Sensor Design Research Articles

Poly(3,4-ethylenedioxythiophene) and Poly(3-octylthiophene-2,5-diyl) Molecules as Composite Transducers in Potentiometric Sensors—Synthesis and Application

The aim of this paper is to investigate the influence of the molecules of conducting polymers on the properties of potentiometric sensors. Two conducting polymers, poly(3-octylthiophene-2,5-diyl) and poly(3,4-ethylene-1,4-dioxythiophene), were compared in the context of the design of ion-selective electrodes. This study offers a comparison of the most popular conducting polymers in the context of the design of potentiometric sensors. Firstly, the properties of both materials, such as their microstructure, electrical performance, wettability, and thermic properties, were examined. Subsequently, conducting polymers were applied as transducer layers in potassium-selective sensors. The properties of both groups of sensors were evaluated using the potentiometry method. Research has shown that the presence of poly(3-octylthiophene-2,5-diyl) (POT) in the transducer layer makes it superhydrophobic, leading to a long lifetime of sensors. On the other hand, the addition of poly(3,4-ethylene-1,4-dioxythiophene) polystyrene sulfonate (PEDOT:PSS) allows for the enhancement of electrical capacitance parameter values, which beneficially influence the stability of the potentiometric response of sensors. Both examined conducting polymers turned out to be perfect materials for transducer layers in potentiometric sensors, each being responsible for enhancing different properties of electrodes.

Flexible Strain Sensors with Ultra-High Sensitivity and Wide Range Enabled by Crack-Modulated Electrical Pathways

Abstract This study presents a breakthrough in flexible strain sensor technology with the development of an ultra-high sensitivity and wide-range sensor, addressing the critical challenge of reconciling sensitivity with measurement range. Inspired by the structure of bamboo slips, we introduce a novel approach that utilises liquid metal to modulate the electrical pathways within a cracked platinum fabric electrode. The resulting sensor demonstrates a gauge factor greater than 108 and a strain measurement capability exceeding 100%. The integration of patterned liquid metal enables customisable tuning of the sensor’s response, while the porous fabric structure ensures superior comfort and air permeability for the wearer. Our design not only optimises the sensor’s performance but also enhances the electrical stability that is essential for practical applications. Through systematic investigation, we reveal the intrinsic mechanisms governing the sensor’s response, offering valuable insights for the design of wearable strain sensors. The sensor’s exceptional performance across a spectrum of applications, from micro-strain to large-strain detection, highlights its potential for a wide range of real-world uses, demonstrating a significant advancement in the field of flexible electronics.

Design and Manufacturing of Shear Stress Sensor for High-Temperature Applications With Embedded Capacitive Floating Unit

Design and Manufacturing of Shear Stress Sensor for High-Temperature Applications With Embedded Capacitive Floating Unit

Additively manufactured microwave sensor for glucose level detection in saliva.

In this paper, a novel realization of an ink-on-glass microwave sensor for biomedical applications is proposed. The Aerosol Jet Printing (AJP) technology is leveraged to implement a compact single-layer coplanar waveguide sensor featuring arc-shaped interdigital fingers that can accommodate a droplet of the Material-Under-Test (MUT). Such geometry provides a high sensitivity to even a very small deviation of MUT`s electrical properties when placed as a superstrate. An application towards the detection of trace amounts of glucose in saliva, which is a biomarker for diabetes, is showcased. The design and fabrication process of an exemplary sensor is discussed in detail. A circular geometry feature is introduced that helps a droplet to lie over the sensitive region due to wettability difference of glass substrate and silver ink. Sensor operating in K-band is developed providing a tradeoff between circuit size and droplet volume. The study is conducted for an artificial saliva requiring roughly a 0.5 µL droplet where changes in mixture content are proportional to relative changes of sensor`s transmission coefficient in a broad frequency range for occupied vs. empty states. The obtained results show that 10mg of glucose per 100ml of saliva can be easily distinguished in a frequency range of 20-30GHz, whereas a monotonical change is visible for frequencies 20-26GHz, which indicates the applicability of this sensor towards the detection of saliva-glucose levels and potential application in the detection of small amounts of other substances in liquids.

Abstract I006: Programmable RNA sensors for internal states and external cues in living cells

Abstract Cell and gene therapies against cancer could be empowered by sensors that produce proteins in response to specific intracellular markers (e.g., expressing a cytotoxic payload in response to oncogenes) or extracellular cues (e.g., expressing a chimeric antigen receptor only when T cells are in the tumor microenvironment). An emerging delivery modality is in vitro transcribed mRNA, which holds the promise of superior safety and affordability than more conventional DNA vectors. While synthetic biology tools have predominantly relied on DNA-level regulations, here I will share two sensor designs uniquely compatible with mRNA delivery. First, for intracellular markers, a promising strategy is sensing mRNA transcripts. It is now straightforward to obtain single-cell transcriptomics, which are highly informative of cell type/state. If we can build RNA sensors to output arbitrary proteins in direct response to specific RNAs, we will access virtually any cell type/state for research and therapy. Furthermore, RNA base pairing would facilitate programmable designs. Previous RNA sensors relied on base pairing-induced conformational changes of RNA, and have met with limited success in mammalian cells. As an alternative strategy, we took advantage of endogenous ADAR (adenosine deaminases acting on RNA) enzymes that specifically edit adenosines to inosines (treated as guanosines during translation) in dsRNA, and created RADAR (RNA sensing using ADAR, Kaseniit et al., 2023). RADAR relies on the formation of dsRNA between the sensor RNA we introduce and the endogenous input RNA we’d like to detect; ADAR then edits a strategically positioned stop codon (UAG) into a tryptophan codon (UIG), enabling the translation of the downstream output coding sequence. We validated and optimized RADAR, and reported its compatibility with human/mouse transcriptomes. We showed that RADAR is programmable and modular. It also uniquely enables compact implementation of AND logic, important for integrating the inputs from multiple markers and unambiguously identifying cells of interest. We have since demonstrated RADAR in a variety of contexts including cancer lines with an elevated oncogene. Second, while transcripts mark cell types/states, extracellular ligands reflect the other key aspect that payload production could be conditioned on – the microenvironment. Although our field has plenty of synthetic receptors to offer when it comes to sensing ligands, all previously engineered modular ones require DNA-level operation. Inspired by the power of ADAR editing, we created LIDAR (Ligand-Induced Dimerization Activating RNA editing, Zhang & Mille et al., 2024). LIDAR brings an attenuated ADAR to the proximity of a stop codon in a ligand-dependent fashion, and is compatible with diverse inputs, outputs, and cellular contexts. We are in the process of demonstrating RADAR and LIDAR to improve cell therapies and enable non-invasive diagnostics. Citation Format: Xiaojing Gao. Programmable RNA sensors for internal states and external cues in living cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr I006.

Low Power Gas Sensors: From Structure to Application.

Gas sensors are pivotal across industries, encompassing environmental monitoring, industrial safety, and healthcare. Recently, a surge in demand for low power gas sensors has emerged, driven by the huge need for applications in portable devices, wireless sensor networks, and the Internet of things (IoT). The practical realization of a densely interconnected sensor network demands gas sensors to have low power consumption for energy-efficient operation. This Perspective offers a comprehensive overview of the progress of low-power sensors for gas and volatile organic compound detection, with a keen focus on the interplay between sensing materials (including metal oxide semiconductors, metal-organic frameworks, and two-dimensional materials), sensor structures, and power consumption. The main gas sensing mechanisms are discussed, and we delve into the mechanisms for achieving low power consumption including material properties and sensor design. Furthermore, typical applications of low power gas sensors are also presented, including wearable technology, food safety, and environmental monitoring. The review will end by discussing some open questions and ongoing needs.

Identifying and Assessing Physiological Biomarkers for Food and Energy Intake: A Systematic Review with Meta-Analysis Protocol

Background Traditional dietary assessments are often inaccurate and prone to self-reporting biases. Tracking the physiological responses associated with eating and digestion events via wearable technologies may provide an effective approach for continuously monitoring food intake and estimating energy consumption. Eating and digestion are accompanied by a series of changes in the heart rate, skin temperature, blood oxygen saturation, and blood pressure. These changes can be tracked by wearable devices, such as smartwatches, which have been widely accepted in the market. This systematic review is the first to evaluate the effectiveness of tracking such physiological biomarkers in differentiating between high- and low-calorie meals, potentially paving the way for more accurate dietary monitoring. Methods Following the PRISMA-P guidelines, we will conduct a systematic literature search through MEDLINE, EMBASE, and PubMed for clinical trials that investigated physiological responses following meal intake in healthy subjects. Two independent reviewers will screen and select articles based on pre-defined eligibility criteria, with a third review to resolve any discrepancies. This will be followed by data extraction and quality assessment of the included studies. Statistical analyses, including meta-analyses, will be performed using R Studio software. Our primary outcome will be the comparison of physiological biomarkers before and after meal intake, while secondary outcomes will include comparisons of physiological biomarkers between high- and low-calorie meal consumption and the correlation between the caloric content of consumed meals and postprandial physiological changes. Discussion This systematic review and meta-analysis will identify physiological indicators for eating events and inform the design of wearable sensors that estimate food intake in healthy subjects. Systematic Review Registration PROSPERO Registration ID: CRD42024544353

Porous and Homogeneous Nanoheterojunction-Accumulating PdO@ZnO Structure for Exhaled Breath Ammonia Sensing.

The functional gas sensor device plays a pivotal role in intelligent medical treatment, among which metal oxide semiconductors are widely studied because of their inexpensiveness and ease of fabrication. However, the metal oxide sensors present a significant challenge in detecting NH3 at ppm levels within complex exhaled gases. Herein, the ZnO/PdO-x series were prepared by in situ loading palladium particles and calcining using nano-ZIF-8 as a precursor, which not only provided more transport path for ammonia adsorption but also achieved homogeneous nanoheterojunction accumulation structure. The tailor-made ZnO/PdO-2 sensor exhibits the optimum gas sensitivity, with a response value of 5.56 for 100 ppm of NH3 at 160 °C and a lower detection limit of 0.75 ppm. Particularly, it has a clear quantitative response to the actual exhaled gas of liver and kidney patients. By elucidating the intrinsic link between the in situ loading of MOF templates and the sensing mechanism, it is expected to broaden the rational design of metal-oxide sensors and thus provide an effective method for clinical detection.

Research on Voltage Self-Calibration Sensing Technology Based on Measurement Circuit Topology Changes

In capacitance-coupled voltage-sensing technology, the degree of coupling capacitance is affected by the sensing area, relative position deviation, and other factors, and thus the measurement coefficient is often difficult to determine accurately and presents greater implementation difficulties in actual deployment. This paper proposes a dynamic reconfiguration based on the measurement circuit topology of the voltage sensor adaptive calibration method in order to measure voltage sensor gain in the process of automatic measuring. Firstly, the basic principle of voltage measurement is introduced, and the self-calibration method is proposed, considering the influence of the sensing area and the relative position error on the change in the coupling capacitance. On this basis, the influence of calibration accuracy on sensor structure parameters is analyzed using network sensitivity analysis, and the parameter selection principle is given, according to which the selection criterion of parameter optimization is formulated to complete the sensor design. By analyzing the coupling effect of the three-phase measurement, the installation method of the sensing structure is proposed. An experimental platform is built to test the accuracy of the voltage measurement of the sensor under laboratory conditions. The experimental results show that the maximum relative error of the voltage measurement amplitude is 2.24%. In order to verify the feasibility of the sensor technology designed, the measurement models that integrate communication, acquisition, and processing are installed on both ends of the circuit breaker wire, and the voltage waveform generated during the circuit breaker closing process is recorded in real time. The experimental results show that the sensor technology can accurately record the voltage waveform of the signal to be measured, and the feasibility of its application in switchgear equipment signal measurement is preliminarily verified by the results.

Modeling Electrochemical Impedance Spectroscopy Using Time-Dependent Finite Element Method

A time-dependent electrochemical impedance spectroscopy (EIS) model is presented using the finite element method (FEM) to simulate a 2D interdigitated electrode in an aqueous NaCl electrolyte. Developed in COMSOL Multiphysics, the model incorporates ion transport, electric field distribution, Stern layer effects, and electrode sheet resistance, governed by the Poisson and Nernst–Planck equations. This model can predict the transient current response to an applied excitation voltage, which gives information about the dynamics of the electrochemical system. The simulation results are compared with the experimental data, reproducing key features of the measurements. The transient current response indicates the need for multiple excitation cycles to stabilize the impedance measurement. At low frequencies (<1 kHz), the voltage drop at the Stern layer is significant, while at higher frequencies (>100 kHz), the voltage drop due to sheet resistance dominates. Moreover, the amplitude of the excitation voltage influences the EIS measurement, higher amplitudes (above 0.1 V) lead to non-linear impedance behavior, particularly at low ion concentrations. Discrepancies at low frequencies suggest that Faradaic processes may need to be incorporated for improved accuracy. Overall, this model provides quantitative insights for optimizing EIS sensor design and highlights critical factors for high-frequency and low-concentration conditions, laying the foundation for future biosensing applications with functionalized electrodes.

Design and fabrication of a novel resisto-capacitive sensor for the detection of the freshness of fish

Design and fabrication of a novel resisto-capacitive sensor for the detection of the freshness of fish

Improved Large-Scale Synthesis of Acridonylalanine for Diverse Peptide and Protein Applications.

Fluorescent unnatural amino acids give biochemists, biophysicists, and bioengineers new ways to probe the properties of proteins and peptides. Here, the synthesis of acridon-2-ylalanine (Acd) is optimized for large-scale production to enable ribosomal incorporation through genetic code expansion (GCE), and fluorenylmethoxycarbonyl (Fmoc)-protected Acd is prepared for solid-phase peptide synthesis (SPPS). We demonstrate the utility of Acd in several applications: first, Acd quenching by Tyr is used in the design of fluorescent protease sensors made by SPPS. Second, we demonstrate Acd incorporation into a lanthanide-binding peptide that is generated either by GCE or by SPPS and demonstrate the utility of Acd for sensitizing the emission of Eu3+. Finally, Acd is inserted into the intrinsically disordered protein, α-synuclein, using GCE and used to study ion binding and aggregation.

Negative longitudinal piezoelectric effect and electric auxetic effect in ferroelectric HfO2 and related fluorite-structure ferroelectrics

Unusual negative longitudinal piezoelectric effect (NLPE) and electric auxetic effect (EAE) have essential implications for designs of piezoelectric sensors and actuators. The emerging ferroelectric HfO2 is recently discovered to have both effects, while the underlying physical mechanisms remain elusive. To understand and regulate these intriguing effects, it is crucial to investigate the piezoelectricity in ferroelectric HfO2 and related fluorite-structure ferroelectrics. Here, we corroborate using first-principles calculations that all twelve fluorite-structure ferroelectrics covered in this study possess the NLPE. A chemical tendency of piezoelectricity is demonstrated, i.e., the larger the “iconicity,” the stronger the NLPE. The structural origin is attributed to the predominant influence of the triple-coordinated anion displacement, namely, the more “ionic” fluorite-structure ferroelectrics exhibit larger anion displacement under a pressure or strain, which gives rise to a more negative internal-strain contribution dominating over the positive clamped-ion contribution and hence a stronger NLPE. Moreover, we confirm several electric auxetic materials in fluorite-structure ferroelectrics with finite electric field calculations. We find that the piezoelectricity of electric auxetic materials is suppressed by the external electric field along the polar direction, since it weakens the bonding heterogeneity. The unraveled fundamental understanding of the NLPE and EAE in this study may profoundly benefit the design and application of fluorite-structure ferroelectrics.

High-precision ethanol concentration microsensor with global spectra aided by the multi-layer perceptron

Whispering gallery mode (WGM) resonators can be used for precision measurement thanks to their high sensitivity, small size, and fast response time. Nevertheless, the design of such sensors is usually achieved by selecting a typical single-mode tracking method, which leads to low utilization of a great deal of information in the resonance spectrum and affects the precision. Here, we use the multi-layer perceptron (MLP) deep learning algorithm to train the global spectra and realize the high-precision measurement of ethanol concentration. Firstly, a large number of transmission spectra of different ethanol concentrations are collected and directly used as the original data sets. Secondly, the MLP algorithm is used for training and testing. Finally, the local feature dimension is extracted from the global features of the spectrum for prediction. The results show that the prediction accuracy of the global spectra sensing is 99.81%, which is 13.02% higher than that of extracting 10 local features. In addition, the prediction accuracy of the MLP is compared with four other commonly used machine learning (ML) algorithms, and the results show that the MLP algorithm has the highest prediction accuracy. Therefore, the high-precision ethanol concentration sensor proposed in this paper opens a new way for intelligent optical micro-resonator sensing.

Carbazolyl-Decorated Metal-Organic Framework with a High Fluorescent Quantum Yield for Detection and Photocatalytic Degradation of Organic Contaminants.

A carbazolyl-based fluorescent metal-organic framework with deep-blue light emission and a high quantum yield (88%) has been screened out by changing the ratio of mixed ligands, named Cz-MOF-2, which was constructed by ZrO4(OH)4 clusters, 3-(9-ethyl-9H-carbazol-3-yl)-4-methylthieno[2,3-b]thiophene-2,5-dicarboxylate (H2ECMTDC) and 3,4-dimethyl-dihydrothieno[2,3-b]thiophene-2,5-dicarboxylate (H2DMTDC). Cz-MOF-2 has excellent sensitivity in fluorescent sensing of 2,6-dichloro-4-nitroaniline (DCN), nitrofurazone (NZF), and nitrofurantoin (NFT) with detection limits of 3.37 × 10-7, 1.64 × 10-5, and 1.76 × 10-5 M, respectively. The quenching mechanisms involving electron transfer and competitive absorption were comprehensively elucidated through DFT calculations and UV absorption experiments. Cz-MOF-2 has been fabricated into a rapidly regenerated composite membrane with PVDF as a visualized fluorescent sensor for DCN. Furthermore, the carbazolyl groups covalently modified in the framework endowed Cz-MOF-2 with a suitable band gap (2.58 eV) for photocatalytic degradation of organic contaminants. It demonstrated superior photocatalytic efficiency compared to UiO-66 and NH2-UiO-66 in the degradation of tetracycline (TC), and the removal efficiency was up to 85% (20 mg of catalyst, 50 mL, 20 mg/L TC solution and 180 min) under simulated sunlight irradiation using a 300 W Xe lamp. Photoelectrochemical tests demonstrate that Cz-MOF-2 exhibits high photocurrent density and low charge transfer resistance, which provide evidence for the efficient charge separation capability. Radical trapping experiments and ESR detection have substantiated that ·O2- and h+ are the primary active species involved in this photocatalytic reaction. This work highlights the potential of MOFs in the targeted design and development of fluorescent sensors and photocatalysts for environmental detection and remediation.

Silk Flocked Flexible Sensor Capable of Wide-Range and Sensitive Pressure Perception.

In recent years, there have been advancements in high-performance soft sensors with simultaneous moderate sensitivity and wide linearity. However, it remains challenging to combine high-efficiency production and high performance for soft sensors. The skin and hair structure provide an elegantly simple sensing model, where hair acts as signal receptors and basal skin acts as signal processors. Herein, we used efficient electrostatic flocking and extrusion printing to engineer comb-shaped electrodes with conductive polydimethylsiloxane (PDMS) flocked with conductive silk fibers as a biomimetic flexible sensor. The mechanical and electrical properties of modified PDMS and silk fibers were characterized to optimize the functionalization process. The sensor unit exhibited a high linear range up to 2,000 kPa and a competitively good sensitivity of 0.0285 kPa-1 in the contact mode with conductive materials, as well as good resolution in the noncontact mode. Such sensors and a sensor array demonstrated potential applications for detecting pressure disturbances from acoustic activity and for human-robot interactions. We anticipate that the straightforward design and facile fabrication of soft sensors with vertical fibrous morphology for perception will open new avenues for the next generation of high-performance soft sensors integrated with artificial intelligence.

Assessing the effectiveness of vegetation indices in detecting forest disturbances in the southeast Amazon

Amazon forests are becoming increasingly vulnerable to disturbances such as droughts, fires, windstorms, logging, and forest fragmentation, all of which lead to forest degradation. Nevertheless, quantifying the extent and severity of disturbances and their cumulative impact on forest degradation remains a significant challenge. In this study, we combined multispectral data from Landsat sensors with hyperspectral data from the Earth Observing-One (Hyperion/EO-1) sensor to evaluate the efficacy of multiple vegetation indices in detecting forest responses to disturbances in an experimentally burned forest in southeastern Amazonia. Our experimental area was adjacent to an agricultural field and consisted of three 50-ha treatments – an unburned Control, a plot burned every three years, and a plot burned annually from 2004 to 2010. All plots were monitored to assess vegetation recovery after fire disturbance. These areas were also affected by three drought events (2007, 2010, and 2016) over the study period. We evaluated a total of 18 Vegetation Indices (VI), one unique to Landsat, 12 unique to Hyperion/EO-1, and five commons to both satellites (i.e., 6 total from Landsat and 17 from Hyperion). We used linear models (LM) to evaluate how changes in ground observations of forest structure (biomass, leaf area index [LAI], and litter production) associated with fire were captured by the two VIs most sensitive to forest degradation. Our results indicate that the Plant Senescence Reflectance Index (PSRI) derived from Hyperion/EO-1 was the most sensitive to vegetation changes associated with forest fires, increasing by 94% in burned vs. unburned forests. Of the Landsat-derived VIs, we found that the Green-Red Normalized Difference (GRND) were the most sensitive to forest degradation by fire, showing a marked decline (87%) in the burned plots compared with the unburned Control. However, compared to PSRI, the GRND was a better predictor of changes associated with fire, both in the forest interior or forest edge, for the three ground variables: biomass stocks (r2 = 0.5–0.8), LAI (r2 = 0.8–0.9), and litter production (r2 = 0.4–0.7). This study demonstrate that VIs can detect forest responses to fire and other disturbances over time, highlighting the relative strengths of each VI. In doing so, it shows how the integration of multispectral and hyperspectral data can be useful for monitoring tropical forest degradation and recovery. Moreover, it provides valuable insights into the limitations of existing approaches, which can inform the design of next-generation sensors for global forest monitoring.

A value-of-information-based optimal sensor placement design framework for cost-effective structural health monitoring (with application to miter gate monitoring)

Structural health monitoring (SHM) involves collecting information to assess the health of a structure, typically to guide risk-informed maintenance decision-making or predict limit state behavior throughout its lifespan. Although the value of information (VoI) obtained from an SHM system can facilitate improved decision-making, it is important to estimate its overall utility by considering the costs involved in designing, developing, installing, maintaining, and operating the system. A feasible SHM system provides greater expected returns resulting from data-informed lifecycle management decisions than the cost of the SHM system design, fabrication, deployment, operation, and maintenance. That is, since data acquisition is a precursor to data-informed decision-making, the design of an SHM system governs its feasibility. Such cost-benefit analyses are a current topic of research in the SHM community. One approach that has been proposed for these analyses is a preposterior decision analysis using the VoI metric. In this paper, we propose a sensor optimization framework that maximizes the VoI over the structure’s lifecycle, constrained by a pre-decided maintenance policy. We use two different VoI metrics: the traditional expected VoI and a gambling-theory-inspired normalized expected-savings-to-investmentrisk ratio. We introduce three time-normalized, unitless VoI metrics that are valuable for evaluating the performance of an SHM design over an extended period. Furthermore, we consider the effect of different risk profiles on the overall optimal sensor design, recognizing that the cost of decision-making depends on the utility and risk perception of the decision-maker. We conduct a detailed analysis on the marginal utility gain of gathered information as we add more sensors, observing the utility to diminish in line with the law of diminishing returns as the information content increases. This framework is applied to the design of an SHM system used for monitoring miter gates.

Theoretical design of an electrically and thermally dual-controlled double-band absorber based on graphene and InSb

A novel, to our knowledge, double-band metamaterial absorber based on graphene and InSb is proposed in the terahertz regime. The amplitude and center frequency of the absorber can be electrically tuned by adjusting the graphene chemical potential and thermally controlled by varying the InSb temperature independently. First, owing to the bright–bright mode coupling effect, the average absorption rate of double-band absorber can reach 97% in the FDTD simulation. Meanwhile, the physical absorption mechanism can be analyzed theoretically by the radiating two-oscillator model. Second, when the chemical potential of graphene increases, the absorption rates of the absorber decrease gradually, and the absorption frequencies are almost unchanged. Moreover, if the temperature of InSb increases, the absorption frequencies and rates of absorber increase simultaneously. Finally, the impact of parameters on the absorption spectra, and the excellent sensing performance of the dual-band absorber as a refractive index sensor, are further discussed. This work provides a theoretical basis for the designs of dual-tunable absorbers and sensors.

Comprehensive and Robust Anti-Jamming Dual-Electrode Pair Sensor.

Capacitive flexible sensors often encounter instability caused by temperature fluctuations, electromagnetic interference, stray capacitance effects, and signal noise induced by ubiquitous vibrations. The challenge lies in achieving comprehensive anti-jamming abilities while preserving a simplistic structure and manufacturing process. To tackle this dilemma, a straightforward and effective design is utilized to achieve comprehensive and robust anti-jamming properties in capacitive sensors. Electrospinning thermoplastic polyurethane (TPU) fiber mats soak with ionic liquid (IL) to create a co-continuous structure (TPU@IL) with high ionic conductivity and dielectric constant, which acts as the sensing units. The sensing mechanism of the TPU@IL with multiple electrode pairs encapsulated by polyethylene terephthalate (PET) is systematically elucidated. The optimal dual-electrode pair design for capacitive and resistive sensors, which have different sensitivities to temperature and stress, simultaneous realizes temperature-stress dual-mode sensing. Remarkably, the sensitivity curve of the TPU@IL/PET capacitive sensor exhibits an intriguing rarely reported S-shape with an adjustable step stress point. No liquid leakage even during extensive stress-strain cycling (>4000 cycles). Despite a slight compromise in sensitivity and response time, the TPU@IL/PET sensor demonstrates exceptional electromechanical stability, reliability, and powerful anti-jamming abilities against various interferences. A simple yet innovative sensor design enhances the performance and applicability of capacitive sensors in challenging environments.