCHO, and NH3 and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.

Polarization- and Electrode-Optimized Polyvinylidene Fluoride Films for Harsh Environmental Piezoelectric Nanogenerator Applications.

While piezoelectric nanogenerators have demonstrated the effective conversion of tiny mechanical vibrations to electricity, their performances are rarely examined under harsh environmental conditions. Here, a multilayered polyvinylidene fluoride (PVDF) film-based piezoelectric nanogenerator (ML-PENG) is demonstrated to generate considerable and stable power outputs even at extremely low temperatures and pressures, and under strong UV. Up-/down-polarized PVDF films are alternately stacked, and Ag electrodes are intercalated between the two adjacent films. At -266 °C and 10-5  Torr, the ML-PENG generates an open-circuit voltage of 1.1 V, a short-circuit current density of 8 nA cm-2 , and a power density of 4.4 nW cm-2 . The piezoelectric outputs are quite stable against prolonged illumination of UV, large temperature- and pressure-variations, and excessive mechanical vibrations. The piezoelectric power density is greatly enhanced above the freezing and glass transition temperatures of PVDF and recorded to be 10, 105, and 282 nW cm-2 at -73, 0, and 77 °C, respectively. The ML-PENG generates sufficient power to operate five light-emitting diodes by harvesting biomechanical energy under simulated Martian conditions. This work suggests that polarization- and electrode-optimized ML-PENG can serve as a reliable and economic power source in harsh and inaccessible environments like polar areas of Earth and extraterrestrial Mars.

Metal Oxide Nanorods-Based Sensor Array for Selective Detection of Biomarker Gases.

The breath gas analysis through gas phase chemical analysis draws attention in terms of non-invasive and real time monitoring. The array-type sensors are one of the diagnostic methods with high sensitivity and selectivity towards the target gases. Herein, we presented a 2 × 4 sensor array with a micro-heater and ceramic chip. The device is designed in a small size for portability, including the internal eight-channel sensor array. In2O3 NRs and WO3 NRs manufactured through the E-beam evaporator's glancing angle method were used as sensing materials. Pt, Pd, and Au metal catalysts were decorated for each channel to enhance functionality. The sensor array was measured for the exhaled gas biomarkers CH3COCH3, NO2, and H2S to confirm the respiratory diagnostic performance. Through this operation, the theoretical detection limit was calculated as 1.48 ppb for CH3COCH3, 1.9 ppt for NO2, and 2.47 ppb for H2S. This excellent detection performance indicates that our sensor array detected the CH3COCH3, NO2, and H2S as biomarkers, applying to the breath gas analysis. Our results showed the high potential of the gas sensor array as a non-invasive diagnostic tool that enables real-time monitoring.

Cellular Auxetic Structures for Mechanical Metamaterials: A Review.

Recent advances in lithography technology and the spread of 3D printers allow us a facile fabrication of special materials with complicated microstructures. The materials are called "designed materials" or "architectured materials" and provide new opportunities for material development. These materials, which owing to their rationally designed architectures exhibit unusual properties at the micro- and nano-scales, are being widely exploited in the development of modern materials with customized and improved performance. Meta-materials are found to possess superior and unusual properties as regards static modulus (axial stress divided by axial strain), density, energy absorption, smart functionality, and negative Poisson's ratio (NPR). However, in spite of recent developments, it has only been feasible to fabricate a few such meta-materials and to implement them in practical applications. Against such a backdrop, a broad review of the wide range of cellular auxetic structures for mechanical metamaterials available at our disposal and their potential application areas is important. Classified according to their geometrical configuration, this paper provides a review of cellular auxetic structures. The structures are presented with a view to tap into their potential abilities and leverage multidimensional fabrication advances to facilitate their application in industry. In this review, there is a special emphasis on state-of-the-art applications of these structures in important domains such as sensors and actuators, the medical industry, and defense while touching upon ways to accelerate the material development process.

Self-Powered Chemical Sensing Driven by Graphene-Based Photovoltaic Heterojunctions with Chemically Tunable Built-In Potentials.

Ultralow power chemical sensing is essential toward realizing the Internet of Things. However, electrically driven sensors must consume power to generate an electrical readout. Here, a different class of self-powered chemical sensing platform based on unconventional photovoltaic heterojunctions consisting of a top graphene (Gr) layer in contact with underlying photoactive semiconductors including bulk silicon and layered transition metal dichalcogenides is proposed. Owing to the chemically tunable electrochemical potential of Gr, the built-in potential at the junction is effectively modulated by absorbed gas molecules in a predictable manner depending on their redox characteristics. Such ability distinctive from bulk photovoltaic counterparts enables photovoltaic-driven chemical sensing without electric power consumption. Furthermore, it is demonstrated that the hydrogen (H2 ) sensing properties are independent of the light intensity, but sensitive to the gas concentration down to the 1 ppm level at room temperature. These results present an innovative strategy to realize extremely energy-efficient sensors, providing an important advancement for future ubiquitous sensing.

Ionic-Activated Chemiresistive Gas Sensors for Room-Temperature Operation.

The development of high performance gas sensors that operate at room temperature has attracted considerable attention. Unfortunately, the conventional mechanism of chemiresistive sensors is restricted at room temperature by insufficient reaction energy with target molecules. Herein, novel strategy for room temperature gas sensors is reported using an ionic-activated sensing mechanism. The investigation reveals that a hydroxide layer is developed by the applied voltages on the SnO2 surface in the presence of humidity, leading to increased electrical conductivity. Surprisingly, the experimental results indicate ideal sensing behavior at room temperature for NO2 detection with sub-parts-per-trillion (132.3 ppt) detection and fast recovery (25.7 s) to 5 ppm NO2 under humid conditions. The ionic-activated sensing mechanism is proposed as a cascade process involving the formation of ionic conduction, reaction with a target gas, and demonstrates the novelty of the approach. It is believed that the results presented will open new pathways as a promising method for room temperature gas sensors.

A Ru-Pt alloy electrode to suppress leakage currents of dynamic random-access memory capacitors.

Rutile TiO2, a high temperature phase, has attracted interest as a capacitor dielectric in dynamic random-access memories (DRAMs). Despite its high dielectric constant of >80, large leakage currents caused by a low Schottky barrier height at the TiO2/electrode interface have hindered the use of rutile TiO2 as a commercial DRAM capacitor. Here, we propose a new Ru-Pt alloy electrode to increase the height of the Schottky barrier. The Ru-Pt mixed layer was grown by atomic layer deposition. The atomic ratio of Ru/Pt varied in the entire range from 100 at.% Ru to 100 at.% Pt. Rutile TiO2 films were inductively formed only on the Ru-Pt layer containing ≤43 at.% Pt, while anatase TiO2 films with a relatively low dielectric constant (∼40) were formed at Pt compositions > 63 at.%. The Ru-Pt (40-50 at.%) layer also attained an increase in work function of ∼0.3-0.4 eV, leading to an improvement in the leakage currents of the TiO2/Ru-Pt capacitor. These findings suggested that a Ru-Pt layer could serve as a promising electrode for next-generation DRAM capacitors.

Highly Sensitive Sensors Based on Metal-Oxide Nanocolumns for Fire Detection.

A fire detector is the most important component in a fire alarm system. Herein, we present the feasibility of a highly sensitive and rapid response gas sensor based on metal oxides as a high performance fire detector. The glancing angle deposition (GLAD) technique is used to make the highly porous structure such as nanocolumns (NCs) of various metal oxides for enhancing the gas-sensing performance. To measure the fire detection, the interface circuitry for our sensors (NiO, SnO2, WO3 and In2O3 NCs) is designed. When all the sensors with various metal-oxide NCs are exposed to fire environment, they entirely react with the target gases emitted from Poly(vinyl chlorides) (PVC) decomposed at high temperature. Before the emission of smoke from the PVC (a hot-plate temperature of 200 °C), the resistances of the metal-oxide NCs are abruptly changed and SnO2 NCs show the highest response of 2.1. However, a commercial smoke detector did not inform any warning. Interestingly, although the NiO NCs are a p-type semiconductor, they show the highest response of 577.1 after the emission of smoke from the PVC (a hot-plate temperature of 350 °C). The response time of SnO2 NCs is much faster than that of a commercial smoke detector at the hot-plate temperature of 350 °C. In addition, we investigated the selectivity of our sensors by analyzing the responses of all sensors. Our results show the high potential of a gas sensor based on metal-oxide NCs for early fire detection.

Control of the initial growth in atomic layer deposition of Pt films by surface pretreatment.

The controllability of the nucleation behavior of Pt in atomic layer deposition (ALD) by surface pretreatments with H2O, H2S, and NH3 was investigated. The H2O pretreatment on SiO2 and TiO2 surfaces had little effect on the nucleation of Pt. The H2S pretreatment on the SiO2 and TiO2 surfaces significantly delayed the nucleation of Pt on them, while the NH3 pretreatment on the TiO2 surface led to fluent nucleation of Pt. In particular, a continuous Pt film was successfully formed even at an ultrathin thickness of approximately 2.2 nm by NH3 pretreatment. This work suggests that the pretreatment with H2S and NH3 is an efficient way to control the nucleation of Pt in ALD without the support of any reactive species, such as plasma or O3. Such a strategy enables the easy control of the size and distribution density of Pt nanoparticles for a wide range of applications.

Highly Sensitive H2S Sensor Based on the Metal-Catalyzed SnO2 Nanocolumns Fabricated by Glancing Angle Deposition.

As highly sensitive H2S gas sensors, Au- and Ag-catalyzed SnO2 thin films with morphology-controlled nanostructures were fabricated by using e-beam evaporation in combination with the glancing angle deposition (GAD) technique. After annealing at 500 °C for 40 h, the sensors showed a polycrystalline phase with a porous, tilted columnar nanostructure. The gas sensitivities (S = Rgas/Rair) of Au and Ag-catalyzed SnO2 sensors fabricated by the GAD process were 0.009 and 0.015, respectively, under 5 ppm H2S at 300 °C, and the 90% response time was approximately 5 s. These sensors showed excellent sensitivities compared with the SnO2 thin film sensors that were deposited normally (glancing angle = 0°, S = 0.48).

A ferroelectric photocatalyst for enhancing hydrogen evolution: polarized particulate suspension.

A particle-based photocatalyst with a permanent internal field prepared by a corona poling method is presented as a novel approach to enhance the hydrogen evolution reaction in a particulate-suspension system. Photocatalytic activity of K0.5Na0.5NbO3 was significantly improved by 7.4 times after the polarization.

An Artificial Olfactory System Based on a Chemi-Memristive Device.

Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Highly Reliable Threshold Switching Characteristics of Surface-Modulated Diffusive Memristors Immune to Atmospheric Changes.

Active cation-based diffusive memristors featuring essentially volatile threshold switching have been proposed for novel applications, such as a selector in a one-selector-and-one-resistor structure and signal generators in neuromorphic computing. However, the high variability of the switching behavior, which results from the high electroforming voltage, external environmental conditions, and transition to the non-volatile switching mode in a high-current range, is considered a major impediment to such applications. Herein, for the first time, we developed a highly reliable threshold switching device immune to atmospheric changes based on an ultraviolet-ozone (UVO)-treated diffusive memristor consisting of Ag and SiO2 nanorods (NRs). UVO treatment forms a stable water reservoir on the surface of SiO2 NRs, facilitating the redox reaction and ion migration of Ag. Consequently, diffusive memristors possess reliable switching characteristics, including electroforming-free, repeatable, and consistent switching with resistance to changes in ambient conditions and compliance levels during operation. We demonstrated that our approach is suitable for various metal oxides and can be used in numerous applications.

Breathable MOFs Layer on Atomically Grown 2D SnS2 for Stable and Selective Surface Activation.

2D transition metal dichalcogenides (TMDs) have significant research interests in various novel applications due to their intriguing physicochemical properties. Notably, one of the 2D TMDs, SnS2 , has superior chemiresistive sensing properties, including a planar crystal structure, a large surface-to-volume ratio, and a low electronic noise. However, the long-term stability of SnS2 in humid conditions remains a critical shortcoming towards a significant degradation of sensitivity. Herein, it is demonstrated that the subsequent self-assembly of zeolite imidazolate framework (ZIF-8) can be achieved in situ growing on SnS2 nanoflakes as the homogeneous porous materials. ZIF-8 layer on SnS2 allows the selective diffusion of target gas species, while effectively preventing the SnS2 from severe oxidative degradation. Molecular modeling such as molecular dynamic simulation and DFT calculation, further supports the mechanism of sensing stability and selectivity. From the results, the in situ grown ZIF-8 porous membrane on 2D materials corroborates the generalizable strategy for durable and reliable high-performance electronic applications of 2D materials.

Autonomous Resonance-Tuning Mechanism for Environmental Adaptive Energy Harvesting.

An innovative autonomous resonance-tuning (ART) energy harvester is reported that utilizes adaptive clamping systems driven by intrinsic mechanical mechanisms without outsourcing additional energy. The adaptive clamping system modulates the natural frequency of the harvester's main beam (MB) by adjusting the clamping position of the MB. The pulling force induced by the resonance vibration of the tuning beam (TB) provides the driving force for operating the adaptive clamp. The ART mechanism is possible by matching the natural frequencies of the TB and clamped MB. Detailed evaluations are conducted on the optimization of the adaptive clamp tolerance and TB design to increase the pulling force. The energy harvester exhibits an ultrawide resonance bandwidth of over 30 Hz in the commonly accessible low vibration frequency range (<100 Hz) owing to the ART function. The practical feasibility is demonstrated by evaluating the ART performance under both frequency and acceleration-variant conditions and powering a location tracking sensor.

Quick, controlled synthesis of ultrathin Bi2Se3 nanodiscs and nanosheets.

Ultrathin (4-6 nm) single-crystal Bi(2)Se(3) nanodiscs and nanosheets were synthesized through a simple and quick solution process. The growth mechanism was investigated in detail. Crystal seeds grew via 2D self-attachment of small nanoparticles followed by epitaxial recrystallization into single crystals. The lateral dimension of the nanodiscs increased and their shape changed from circles to hexagons as the reaction temperature increased. Positively charged polymer surfactants greatly enlarged the lateral dimension to produce nanosheets with uniform thickness.

Thermoelectric properties of Bi2Te3-PbTe pseudo binary near the eutectic composition.

(Bi2Te3)(1-x)(PbTe)(x) binary systems near eutectic composition were prepared by melting of elemental metals and a sequential water quenching process and their microstructures and thermoelectric properties were investigated. Multiple phases such as Bi2Te3, BiPbTe and PbTe were observed due to phase separation when the composition x was higher than the eutectic point. Also the electrical conduction type of the alloys converted from p-type to n-type in the phase separated alloys. The lattice thermal conductivities in the phase-separated alloys are lower than those in alloys without phase separation, attributable to increased boundary scattering.

Controllable SiOx Nanorod Memristive Neuron for Probabilistic Bayesian Inference.

Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010 ) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10-2 ) and its robustness to the ProbAct variation.

Artificial Adaptive and Maladaptive Sensory Receptors Based on a Surface-Dominated Diffusive Memristor.

A biological receptor serves as sensory transduction from an external stimulus to an electrical signal. It allows humans to better match the environment by filtering out repetitive innocuous information and recognize potentially damaging stimuli through key features, including adaptive and maladaptive behaviors. Herein, for the first time, the authors develop substantial artificial receptors involving both adaptive and maladaptive behaviors using diffusive memristor. Metal-oxide nanorods (NR) as a switching matrix enable the electromigration of an active metal along the surface of the NRs under electrical stimulation, resulting in unique surface-dominated switching dynamics with the advantage of fast Ag migration and fine controllability of the conductive filament. To experimentally demonstrate its potential application, a thermoreceptor system is constructed using memristive artificial receptors. The proposed surface-dominated diffusive memristor allows the direct emulation of the biological receptors, which represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Surface-Dominated HfO2 Nanorod-Based Memristor Exhibiting Highly Linear and Symmetrical Conductance Modulation for High-Precision Neuromorphic Computing.

The switching characteristics and performance of oxide-based memristors are predominately determined by oxygen- or oxygen-vacancy-mediated redox reactions and the consequent formation of conducting filaments (CFs). Devices using oxide thin films as the switching layer usually require an electroforming process for subsequent switching operations, which induces large device-to-device variations. In addition, the hard-to-control redox reaction during repeated switching causes random fluctuations or degradation of each resistance state, hindering reliable switching operations. In this study, an HfO2 nanorod (NR)-based memristor is proposed for simultaneously achieving highly uniform, electroforming-free, fast, and reliable analogue switching properties. The well-controlled redox reaction due to the easy gas exchange with the environment at the surface of the NRs enhances the generation of oxygen or oxygen vacancies during the switching operation, resulting in electroforming-free and reliable switching behavior. In addition, the one-dimensional surface growth of CFs facilitates highly linear conductance modulation with smaller conductance changes compared with the two-dimensional volume growth in thin-film-based memristors, resulting in a high accuracy of >92% in the Modified National Institute of Standards and Technology pattern-recognition test and desirable spike-timing-dependent plasticity.