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Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.
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Comércio , Aprendizado de Máquina , Bases de Dados Factuais , Fatores de TempoRESUMO
The prior theoretical model shows that GaSb is one of the few non-alloy semiconductors showing phonons ballistic effect in the thermal conductivity. However, no previous literature had been reported on the experimental measurements on the quasi-ballistic thermal transport of the GaSb thin film. In this paper, we employed the time-domain thermoreflectance (TDTR) to study the thermal transport of nanoparticles embedded GaSb thin film. Our measurements results provide first experimental evidence to verify the quasi-ballistic effect in the thermal transport of the GaSb thin film. The apparent cross-plane thermal conductivity of pure GaSb sample drops â¼15% when the pump laser modulation frequency is increased from 0.8 MHz to 10 MHz at room temperature. To further understand the thermal transport mechanism, Tempered Lévy analysis is employed to study the quasi-ballistic effect of the GaSb thin film. The model shows that GaSb thin film thermal transport has a superdiffusion exponent, [Formula: see text] = 1.51 ± 0.23 and Lévy-Fourier transition length, r LF = 0.19 ± 0.13 µm. Both obtained values via Tempered Lévy indicates the quasi-ballistic transport phenomena in GaSb thin film. However, this frequency dependence of the cross-plane thermal conductivity will disappear in the presence of the 3%-20% ErSb nanoparticles. Another thermal transport mechanism, i.e. anisotropic thermal transport, can be observed in GaSb thin film. The ratio of in- to cross-plane thermal conductivity varies from â¼0.2 to â¼0.7 in the 0%-20% ErSb nanoparticles volume concentrations. Detailed temperature dependence of the in-plane thermal conductivity of ErSb:GaSb samples with 0%-20% are also included in the paper for the understanding of the scattering mechanism in the thin film thermal transport. With enhanced understanding of the quasi-ballistic and anisotropic thin film thermal transport, our results might improve the thermal management efficiency of the GaSb devices.
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Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.
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The field of thermoplasmonics has thrived in the past decades because it uniquely provides remotely controllable nanometer-scale heat sources that have augmented numerous technologies. Despite the extensive studies on steady-state plasmonic heating, the dynamic behavior of the plasmonic heaters in the nanosecond regime has remained largely unexplored, yet such a time scale is indeed essential for a broad range of applications such as photocatalysis, optical modulators, and detectors. Here, we use two distinct techniques based on the temperature-dependent surface reflectivity of materials, optical thermoreflectance imaging (OTI) and time-domain thermoreflectance (TDTR), to comprehensively investigate plasmonic heating in both spatial and temporal domains. Specifically, OTI enables the rapid visualization of plasmonic heating with sub-micron resolution, outperforming a standard thermal camera, and allows us to establish the connection between the optical absorptance and heating efficiency as well as to analyze plasmonic heating dynamics on the millisecond scale. Using the TDTR technique, we, for the first time, study the optical resonance-dependent heat-transfer dynamics of a nanometer-scale plasmonic structure in the nanosecond regime and use a detailed computational model to extract the impulse response and thermal interface conductance of a multilayer plasmonic structure. The study reveals a quantitative relationship between the dimensions of the nanopatterned structure and its spatiotemporal thermal response to the light pulse excitation, a thermoplasmonic effect resulting from the spatial distribution of the absorbed electromagnetic energy. We also conclude that the two thermoreflectance techniques provide necessary feedback to nanoscale thermoplasmonic heat management, for which optimization in either heating power or temperature decay speed is needed.
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Conventional models for predicting thermal conductivity of alloys usually assume a pure kinetic regime as alloy scattering dominates normal processes. However, some discrepancies between these models and experiments at very small alloy concentrations have been reported. In this work, we use the full first principles kinetic collective model (KCM) to calculate the thermal conductivity of Si1-xGex and InxGa1-xAs alloys. The calculated thermal conductivities match well with the experimental data for all alloy concentrations. The model shows that the collective contribution must be taken into account at very low impurity concentrations. For higher concentrations, the collective contribution is suppressed, but normal collisions have the effect of significantly reducing the kinetic contribution. The study thus shows the importance of the proper inclusion of normal processes even for alloys for accurate modeling of thermal transport. Furthermore, the phonon spectral distribution of the thermal conductivity is studied in the framework of KCM, providing insights to interpret the superdiffusive regime introduced in the truncated Lévy flight framework.
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In this paper, we systematically investigate three different routes of synthesizing 2% Na-doped PbTe after melting the elements: (i) quenching followed by hot-pressing (QH), (ii) annealing followed by hot-pressing, and (iii) quenching and annealing followed by hot-pressing. We found that the thermoelectric figure of merit, zT, strongly depends on the synthesis condition and that its value can be enhanced to â¼ 2.0 at 773 K by optimizing the size distribution of the nanostructures in the material. Based on our theoretical analysis on both electron and thermal transport, this zT enhancement is attributed to the reduction of both the lattice and electronic thermal conductivities; the smallest sizes (2 â¼ 6 nm) of nanostructures in the QH sample are responsible for effectively scattering the wide range of phonon wavelengths to minimize the lattice thermal conductivity to â¼ 0.5 W/m K. The reduced electronic thermal conductivity associated with the suppressed electrical conductivity by nanostructures also helped reduce the total thermal conductivity. In addition to the high zT of the QH sample, the mechanical hardness is higher than the other samples by a factor of around 2 due to the smaller grain sizes. Overall, this paper suggests a guideline on how to achieve high zT and mechanical strength of a thermoelectric material by controlling nano- and microstructures of the material.
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During routine operation, electrically percolating nanocomposites are subjected to high voltages, leading to spatially heterogeneous current distribution. The heterogeneity implies localized self-heating that may (self-consistently) reroute the percolation pathways and even irreversibly damage the material. In the absence of experiments that can spatially resolve the current distribution and a nonlinear percolation model suitable to interpret them, one relies on empirical rules and safety factors to engineer these materials. In this paper, we use ultrahigh resolution thermo-reflectance imaging, coupled with a new imaging processing technique, to map the spatial distribution ΔT(x, y; I) and histogram f(ΔT) of temperature rise due to self-heating in two types of 2D networks (percolating and copercolating). Remarkably, we find that the self-heating can be described by a simple two-parameter Weibull distribution, even under voltages high enough to reconfigure the percolation pathways. Given the generality of the phenomenological argument supporting the distribution, other percolating networks are likely to show similar stress distribution in response to sufficiently large stimuli. Furthermore, the spatial evolution of the self-heating of network was investigated by analyzing the spatial distribution and spatial correlation, respectively. An estimation of degree of hotspot clustering reveals a mechanism analogous to crystallization physics. The results should encourage nonlinear generalization of percolation models necessary for predictive engineering of nanocomposite materials.
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The recent developments in nanoscale solid-state cooling are reviewed. This includes both theoretical and experimental studies of different physical concepts, as well as nanostructured material design and device configurations. We primarily focus on thermoelectric, thermionic and thermo-magnetic coolers. Particular emphasis is given to the concepts based on metal-semiconductor superlattices, graded materials, non-equilibrium thermoelectric devices, Thomson coolers, and photon assisted Peltier coolers as promising methods for efficient solid-state cooling. Thermomagnetic effects such as magneto-Peltier and Nernst-Ettingshausen cooling are briefly described and recent advances and future trends in these areas are reviewed. The ongoing progress in solid-state cooling concepts such as spin-calorimetrics, electrocalorics, non-equilibrium/nonlinear Peltier devices, superconducting junctions and two-dimensional materials are also elucidated and practical achievements are reviewed. We explain the thermoreflectance thermal imaging microscopy and the transient Harman method as two unique techniques developed for characterization of thermoelectric microrefrigerators. The future prospects for solid-state cooling are briefly summarized.
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Electroreflectance microscopy is demonstrated as a high-resolution, non-contact method to image dynamic charge distribution in integrated microsupercapacitor structures during fast voltage cycling. Electroreflectance camera images of a gold electrode H3PO4 polymer electrolyte microsupercapacitor reveal time varying charge distribution with submicron spatial resolution, millisecond time resolution, and electroreflectance resolution on the order of 500 nC cm(-2). A model describing changes in the metal electrode's optical constants as a function of free electron concentration shows good agreement with measured electroreflectance. The proposed method can be used for sensitive, non-contact measurements of charge spatial distribution, and defect and performance characterization in electrode-electrolyte microdevices.
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This microsupercapacitor ageing study demonstrates the usefulness of the electroreflectance technique by quantifying local charge accumulation. Two separate devices with interdigitated electrodes were evaulated over a period of 4.1 million charge/discharge cycles. The key results are spatial mapping of charge accumulation in the gold electrodes derived from variation in the observed electrode reflectance. The nominal device exhibited little change in spatial distribution throughout the ageing cycle and serves as a comparison for the test device, which exhibited some nonuniform charge accumulation behavior. Further, an accelerated ageing test was completed by applying increasing voltage pulses up to 1.46 V to the device. Visual evidence of electrode ageing emerged in the reflectance distribution. An equivalent circuit model was developed to assess the evolution of individual circuit elements that correlate to the physical causes of ageing.
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To design superior thermoelectric materials the minority carrier blocking effect in which the unwanted bipolar transport is prevented by the interfacial energy barriers in the heterogeneous nanostructures has been theoretically proposed recently. The theory predicts an enhanced power factor and a reduced bipolar thermal conductivity for materials with a relatively low doping level, which could lead to an improvement in the thermoelectric figure of merit (ZT). Here we show the first experimental demonstration of the minority carrier blocking in lead telluride-silver telluride (PbTe-Ag2Te) nanowire heterostructure-based nanocomposites. The nanocomposites are made by sintering PbTe-Ag2Te nanowire heterostructures produced in a highly scalable solution-phase synthesis. Compared with Ag2Te nanowire-based nanocomposite produced in similar method, the PbTe-Ag2Te nanocomposite containing â¼5 atomic % PbTe exhibits enhanced Seebeck coefficient, reduced thermal conductivity, and â¼40% improved ZT, which can be well explained by the theoretical modeling based on the Boltzmann transport equations when energy barriers for both electrons and holes at the heterostructure interfaces are considered in the calculations. For this p-type PbTe-Ag2Te nanocomposite, the barriers for electrons, that is, minority carriers, are primarily responsible for the ZT enhancement. By extending this approach to other nanostructured systems, it represents a key step toward low-cost solution-processable nanomaterials without heavy doping level for high-performance thermoelectric energy harvesting.
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Materials with embedded nanoparticles are of considerable interest for thermoelectric applications. Here, we experimentally characterize the effect of nanoparticles on the recently discovered Lévy phonon transport in semiconductor alloys. The fractal space dimension α ≈ 1.55 of quasiballistic (superdiffusive) heat conduction in (ErAs)x:InGaAlAs is virtually independent of the Er content 0.001 < x < 0.1 but instead controlled by alloy scattering of the host matrix. The increased nanoparticle concentration does reduce the diffusive recovery length by an order of magnitude. The bulk conductivity drops by 3-fold, in close agreement with a Callaway model. Our results may provide helpful hints toward engineering superdiffusive heat transport similar to what has been achieved with light in Lévy glasses.
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Solution-synthesized thermoelectric nanostructured materials have the potential to have lower cost and higher performance than materials synthesized by solid-state methods. Herein we present the synthesis of ultrathin PbTe nanowires, which are compressed by spark plasma sintering at various temperatures in the range of 405-500 °C. The resulting discs possess grains with sizes of 5-30 µm as well as grains with sizes on the order of the original 12 nm diameter PbTe nanowires. This micro- and nanostructure leads to a significantly reduced thermal conductivity compared to bulk PbTe. Careful electron transport analysis shows suppressed electrical conductivity due to increased short-range and ionized defect scatterings, while the Seebeck coefficient remains comparable to the bulk value. The PbTe nanowire samples are found unintentionally p-type doped to hole concentrations of 2.16-2.59 × 10(18) cm(-3). The maximum figure of merit achieved in the unintentionally doped spark plasma sintered PbTe nanowires is 0.33 at 350 K, which is among the highest reported for unintentionally doped PbTe at low temperatures.
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In this article, we demonstrated that composition modulation of Ag2Te nanowires can be achieved during the self-templated transformation of Te nanowires into Ag2Te nanowires during solution phase synthesis, which provides a mean to tune the carrier density of the Ag2Te nanowires. Both nearly stoichiometric and Ag-rich nanowires have been synthesized, which give rise to p-type and n-type Ag2Te nanocomposites after hot press, respectively. The electrical and thermal properties of the two kinds of samples have been measured. Theoretical modeling based on the near-equilibrium Boltzmann transport equations has been used to understand the experimental results. We found that ZT of the heavily doped n-type sample reaches 0.55 at 400 K, which is the highest ZT value reported for Ag2Te at the same temperature mainly due to the reduced thermal conductivity by the nanostructures. Theoretical analysis on the carrier transport shows that the power factor is also very well optimized in the doped Ag2Te sample considering the reduced carrier mobility by the nanostructures.
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The metal to insulator transition (MIT) of strongly correlated materials is subject to strong lattice coupling, which brings about the unique one-dimensional alignment of metal-insulator (M-I) domains along nanowires or nanobeams. Many studies have investigated the effects of stress on the MIT and hence the phase boundary, but few have directly examined the temperature profile across the metal-insulating interface. Here, we use thermoreflectance microscopy to create two-dimensional temperature maps of single-crystalline VO2 nanobeams under external bias in the phase coexisting regime. We directly observe highly localized alternating Peltier heating and cooling as well as Joule heating concentrated at the M-I domain boundaries, indicating the significance of the domain walls and band offsets. Utilizing the thermoreflectance technique, we are able to elucidate strain accumulation along the nanobeam and distinguish between two insulating phases of VO2 through detection of the opposite polarity of their respective thermoreflectance coefficients. Microelasticity theory was employed to predict favorable domain wall configurations, confirming the monoclinic phase identification.
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Potentiometric ion-selective electrodes (ISEs) have broad applications in personalized healthcare, smart agriculture, oil/gas exploration, and environmental monitoring. However, high-precision potentiometric sensing is difficult with field-deployed sensors due to time-dependent voltage drift and the need for frequent calibration. In the laboratory setting, these issues are resolved by repeated calibration by measuring the voltage response at multiple standard solutions at a constant temperature. For field-deployed sensors, it is difficult to frequently interrupt operation and recalibrate with standard solutions. Moreover, the constant surrounding temperature constraint imposed by the traditional calibration process makes it unsuitable for temperature-varying field use. To address the challenges of traditional calibration for field-deployed sensors, in this study, we propose a novel in situ calibration approach in which we use natural/external temperature variation in the field to obtain the time-varying calibration parameters, without having to relocate the sensors or use any complex system. We also develop a temperature-supervised monitoring method to detect the drift of the sensor during operation. Collectively, the temperature-based drift monitoring and in situ calibration methods allow us to monitor the drift of sensors and correct them periodically to achieve high-precision sensing. We demonstrate our approach in three testbeds: (1) under controlled temperature variation in the lab, (2) under natural temperature variation in a greenhouse, and (3) in the field to monitor nitrate activity of an agricultural site. In the laboratory study, we validate that the calibration parameters of printed nitrate ISEs can be reproduced by our proposed calibration process; therefore, it can serve as an alternative to traditional calibration processes. In the greenhouse, we show the use of natural temperature variation to calibrate the sensors and detect the drift in a fixed concentration nitrate solution. Finally, we demonstrate the use of the method to monitor the nitrate activity of an agricultural field within 10% of laboratory-based measurements (i.e., a sensitivity of 0.03 mM) for a period of 22 days. The findings highlight the prospect of temperature-based calibration and drift monitoring for high-precision sensing with field-deployed ISEs.
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Eletrodos Seletivos de Íons , Nitratos , Calibragem , Temperatura , PotenciometriaRESUMO
For a continuous healthcare or environmental monitoring system, it is essential to reliably sense the analyte concentration reported by electrochemical sensors. However, environmental perturbation, sensor drift, and power-constraint make reliable sensing with wearable and implantable sensors difficult. While most studies focus on improving sensor stability and precision by increasing the system's complexity and cost, we aim to address this challenge using low-cost sensors. To obtain the desired accuracy from low-cost sensors, we borrow two fundamental concepts from communication theory and computer science. First, inspired by reliable data transmission over a noisy communication channel by incorporating redundancy, we propose to measure the same quantity (i.e., analyte concentration) with multiple sensors. Second, we estimate the true signal by aggregating the output of the sensors based on their credibility, a technique originally developed for "truth discovery" in social sensing applications. We use the Maximum Likelihood Estimation to estimate the true signal and the credibility index of the sensors over time. Using the estimated signal, we develop an on-the-fly drift-correction method to make unreliable sensors reliable by correcting any systematic drifts during operation. Our approach can determine solution pH within 0.09 pH for more than three months by detecting and correcting the gradual drift of pH sensors as a function of gamma-ray irradiation. In the field study, we validate our method by measuring nitrate levels in an agricultural field onsite over 22 days within 0.06 mM of a high-precision laboratory-based sensor. We theoretically demonstrate and numerically validate that our approach can estimate the true signal even when the majority (~ 80%) of the sensors are unreliable. Moreover, by restricting wireless transmission to high-credible sensors, we achieve near-perfect information transfer at a fraction of the energy cost. The high-precision sensing with low-cost sensors at reduced transmission cost will pave the way for pervasive in-field sensing with electrochemical sensors. The approach is general and can improve the accuracy of any field-deployed sensors undergoing drift and degradation during operation.
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Coherent potential approximation is used to study the effect of adding doped spherical nanoparticles inside a host matrix on the thermoelectric properties. This takes into account electron multiple scatterings that are important in samples with relatively high volume fraction of nanoparticles (>1%). We show that with large fraction of uniform small size nanoparticles (â¼1 nm), the power factor can be enhanced significantly. The improvement could be large (up to 450% for GaAs) especially at low temperatures when the mobility is limited by impurity or nanoparticle scattering. The advantage of doping via embedded nanoparticles compared to the conventional shallow impurities is quantified. At the optimum thermoelectric power factor, the electrical conductivity of the nanoparticle-doped material is larger than that of impurity-doped one at the studied temperature range (50-500 K) whereas the Seebeck coefficient of the nanoparticle doped material is enhanced only at low temperatures (â¼50 K).
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The dynamics of optical nonlinearity in the presence of gain and feedback can be complex leading to chaos in certain regimes. Temporal, spectral, spatial, or polarization instability of optical fields can emerge from chaotic response of an optical [Formula: see text] or [Formula: see text] nonlinear medium placed between two cavity mirrors or before a single feedback mirror. The complex mode dynamics, high-order correlations, and transition to instability in these systems are not well known. We consider a [Formula: see text] medium with amplified four-wave mixing process and study noise and correlation between multiple optical modes. Although individual modes show intensity instability, we observe relative intensity noise reduction close to the standard quantum noise, limited by the camera speed. We observe a relative noise reduction of more than 20 dB and fourth-order intensity correlation between four spatial modes. More than 100 distinct correlated quadruple modes can be generated using this process.
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Precision Agriculture (PA) is an integral component of the contemporary agricultural revolution that focuses on enhancing food productivity in proportion to the increasing global population while minimizing resource waste. While the recent advancements in PA, such as the integration of IoT (Internet of Things) sensors, have significantly improved the surveillance of field conditions to achieve high yields, the presence of batteries and electronic chips makes them expensive and non-biodegradable. To address these limitations, for the first time, we have developed a fully Degradable Intelligent Radio Transmitting Sensor (DIRTS) that allows remote sensing of subsoil volumetric water using drone-assisted wireless monitoring. The device consists of a simple miniaturized resonating antenna encapsulated in a biodegradable polymer material such that the resonant frequency of the device is dependent on the dielectric properties of the soil surrounding the encapsulated structure. The simple structure of DIRTS enables scalable additive manufacturing processes using cost-effective, biodegradable materials to fabricate them in a miniaturized size, thereby facilitating their automated distribution in the soil. As a proof-of-concept, we present the use of DIRTS in lab and field conditions where the sensors demonstrate the capability to detect volumetric water content within the range of 3.7-23.5% with a minimum sensitivity of 9.07 MHz/%. Remote sensing of DIRTS can be achieved from an elevation of 40 cm using drones to provide comparable performance to lab measurements. A systematic biodegradation study reveals that DIRTS can provide stable readings within the expected duration of 1 year with less than 4% change in sensitivity before signs of degradation. DIRTS provides a new steppingstone toward advancing precision agriculture while minimizing the environmental footprint.