In Situ Drift Monitoring and Calibration of Field-Deployed Potentiometric Sensors Using Temperature Supervision.

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.

A new paradigm of reliable sensing with field-deployed electrochemical sensors integrating data redundancy and source credibility.

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.

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets.

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.

Temperature Self-Calibration of Always-On, Field-Deployed Ion-Selective Electrodes Based on Differential Voltage Measurement.

Originally developed for use in controlled laboratory settings, potentiometric ion-selective electrode (ISE) sensors have recently been deployed for continuous, in situ measurement of analyte concentration in agricultural (e.g., nitrate), environmental (e.g., ocean acidification), industrial (e.g., wastewater), and health-care sectors (e.g., sweat sensors). However, due to uncontrolled temperature and lack of frequent calibration in these field applications, it has been difficult to achieve accuracy comparable to the laboratory setting. In this paper, we propose a novel temperature self-calibration method where the ISE sensors can serve as their own thermometer and therefore precisely measure the analyte concentration in the field condition by compensating for the temperature variations. We validate the method with controlled experiments using pH and nitrate ISEs, which use the Nernst principle for electrochemical sensing. We show that, using temperature self-calibration, pH and nitrate can be measured within 0.3% and 5% of the true concentration, respectively, under varying concentrations and temperature conditions. Moreover, we perform a field study to continuously monitor the nitrate concentration of an agricultural field over a period of 6 days. Our temperature self-calibration approach determines the nitrate concentration within 4% of the ground truth measured by laboratory-based high-precision nitrate sensors. Our approach is general and would allow battery-free temperature-corrected analyte measurement for all Nernst principle-based sensors being deployed as wearable or implantable sensors.

Intensity instability and correlation in amplified multimode wave mixing.

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.

A biodegradable chipless sensor for wireless subsoil health monitoring.

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.

Steady-State and Transient Performance of Ion-Sensitive Electrodes Suitable for Wearable and Implantable Electro-Chemical Sensing.

Traditional Potentiometric Ion-selective Electrodes (ISE) are widely used in industrial and clinical settings. The simplicity and small footprint of ISE have encouraged their recent adoption as wearable/implantable sensors for personalized healthcare and precision agriculture, creating a new set of unique challenges absent in traditional ISE. In this paper, we develop a fundamental physics-based model to describe both steady-state and transient responses of ISE relevant for wearable/implantable sensors. The model is encapsulated in a "generalized Nernst formula" that explicitly accounts for the analyte density, time-dynamics of signal transduction, ion-selective membrane thickness, and other sensor parameters. The formula is validated numerically by self-consistent modeling of multispecies ion-transport and experimentally by interpreting the time dynamics and thickness dependence of thin-film solid-contact and graphene-based ISE sensors for measuring soil nitrate concentration. These fundamental results will support the accelerated development of ISE for wearable/implantable applications.

Anisotropic thermal conductivity of the nanoparticles embedded GaSb thin film semiconductor.

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 ∼15% 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 ∼0.2 to ∼0.7 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.

Relativistic stable processes in quasiballistic heat conduction in semiconductors.

In this article, we show how relativistic α-stable processes can be used to explain quasiballistic heat conduction in semiconductors. This is a method that can fit experimental results of ultrafast laser heating in alloys. It also provides a connection to a rich literature on the Feynman-Kac formalism and random processes that transition from a stable Lévy process on short time and length scales to the Brownian motion at larger scales. This transition was captured by a heuristic truncated Lévy distribution in earlier papers. The rigorous Feynman-Kac approach is used to derive sharp bounds for the transition kernel. Future directions are briefly discussed.

Far-field thermal imaging below diffraction limit.

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.

Ultrafast chemical imaging by widefield photothermal sensing of infrared absorption.

Infrared (IR) imaging has become a viable tool for visualizing various chemical bonds in a specimen. The performance, however, is limited in terms of spatial resolution and imaging speed. Here, instead of measuring the loss of the IR beam, we use a pulsed visible light for high-throughput, widefield sensing of the transient photothermal effect induced by absorption of single mid-IR pulses. To extract these transient signals, we built a virtual lock-in camera synchronized to the visible probe and IR light pulses with precisely controlled delays, allowing submicrosecond temporal resolution determined by the probe pulse width. Our widefield photothermal sensing microscope enabled chemical imaging at a speed up to 1250 frames/s, with high spectral fidelity, while offering submicrometer spatial resolution. With the capability of imaging living cells and nanometer-scale polymer films, widefield photothermal microscopy opens a new way for high-throughput characterization of biological and material specimens.

Spatial and Temporal Nanoscale Plasmonic Heating Quantified by Thermoreflectance.

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.

Collective thermal transport in pure and alloy semiconductors.

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.

Full-field thermal imaging of quasiballistic crosstalk reduction in nanoscale devices.

Understanding nanoscale thermal transport is of substantial importance for designing contemporary semiconductor technologies. Heat removal from small sources is well established to be severely impeded compared to diffusive predictions due to the ballistic nature of the dominant heat carriers. Experimental observations are commonly interpreted through a reduction of effective thermal conductivity, even though most measurements only probe a single aggregate thermal metric. Here, we employ thermoreflectance thermal imaging to directly visualise the 2D temperature field produced by localised heat sources on InGaAs with characteristic widths down to 100 nm. Besides displaying effective thermal performance reductions up to 50% at the active junctions in agreement with prior studies, our steady-state thermal images reveal that, remarkably, 1-3 µm adjacent to submicron devices the crosstalk is actually reduced by up to fourfold. Submicrosecond transient imaging additionally shows responses to be faster than conventionally predicted. A possible explanation based on hydrodynamic heat transport, and some open questions, are discussed.

Steep-slope hysteresis-free negative capacitance MoS2 transistors.

The so-called Boltzmann tyranny defines the fundamental thermionic limit of the subthreshold slope of a metal-oxide-semiconductor field-effect transistor (MOSFET) at 60 mV dec-1 at room temperature and therefore precludes lowering of the supply voltage and overall power consumption 1,2 . Adding a ferroelectric negative capacitor to the gate stack of a MOSFET may offer a promising solution to bypassing this fundamental barrier 3 . Meanwhile, two-dimensional semiconductors such as atomically thin transition-metal dichalcogenides, due to their low dielectric constant and ease of integration into a junctionless transistor topology, offer enhanced electrostatic control of the channel 4-12 . Here, we combine these two advantages and demonstrate a molybdenum disulfide (MoS2) two-dimensional steep-slope transistor with a ferroelectric hafnium zirconium oxide layer in the gate dielectric stack. This device exhibits excellent performance in both on and off states, with a maximum drain current of 510 µA µm-1 and a sub-thermionic subthreshold slope, and is essentially hysteresis-free. Negative differential resistance was observed at room temperature in the MoS2 negative-capacitance FETs as the result of negative capacitance due to the negative drain-induced barrier lowering. A high on-current-induced self-heating effect was also observed and studied.

Thermodynamic Studies of ß-Ga2O3 Nanomembrane Field-Effect Transistors on a Sapphire Substrate.

The self-heating effect is a severe issue for high-power semiconductor devices, which degrades the electron mobility and saturation velocity, and also affects the device reliability. On applying an ultrafast and high-resolution thermoreflectance imaging technique, the direct self-heating effect and surface temperature increase phenomenon are observed on novel top-gate ß-Ga2O3 on insulator field-effect transistors. Here, we demonstrate that by utilizing a higher thermal conductivity sapphire substrate rather than a SiO2/Si substrate, the temperature rise above room temperature of ß-Ga2O3 on the insulator field-effect transistor can be reduced by a factor of 3 and thereby the self-heating effect is significantly reduced. Both thermoreflectance characterization and simulation verify that the thermal resistance on the sapphire substrate is less than 1/3 of that on the SiO2/Si substrate. Therefore, maximum drain current density of 535 mA/mm is achieved on the sapphire substrate, which is 70% higher than that on the SiO2/Si substrate due to reduced self-heating. Integration of ß-Ga2O3 channel on a higher thermal conductivity substrate opens a new route to address the low thermal conductivity issue of ß-Ga2O3 for power electronics applications.

Nanoscale solid-state cooling: a review.

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.

Evidence of Universal Temperature Scaling in Self-Heated Percolating Networks.

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.

Thermal Transport Driven by Extraneous Nanoparticles and Phase Segregation in Nanostructured Mg2(Si,Sn) and Estimation of Optimum Thermoelectric Performance.

Solid solutions of magnesium silicide and magnesium stannide were recently reported to have high thermoelectric figure-of-merits (ZT) due to remarkably low thermal conductivity, which was conjectured to come from phonon scattering by segregated Mg2Si and Mg2Sn phases without detailed study. However, it is essential to identify the main cause for further improving ZT as well as estimating its upper bound. Here we synthesized Mg2(Si,Sn) with nanoparticles and segregated phases, and theoretically analyzed and estimated the thermal conductivity upon segregated fraction and extraneous nanoparticle addition by fitting experimentally obtained thermal conductivity, electrical conductivity, and thermopower. In opposition to the previous speculation that segregated phases intensify phonon scattering, we found that lattice thermal conductivity was increased by the phase segregation, which is difficult to avoid due to the miscibility gap. We selected extraneous TiO2 nanoparticles dissimilar to the host materials as additives to reduce lattice thermal conductivity. Our experimental results showed the maximum ZT was improved from ∼0.9 without the nanoparticles to ∼1.1 with 2 and 5 vol % TiO2 nanoparticles at 550 °C. According to our theoretical analysis, this ZT increase by the nanoparticle addition mainly comes from suppressed lattice thermal conductivity in addition to lower bipolar thermal conductivity at high temperatures. The upper bound of ZT was predicted to be ∼1.8 for the ideal case of no phase segregation and addition of 5 vol % TiO2 nanoparticles. We believe this study offers a new direction toward improved thermoelectric performance of Mg2(Si,Sn).

Electroreflectance imaging of gold-H3PO4 supercapacitors. Part I: experimental methodology.

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.