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Introduction: Terebinth (Pistacia terebinthus) belongs to the same botanical family as pistachio (Pistacia vera) and cashew (Anacardium occidentale). Although it is known that there is cross-sensitivity between pistachio and cashew, the cross-sensitivity of terebinth with pistachio or cashew has not been investigated. The objective of our study was to evaluate the sensitivity to terebinth in children with pistachio sensitivity. Methods: This study was conducted between September 2021 and June 2022 at Adıyaman University Faculty of Medicine Hospital. It analyzed the results of children who underwent skin prick testing (SPT) for food allergy. Of the 712 food skin prick tests reviewed, 27 children were identified with pistachio sensitivity. Prick tests with commercial extract for cashew and prick-to-prick tests for terebinth were applied to these children. Results: The median age was two, and 78% were male. Of the children with pistachio sensitivity, 96% demonstrated cross-sensitivity to terebinth and 100% to cashew. There was a strong correlation between the size of SPT responses in pistachio, cashew, and terebinth. Only four children had previously consumed terebinth, and two of these children had allergic reactions. Conclusion: Our study demonstrates a high cross-sensitivity between terebinth, pistachio, and cashew. We recommend that individuals with pistachio or cashew allergy/sensitivity avoid terebinth until tests confirm it is safe to consume. Further studies are needed to demonstrate the clinical significance of this cross-sensitivity and identify the major allergen involved. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers. However, their high operating temperature and high power consumption restrict their deployment in battery-powered devices. This review examines the underlying principles of chemiresistive gas sensors. It comprehensively summarizes recent advances in low-power gas sensors for the detection of dissolved fault characteristic gases (H2, C2H2, CH4, C2H6, C2H4, CO, and CO2). Emphasis is placed on the synthesis methods of sensitive materials and their properties. The investigations have yielded substantial experimental data, indicating that adjusting the particle size and morphology structure of the sensitive materials and combining them with noble metal doping are the principal methods for enhancing the sensitivity performance and reducing the power consumption of chemiresistive gas sensors. Additionally, strategies to overcome the significant challenge of cross-sensitivity encountered in applications are provided. Finally, the future development direction of chemiresistive gas sensors for DGA is envisioned, offering guidance for developing and applying novel gas-sensitive sensors in transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

Highlights The types, working principles, advantages and limitations of pattern recognition methods based on chemiresistive gas sensor array are reviewed and discussed comprehensively. Outstanding and novel advancements in the application of machine learning methods for gas recognition in different important areas are compared, summarized and evaluated. The current challenges and future prospects of machine learning methods in artificial olfactory systems are discussed and justified.

Highlights: The types, working principles, advantages and limitations of pattern recognition methods based on chemiresistive gas sensor array are reviewed and discussed comprehensively. Outstanding and novel advancements in the application of machine learning methods for gas recognition in different important areas are compared, summarized and evaluated. The current challenges and future prospects of machine learning methods in artificial olfactory systems are discussed and justified. As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications. [ABSTRACT FROM AUTHOR]

Thermal conductivity sensors face an omnipresent cross-influence through varying humidity levels in real-life applications. We present the results of investigations on the influence of humidity on a hydrogen thermal conductivity sensor and approaches for predicting the behavior of thermal conductivity towards humidity. A literature search and comparison of different mixing equations for binary gas mixtures were carried out. The theoretical results were compared with experimental results from three different thermal conductivity sensors with mixtures of water vapor in nitrogen. The mixing equations show a large discrepancy between each other. Some of the models predict a continuously decreasing thermal conductivity and some predict an increasing thermal conductivity for increasing levels of humidity. Our measurements indicate an increase in thermal conductivity followed by a decrease after reaching a peak value. It is shown that the measured behavior is reproducible with different sensors. Depending on the sensor, this corresponds to an error up to 2 vol.% in the measured hydrogen value. The measured behavior is consistent with only one of the three models. Compared to this model, our own sensor shows a maximum deviation of 1.4%. Mixing equations for gas mixtures must be chosen carefully, taking into consideration whether mixing partners include polar or non-polar molecules. Some simplified mixing equations cannot be used to calculate the thermal conductivity of water vapor in air or nitrogen. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers. However, their high operating temperature and high power consumption restrict their deployment in battery-powered devices. This review examines the underlying principles of chemiresistive gas sensors. It comprehensively summarizes recent advances in low-power gas sensors for the detection of dissolved fault characteristic gases (H2, C2H2, CH4, C2H6, C2H4, CO, and CO2). Emphasis is placed on the synthesis methods of sensitive materials and their properties. The investigations have yielded substantial experimental data, indicating that adjusting the particle size and morphology structure of the sensitive materials and combining them with noble metal doping are the principal methods for enhancing the sensitivity performance and reducing the power consumption of chemiresistive gas sensors. Additionally, strategies to overcome the significant challenge of cross-sensitivity encountered in applications are provided. Finally, the future development direction of chemiresistive gas sensors for DGA is envisioned, offering guidance for developing and applying novel gas-sensitive sensors in transformer fault diagnosis.

Thermal conductivity sensors face an omnipresent cross-influence through varying humidity levels in real-life applications. We present the results of investigations on the influence of humidity on a hydrogen thermal conductivity sensor and approaches for predicting the behavior of thermal conductivity towards humidity. A literature search and comparison of different mixing equations for binary gas mixtures were carried out. The theoretical results were compared with experimental results from three different thermal conductivity sensors with mixtures of water vapor in nitrogen. The mixing equations show a large discrepancy between each other. Some of the models predict a continuously decreasing thermal conductivity and some predict an increasing thermal conductivity for increasing levels of humidity. Our measurements indicate an increase in thermal conductivity followed by a decrease after reaching a peak value. It is shown that the measured behavior is reproducible with different sensors. Depending on the sensor, this corresponds to an error up to 2 vol.% in the measured hydrogen value. The measured behavior is consistent with only one of the three models. Compared to this model, our own sensor shows a maximum deviation of 1.4%. Mixing equations for gas mixtures must be chosen carefully, taking into consideration whether mixing partners include polar or non-polar molecules. Some simplified mixing equations cannot be used to calculate the thermal conductivity of water vapor in air or nitrogen.

The use of heterojunctions is a promising solution to the problem of cross‐sensitivity in gas sensors. In this work, a carbon monoxide sensor based on the CuO/TiO2 heterojunction was designed and fabricated. Due to the good adsorption properties of CuO materials to CO, and the heterojunction interface charge transfer, the CuO/TiO2 thin film sensor exhibits high sensitivity to CO at room temperature. The response is as high as 10.8–200 ppm CO, about 10 times its response to H2. Interference from H2 is greatly reduced by optimizing the structure of the CuO/TiO2 heterojunction. This reliable detection of carbon monoxide with excellent discrimination against H2 is of great significance for the development of CO gas sensors. [ABSTRACT FROM AUTHOR]

The design, fabrication, and measurement of a microelectromechanical system (MEMS) three-axis magnetic field sensor (MFS) based on the commercial complementary metal oxide semiconductor (CMOS) process are investigated. The MFS is a magnetic transistor type. The performance of the MFS was analyzed employing the semiconductor simulation software, Sentaurus TCAD. In order to decrease the cross-sensitivity of the three-axis MFS, the structure of the MFS is planed to accommodate two independent sensing components, a z-MFS utilized to sense magnetic field (M-F) in the z-direction and a y/x-MFS composed of a y-MFS and a x-MFS to be utilized to sense M-F in the y- and x-directions. The z-MFS incorporates four additional collectors to increase its sensitivity. The commercial 1P6M 0.18 μm CMOS process of the Taiwan Semiconductor Manufacturing Company (TSMC) is utilized to manufacture the MFS. Experiments depict that the MFS has a low cross-sensitivity of less than 3%. The sensitivities of z-, y-, and x-MFS are 237 mV/T, 485 mV/T, and 484 mV/T, respectively. [ABSTRACT FROM AUTHOR]

In response to increasing ecological and environmental challenges in arid areas, it is of great significance to investigate the ecosystem service value (ESV), accompanying the changes in ecological sensitivity for the protection of ecologically vulnerable areas. Our analysis seeks to elucidate the ESV and ecological sensitivity changes in the middle and lower reaches of the Shiyang River to determine the trends and influencing factors of ESV under changing land use patterns. The key findings include: (1) From 1995 to 2020, the ESV in the study area witnessed fluctuations, culminating in an overall decline of 1.249 × 108 yuan. (2) In 2020, sensitivity coefficients (CSs) for ESV were as follows: 0.4335 for grassland, 0.2586 for farmland, and 0.1170 for unused land within the study area. Furthermore, coefficients of improved cross-sensitivity (CICSs) for the reciprocal transformation of farmland, grassland, and unused land were 1.10, 1.18, and 1.54, respectively, indicating the pivotal role of the three land types in driving ESV fluctuations.

Abstract Digital Twins are essential in establishing intelligent asset management for an asset or machine. They can be described as the bidirectional communication between a cyber representation and a physical asset. Predictive Maintenance is dependent on the existence of three data sets: Fault history, Maintenance/Repair History, and Machine Conditions. Current Digital Twin solutions can fail to simulate the behaviour of a faulty asset. These solutions also prove to be difficult to implement when an asset’s fault history is incomplete. This paper presents the novel methodology, LIVE Digital Twin, to develop Digital Twins with the focus of Predictive Maintenance. The four phases, Learn, Identify, Verify, and Extend are discussed. A case study analyzes the relationship of component stiffness and vibration in detecting the health of various components. The Learning phase is implemented to demonstrate the process of locating a preliminary sensor network and develop the faulty history of a Sand Removal Skid assembly. Future studies will consider fewer simplifying assumptions and expand on the results to implement the proceeding phases.

Digital Twins are essential in establishing intelligent asset management for an asset or machine. They can be described as the bidirectional communication between a cyber representation and a physical asset. Predictive Maintenance is dependent on the existence of three data sets: Fault history, Maintenance/Repair History, and Machine Conditions. Current Digital Twin solutions can fail to simulate the behaviour of a faulty asset. These solutions also prove to be difficult to implement when an asset's fault history is incomplete. This paper presents the novel methodology, LIVE Digital Twin, to develop Digital Twins with the focus of Predictive Maintenance. The four phases, Learn, Identify, Verify, and Extend are discussed. A case study analyzes the relationship of component stiffness and vibration in detecting the health of various components. The Learning phase is implemented to demonstrate the process of locating a preliminary sensor network and develop the faulty history of a Sand Removal Skid assembly. Future studies will consider fewer simplifying assumptions and expand on the results to implement the proceeding phases. [ABSTRACT FROM AUTHOR]

The evaluation of selectivity/cross-sensitivity is one of the most important tests for gas sensor development, particularly that based on chemiresistive technology. It is known that chemiresistive gas sensors suffer from low selectivity when they provide sensitivity to several analytes. Typically, selectivity testing involves independently assessing a sensor's response to a specific gas. However, there is a growing need to evaluate performance with interfering gases or gas mixtures since gas sensors are always exposed to gas mixtures in practice. Despite the great importance of selectivity characterization, currently, there are no standard methods of selectivity tests at conditions when target gas coexists with interfering gas compounds, which mimics real conditions. We outlined the four main methods researchers use to evaluate the cross-sensitivity of gas sensors. It highlights key aspects of selectivity test performance, assessment methodologies, and procedure features, attempting to classify them by their distinct characteristics. This review covers the essentials of gas properties, adsorption and desorption processes, and gas molecule interactions. Finally, we tried to address the lack of standardized protocols for evaluating chemiresistive gas sensors' cross-sensitivity to interfering gases and guide researchers.

Highly sensitive and multimodal sensors have recently emerged for a wide range of applications, including epidermal electronics, robotics, health‐monitoring devices and human–machine interfaces. However, cross‐sensitivity prevents accurate measurements of the target input signals when a multiple of them are simultaneously present. Therefore, the selection of the multifunctional materials and the design of the sensor structures play a significant role in multimodal sensors with decoupled sensing mechanisms. Hence, this review article introduces varying methods to decouple different input signals for realizing truly multimodal sensors. Early efforts explore different outputs to distinguish the corresponding input signals applied to the sensor in sequence. Next, this study discusses the methods for the suppression of the interference, signal correction, and various decoupling strategies based on different outputs to simultaneously detect multiple inputs. The recent insights into the materials' properties, structure effects, and sensing mechanisms in recognition of different input signals are highlighted. The presence of the various decoupling methods also helps avoid the use of complicated signal processing steps and allows multimodal sensors with high accuracy for applications in bioelectronics, robotics, and human–machine interfaces. Finally, current challenges and potential opportunities are discussed in order to motivate future technological breakthroughs. [ABSTRACT FROM AUTHOR]

We present and validate an equilibrium model based on the Poisson-Boltzmann equations that includes the main ingredients to simulate ion-sensitive membranes in the context of electrochemical potentiometric sensors with and without ionophores. With respect to phase boundary models, our model includes spatial resolution of the electrostatic potential and concentrations. The model enables the study of Nernstian and non-Nernstian equilibrium responses, helps improving the detection range and investigating selectivity and cross-sensitivity issues related to interferent ions in the sample solution. Therefore, the model is a useful support for the design of potentiometric microelectronic sensors and helps optimizing relevant membrane features such as ionic sites and ionophore concentration for best sensitivity and selectivity. [ABSTRACT FROM AUTHOR]

The degradation of sulfacetamide with the formation of sulfanilamide leads to a deterioration in the quality of pharmaceuticals. In this work, potentiometric sensors for the simultaneous determination of sulfanilamide, sulfacetamide and inorganic ions, and for assessing the degradation of pharmaceuticals were developed. A multisensory approach was used for this purpose. The sensor cross-sensitivity to related analytes was achieved using perfluorosulfonic acid membranes with poly(3,4-ethylenedioxythiophene) or polyaniline as dopants. The composite membranes were prepared by oxidative polymerization and characterized using FTIR and UV-Vis spectroscopy, and SEM. The influence of the preparation procedure and the dopant concentration on the membrane hydrophilicity, ion-exchange capacity, water uptake, and transport properties was investigated. The characteristics of the potentiometric sensors in aqueous solutions containing sulfanilamide, sulfacetamide and alkali metals ions in a wide pH range were established. The introduction of proton-acceptor groups and π-conjugated moieties into the perfluorosulfonic acid membranes increased the sensor sensitivity to organic analytes. The relative errors of sulfacetamide and sulfanilamide determination in the UV-degraded eye drops were 1.2 to 1.4 and 1.7 to 4%, respectively, at relative standard deviation of 6 to 9%. [ABSTRACT FROM AUTHOR]

We present an approach to simulate ion’s drift and diffusion in chemical sensors based on ion-selective-membranes (ISMs) in either ion-selective electrode structures or coupled to field-effect transistors. The model is used to analyze the sensitivity, selectivity, and transient response of ISMs in non-equilibrium conditions upon real-time concentration changes. Our simple implementation combines advanced features from semiconductor theory and analytical electrochemistry, such as the Schafetter-Gummel discretization scheme and Chang-Jaffé boundary conditions for ions at the interfaces, thus allowing to perform simulations beyond sensing. The results are in agreement with experimental transient responses reported in the literature. As relevant case studies, we examine the ISM preconditioning in miniaturized sensors and the electrostatic interaction between the FET channel and ISMs. In the first case, simulations reveal that calibration curves performed on incomplete ISM conditioning can lead to hysteretical responses when the ion affinity in the electrolyte and ISM is similar (e.g., with organic ions). In the second case, we find that the gate oxide field in contact with the ISM affects the device characteristics such that the ion concentrations not only change the FET threshold voltage but also the slope of its IV curve. This effect can be minimized by working in the subthreshold regime or using extended gates. [ABSTRACT FROM AUTHOR]

Accurate and real-time detection of characteristics gases is crucial for Dissolved Gas Analysis (DGA) in electrical transformers. Conventional assessments of Metal-Oxide Semiconductor (MOS) sensor selectivity generally analyze the response to singular interfering gases at identical concentrations, failing to represent the realistic intricacies of multi-component gases environments in DGA. Cross-sensitivity complicates the assessment of selectivity. This study proposes a method for re-assessing the selectivity of MOS sensor by considering cross-sensitivity in multi-component gases. Utilizing the Kendall correlation coefficient to analyze the relationship between characteristic gas concentrations and dynamic sensor responses, we determined sensor selectivity, optimal operating temperatures, and sensor interchangeability. Furthermore, we use a new term of quasi-2D sensor characteristics, which facilitated the creation of a complementary sensor array that significantly enhances gas selectivity and effectively mitigates MOS sensor saturation challenges. The complementary array, assessed by Linear Discriminant Analysis (LDA) projections and kernel density estimation, demonstrated enhanced selectivity relative to previous sensor arrays in the detection of multi-component gases. This method not only improves sensor selectivity but also mitigates cross-sensitivity in multi-component gas environments. Future research could focus on assessing a substantial array of MOS sensors, considering particular cross-sensitivity backgrounds, to improve artificial intelligence algorithms for accurate and real-time DGA detection. [Display omitted] • Correlation coefficient quantified sensor selectivity in mixed gas. • Assessment results addressed the impact of operating temperature in mixed gas. • Assessment process quantified sensor interchangeability in mixed gases. • Resistance alteration exhibited directionality between highly selective and mixed gases. [ABSTRACT FROM AUTHOR]

Abstract Highly sensitive and multimodal sensors have recently emerged for a wide range of applications, including epidermal electronics, robotics, health‐monitoring devices and human–machine interfaces. However, cross‐sensitivity prevents accurate measurements of the target input signals when a multiple of them are simultaneously present. Therefore, the selection of the multifunctional materials and the design of the sensor structures play a significant role in multimodal sensors with decoupled sensing mechanisms. Hence, this review article introduces varying methods to decouple different input signals for realizing truly multimodal sensors. Early efforts explore different outputs to distinguish the corresponding input signals applied to the sensor in sequence. Next, this study discusses the methods for the suppression of the interference, signal correction, and various decoupling strategies based on different outputs to simultaneously detect multiple inputs. The recent insights into the materials' properties, structure effects, and sensing mechanisms in recognition of different input signals are highlighted. The presence of the various decoupling methods also helps avoid the use of complicated signal processing steps and allows multimodal sensors with high accuracy for applications in bioelectronics, robotics, and human–machine interfaces. Finally, current challenges and potential opportunities are discussed in order to motivate future technological breakthroughs.

The design, fabrication, and measurement of a microelectromechanical system (MEMS) three-axis magnetic field sensor (MFS) based on the commercial complementary metal oxide semiconductor (CMOS) process are investigated. The MFS is a magnetic transistor type. The performance of the MFS was analyzed employing the semiconductor simulation software, Sentaurus TCAD. In order to decrease the cross-sensitivity of the three-axis MFS, the structure of the MFS is planed to accommodate two independent sensing components, a z-MFS utilized to sense magnetic field (M-F) in the z-direction and a y/x-MFS composed of a y-MFS and a x-MFS to be utilized to sense M-F in the y- and x-directions. The z-MFS incorporates four additional collectors to increase its sensitivity. The commercial 1P6M 0.18 μm CMOS process of the Taiwan Semiconductor Manufacturing Company (TSMC) is utilized to manufacture the MFS. Experiments depict that the MFS has a low cross-sensitivity of less than 3%. The sensitivities of z-, y-, and x-MFS are 237 mV/T, 485 mV/T, and 484 mV/T, respectively.

A set of 78 Rhizoctonia solani isolates from Shanghai, China, were examined for their sensitivity in vitro to four demethylase inhibitor (DMI) fungicides, epoxiconazole, difenoconazole, hexaconazole, and tebuconazole, by the mycelial growth rate method. The isolates were very sensitive to the four DMI fungicides. The ranges and means of the EC50 values were 0.004–0.045 and 0.014 ± 0.007 μg ml−1 for epoxiconazole, 0–0.103 and 0.010 ± 0.016 μg ml−1 for difenoconazole, 0.001–0.078 and 0.011 ± 0.013 μg ml−1 for hexaconazole, and 0.001–0.210 and 0.034 ± 0.035 μg ml−1 for tebuconazole, respectively. The frequency distribution of the EC50 values showed a normal curve for epoxiconazole and an approximately unimodal curve for difenoconazole, hexaconazole, and tebuconazole. There was no correlation between the sensitivities of the R. solani population to any two of the four fungicides (r < 0.3). In field trials, in 2015 and 2016, the control efficacies of hexaconzole, epoxiconazole, and tebuconazole were >90.0% at 75 g.a.i. ha−1, 80.0%–90.0% at 75 g.a.i. ha−1 and 80.0%–90.0% at 97 g.a.i. ha−1, respectively; these values are significantly higher (p < 0.05) than those obtained in this study for difenoconazole (65.0%–70.0% at 80 g.a.i. ha−1) and jinggangmycin (68.0%–71.0% at 150 g.a.i. ha−1). These results show that sensitivity data can be used to monitor and detect any variations in the sensitivity of the R. solani population to these four DMI fungicides, and demonstrate that epoxiconazole, hexaconazole, and tebuconazole are likely to be good alternatives to jinggangmycin for controlling rice sheath blight. [ABSTRACT FROM AUTHOR]

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors. [ABSTRACT FROM AUTHOR]

In recent years, smog and poor air quality have become a growing environmental problem. There is a need to continuously monitor the quality of the air. The lack of selectivity is one of the most important problems limiting the use of gas sensors for this purpose. In this study, the selectivity of six amperometric gas sensors is investigated. First, the sensors were calibrated in order to find a correlation between the concentration level and sensor output. Afterwards, the responses of each sensor to single or multicomponent gas mixtures with concentrations from 50 ppb to 1 ppm were measured. The sensors were studied under controlled conditions, a constant gas flow rate of 100 mL/min and 50 % relative humidity. Single Gas Sensor Response Interpretation, Multiple Linear Regression, and Artificial Neural Network algorithms were used to predict the concentrations of SO2 and NO2. The main goal was to study different interactions between sensors and gases in multicomponent gas mixtures and show that it is insufficient to calibrate sensors in only a single gas.

Abstract Background We aim to explore association between copy number alteration (CNA) and sensitivity to common tyrosine kinase inhibitors (TKIs) used in clear-cell renal cell carcinoma (ccRCC) treatment. Methods CNA with related sensitivity profiles were extracted from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset and was cross-referenced with common CNA in ccRCC in the Cancer Genome Atlas (TCGA) dataset. Functional annotation was profiled using GSEA and NET-GE. Target genes within cytobands of interest were screened in silico and validated in vitro using proliferation assays in A498 and 786-O ccRCC cells. Results Four TKIs (Sunitinib, Cabozantinib, Axitinib and Sorafenib) that were clinically used in ccRCC were selected. In silico analysis showed gain of 20q (+20q) occurred in ~ 23% of cases and was associated with resistance to all four TKIs; loss of 14q (−14q) occurred in ~ 39% of cases and was associated with resistance to Sunitinib and Sorafenib; loss of 18p (−18p) occurred in ~ 39% of cases and was associated with sensitivity to Sunitinib and Sorafenib. All 3 CNAs were associated with worsened prognosis, respectively. Candidate target genes included of RBL1 on 20q, KLHL33 on 14q and ARHGAP28 on18q. In vitro validation showed RBL1 overexpression induced resistance to Sunitinib and Cabozantinib; KLHL33 silencing induced resistance to Sunitinib; ARHGAP28 silencing induced sensitivity to Cabozantinib. Functional annotation indicated FoxO signaling, hypoxic response and Wnt pathway, and Rho-related cellular adhesion were mechanistically associated with +20q, −14q and −18p, respectively. Conclusion Common CNAs in ccRCC are associated with cancer-intrinsic cross-sensitivity to common TKIs. Further validation and functional analyses are therefore needed.

Instrumented wheelsets are widely utilized in the railway industry for the purpose of the measurement of the wheel-rail interaction force. In the conventional instrumented wheelset, the measurement accuracy of the lateral force is reduced due to the bending moment induced by the wheel load. The authors have proposed a new configuration of the instrumented wheelset to reduce the influence of the wheel load on the measurement of the lateral force. This proposed configuration utilizes the shear strains of the wheel web as a measure of the lateral force. This paper describes the accuracy verification of the proposed configuration under the wheel rotating condition. The single-wheelset roller-rig tests are carried out and the test results show that the proposed method can reduce the influence of the wheel load even under the rotating condition.

The admittance-type-level sensor is used for continuous level measurement in both metallic and nonmetallic tanks. However, the use of this sensor is limited because of the significant nonlinearity and cross-sensitivity. In this work, the authors have proposed the concept of a double-electrode admittance-type-level sensor. The said level sensor measures the level and the temperature of a conductive liquid. The multifunctional measurement is possible by the detection of the extent of cross-sensitivity using fuzzy logic and decomposition of the measurement variables. The experimental prototype of the sensor and associated instrumentation have been developed, and from the experimental results, it is found that the error in the measurement is consistently distributed and within the range of 1.86% for the level and 2.73% for temperature, which demonstrates the appropriateness of the design. This measurement system and sensor design can be implemented in the boiler-drum-level measurement for better drum-level control. [ABSTRACT FROM AUTHOR]

A novel technology for the simultaneous and independent measurement of dual parameters is proposed and experimented. By using a single fiber Bragg grating half-pasted by 1C-LV epoxy under different curing conditions, the sensor structure is designed such that the reflective single-peak spectrum splits into a twin-peak spectrum, which makes the FBG spectrum form a natural spectral peak splitting bias. A measurement limitation exists in the FBG sensor packaging at room temperature, which can be solved by the high-temperature cured packaging method. To verify the validity of the theory and methodology, the experimental system is used. In the range from -1000 to +1000 με and from 35 to 75°C, the Bragg wavelength change is relative linear to the strain and temperature. The temperature and strain variations can be independently and simultaneously measured using the split peak, and the deviations of the FBG sensor are ±1°C and ±5 με, respectively. This single FBG sensor can realize dual-parameter measurement, which is valuable for narrow-space health monitoring. [ABSTRACT FROM AUTHOR]

Abstract Background Real-time photoacoustic gas monitoring is used for personnel exposure and environmental monitoring, but its accuracy varies when organic solvents such as alcohol contaminate measurements. This is problematic for anesthetic gas measurements in hospitals, because most disinfectants contain alcohol, which could lead to false-high gas concentrations. We investigated the cross-sensitivities of the photoacoustic gas monitor Innova 1412 (AirTech Instruments, LumaSense, Denmark) against alcohols and alcoholic disinfectants while measuring sevoflurane, desflurane and isoflurane in a laboratory and in hospital during surgery. Methods 25 mL ethyl alcohol was distributed on a hotplate. An optical filter for isoflurane was used and the gas monitor measured the ‘isoflurane’ concentration for five minutes with the measuring probe fixed 30 cm above the hotplate. Then, 5 mL isoflurane was added vaporized via an Anesthetic Conserving Device (Sedana Medical, Uppsala, Sweden). After one-hour measurement, 25 mL isopropyl alcohol, N-propanol, and two alcoholic disinfectants were subsequently added, each in combination with 5 mL isoflurane. The same experiment was in turn performed for sevoflurane and desflurane. The practical impact of the cross-sensitivity was investigated on abdominal surgeons who were exposed intraoperatively to sevoflurane. A new approach to overcome the gas monitor’s cross-sensitivity is presented. Results Cross-sensitivity was observed for all alcohols and its strength characteristic for the tested agent. Simultaneous uses of anesthetic gases and alcohols increased the concentrations and the recovery times significantly, especially while sevoflurane was utilized. Intraoperative measurements revealed mean and maximum sevoflurane concentrations of 0.61 ± 0.26 ppm and 15.27 ± 14.62 ppm. We replaced the cross-sensitivity peaks with the 10th percentile baseline of the anesthetic gas concentration. This reduced mean and maximum concentrations significantly by 37% (p

Abstract Exploring and understanding the ocean is an important field of scientific study. Acquiring accurate and high-resolution temperature and depth profiles of the oceans over relatively short periods of time is an important basis for understanding ocean currents and other associated physical parameters. Traditional measuring instruments based on piezoelectric ceramics have a low spatial resolution and are not inherently waterproof. Meanwhile, sensing systems based on fiber Bragg grating (FBG) have the advantage of facilitating continuous measurements and allow multi-sensor distributed measurements. Therefore, in this paper, an all-fiber seawater temperature and depth-sensing array is used to obtain seawater temperature and depth profiles. In addition, by studying the encapsulation structure of the FBG sensors, this paper also solves the problem of the measurement error present in traditional FBG sensors when measuring seawater temperature. Through a theoretical analysis and seaborne test in the Yellow Sea of China, the sampling frequency of the all-fiber seawater temperature and depth profile measurement system is 1 Hz, the accuracy of the FBG sensors reaches 0.01 °C, and the accuracy of the FBG depth sensors reaches 0.1 % of the full scale. The resulting parameters for these sensors are therefore considered to be acceptable for most survey requirements in physical oceanography.

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

A key element in the safe operation of a modern gas distribution system is gas detection. The addition of hydrogen to natural gas will alter the characteristics of the fuel and, therefore, its impact on gas detection must be considered. It is important that gas detectors remain sufficiently sensitive to the presence of hydrogen and natural gas mixtures, and that they do not lead to false readings. This paper presents analyses of work performed as part of the Office for Gas and Energy Markets (OFGEM) funded HyDeploy project, on the response of various natural gas industry detectors to blended mixtures up to 20 vol percent (vol%) of hydrogen in natural gas. The scope of the detectors under test included survey instruments and personal monitors that are used in the gas industry. Four blend ratios were analysed (0, 10, 15 and 20 vol% hydrogen in natural gas). The laboratory testing undertaken investigated the following: • Flammable response to blends in the ppm range (0–0.2 vol%); • Flammable response to blends in the lower explosion limit range (0.2–5 vol%); • Flammable response to blends in the volume percent range (5–100 vol%); • Oxygen response to blends in the volume percent range (0–25 vol%); and. • Carbon monoxide response to blends in the ppm range (0–1000 ppm). Catalytic and thermal conductivity based flammable detectors in the volume percent range are, as expected, affected in the form of a relative increase in output. All of the carbon monoxide detectors tested were found to be cross-sensitive to hydrogen in the blended mixtures tested at levels which have the potential to cause false alarms at current permissible leakage rates. In summary, a number of measurement ranges across multiple detectors commonly used in the gas industry have been found to be cross-sensitive to hydrogen in natural gas blends to differing degrees. This required the HyDeploy project to select alternative instruments for the purpose of gas detection as part of the trial. This paper relates to the preparatory work to introduce hydrogen into the Keele closed network. • Current gas detector designs are not suitable for a H2/NG blended trial. • A maximum CO cross-sensitivity to H 2 of ≤1.5% is required for a trial detector. • There are no known hydrogen compensated domestic CO detectors on the market. • An IR instrument has been chosen with an LEL calibrated for worst-case blends. • The solutions proposed for Keele are designed to be safe and are conservative. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) have shown promising results for gas sensing due to their high surface area. Since humidity has a great impact on the electrical conductivity of resistive CNT gas sensors, we have investigated the change of humidity on their sensing properties. In this study, we fabricated vertically aligned CNT-based gas sensors for the detection of volatile organic compounds. The morphologies and phase structures of the fabricated samples were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FT-IR), confirming the presence of CNT with some surface impurities. It was found that a relative humidity increase from 10% to 80% can reduce the electrical conductivity of the sensor by around 4%. On the other hand, for a humidity above 80% the conductivity increased slightly. The fabricated device has been used as a gas sensor for volatile organic compounds, and the cross-sensitivity to humidity was investigated. It was found that in the fabricated sensors, a change in humidity up to 80% results in a 40% decrease in the response for the studied organic compounds. [ABSTRACT FROM AUTHOR]

In recent years, smog and poor air quality have become a growing environmental problem. There is a need to continuously monitor the quality of the air. The lack of selectivity is one of the most important problems limiting the use of gas sensors for this purpose. In this study, the selectivity of six amperometric gas sensors is investigated. First, the sensors were calibrated in order to find a correlation between the concentration level and sensor output. Afterwards, the responses of each sensor to single or multicomponent gas mixtures with concentrations from 50 ppb to 1 ppm were measured. The sensors were studied under controlled conditions, a constant gas flow rate of 100 mL/min and 50 % relative humidity. Single Gas Sensor Response Interpretation, Multiple Linear Regression, and Artificial Neural Network algorithms were used to predict the concentrations of SO2 and NO2. The main goal was to study different interactions between sensors and gases in multicomponent gas mixtures and show that it is insufficient to calibrate sensors in only a single gas. [ABSTRACT FROM AUTHOR]

The control of selective catalytic reduction (SCR) systems, via NH $_{\text{3}}$ injection, requires from a precise estimation of the SCR load in order to ensure NOx reduction by minimizing ammonia slip. This article aims to resolve the cross-sensitivity of current NOx sensors at the outlet of the SCR, by providing the control unit with an estimation of NOx and ammonia slip. The problem of discerning between NOx and ammonia slip is solved by identifying an intermediate variable representing the SCR load. The SCR load is estimated by combining the mass conservation principle between the inlet and the outlet of the SCR and a NOx reduction model, via an extended Kalman filter. Current models and observers have several limitations to represent the real behavior of the SCR along all the operating conditions; on one hand, when relying on the mass conservation, small errors at models are integrated, leading to important bias on the SCR load and on the other hand, the dynamics at the SCR must be precisely modelled for an adequate adaptation of the model. The main focus of the developed algorithm is to use a simplified model which might be used for ammonia slip estimation, being aware of current limitations of SCR models in real operation. Experimental results in a EURO 6 compression ignited (CI) engine show the potential of such observation in transient conditions and an adequate correlation with external ammonia measurements provided by additional sensors available on the test bench. [ABSTRACT FROM AUTHOR]

In this work, a gas sensor array has been designed and developed comprising of Pt, Cu and Ag decorated TiO2 and ZnO functionalized GaN nanowires using industry standard top-down fabrication approach. The receptor metal/metal-oxide combinations within the array have been determined from our prior molecular simulation results using first principle calculations based on density functional theory (DFT). The gas sensing data was collected for both singular and mixture of NO2, ethanol, SO2 and H2 in presence of H2O and O2 gases under UV light at room temperature. Each gas produced a unique response pattern across the sensors within the array by which precise identification of cross-sensitive gases is possible. After pre-processing of raw data, unsupervised principal component analysis (PCA) technique was applied on the array response. It is found that, each analyte gas forms a separate cluster in the score plot for all the target gases and their mixtures, indicating a clear discrimination among them. Then, four supervised machine learning algorithms such as- Decision Tree, Support Vector Machine (SVM), Naive Bayes (kernel) and k-Nearest Neighbor (k-NN) were trained and optimized using their significant parameters with our array dataset for the classification of gas type. Results indicate that the optimized SVM and NB classifier models exhibited 100% classification accuracy on test dataset. Practical applicability of the considered algorithms has been discussed as well. Moreover, this array device works at room-temperature using very low power and low-cost UV light-emitting diode (LED) as compared to high power consuming commercially available metal-oxide sensors. [ABSTRACT FROM AUTHOR]

We propose a general methodology to calculate the individual sensitivity and the cross-sensitivities of potentiometric sensor devices (e.g., ion sensitive FETs (ISFETs), CHEMFETs) with an arbitrary number of non-interacting receptors binding to ionic species or analytes in the electrolyte. The surface charge generated at the (bare or functionalized) interface with the electrolyte is described by the Poisson equation coupled to a linear system of equations for each type of receptor, where the unknowns are the fractions of sites binding with a given ion/analyte. Our general model encompasses in a unique framework a few simple special cases so far separately reported in the literature and provides for them closed-form expressions of the average site occupation probability. Detailed procedural description of the usage and benefits of the model is shown for specific cases with concurring surface chemical reactions. [ABSTRACT FROM AUTHOR]

Exploring and understanding the ocean is an important field of scientific study. Acquiring accurate and high-resolution temperature and depth profiles of the oceans over relatively short periods of time is an important basis for understanding ocean currents and other associated physical parameters. Traditional measuring instruments based on piezoelectric ceramics have a low spatial resolution and are not inherently waterproof. Meanwhile, sensing systems based on fiber Bragg grating (FBG) have the advantage of facilitating continuous measurements and allow multi-sensor distributed measurements. Therefore, in this paper, an all-fiber seawater temperature and depth-sensing array is used to obtain seawater temperature and depth profiles. In addition, by studying the encapsulation structure of the FBG sensors, this paper also solves the problem of the measurement error present in traditional FBG sensors when measuring seawater temperature. Through a theoretical analysis and seaborne test in the Yellow Sea of China, the sampling frequency of the all-fiber seawater temperature and depth profile measurement system is 1 Hz, the accuracy of the FBG sensors reaches 0.01 °C, and the accuracy of the FBG depth sensors reaches 0.1 % of the full scale. The resulting parameters for these sensors are therefore considered to be acceptable for most survey requirements in physical oceanography. [ABSTRACT FROM AUTHOR]

Cross-sensitivity among chemical gas sensors leads to inaccurate identification of mixed gas. Pattern recognition algorithms are usually applied to improve the recognition accuracy. However, the abilities of current methods to process sequence property of response data are not strong enough. Especially, they cannot deal with bidirectional cross-sensitive issue, leading to recognition errors. Bidirectional Recurrent Neural Network (BRNN) could well learn bidirectional association features between word sequences in natural language processing field, which is similar to the bidirectional interaction of cross-sensitivity. In this study, an improved deep BRNN model was constructed to solve the cross-sensitivity problem of chemical gas sensor array. A chemical gas sensor array with four units was fabricated and response data was thoroughly obtained. Data preprocessing methods, model structure hyperparameters and optimizers were studied. Finally, an improved deep BRNN model was developed with 3 layers and 100 hidden_size, training with Adamax optimizer. A recognition accuracy of 98.93% was achieved, attributing to the model's excellent learning ability to the bidirectional cross-sensitivity rules among gas sensors. This improved BRNN model provided a novel idea to eliminate cross-sensitivity, exhibiting good potential for recognizing mixed gas analyte accurately. • A 3-layer deep BRNN with 100 hidden_size eliminated cross-sensitivity of gas sensor array. • Embedding layer and decimal places of data reduced helped to provide effective input in time. • Bidirectional self-learning ability to process sequence data helped to achieve good recognition accuracy. [ABSTRACT FROM AUTHOR]

Simultaneous sensing of multiple gases by a single fluorescent-based gas sensor is of utmost importance for practical applications. Such sensing is strongly hindered by cross-sensitivity effects. In this study, we propose a novel analysis method to ameliorate such hindrance. The trial sensor used here was fabricated by coating platinum(II) meso-tetrakis(pentafluorophenyl)porphyrin (PtTFPP) and eosin-Y dye molecules on both sides of a filter paper for sensing O2 and NH3 gases simultaneously. The fluorescent peak intensities of the dyes can be quenched by the analytes and this phenomenon is used to identify the gas concentrations. Ideally, each dye is only sensitive to one gas species. However, the fluorescent peak related to O2 sensing is also quenched by NH3 and vice versa. Such cross-sensitivity strongly hinders gas concentration detection. Therefore, we have studied this cross-sensitivity effect systematically and thus proposed a new analysis method for accurate estimation of gas concentration. Comparing with a traditional method (neglecting cross-sensitivity), this analysis improves O2-detection error from −11.4% ± 34.3% to 2.0% ± 10.2% in a mixed background of NH3 and N2.

Abstract We fabricated a simple, compact, and stable temperature sensor based on an S-shaped dislocated optical fiber. The dislocation optical fiber has two splice points, and we obtained the optimal parameters based on the theory and our experiment, such as the dislocation amount and length of the dislocation optical fiber. According to the relationship between the temperature and the peak wavelength shift, the temperature of the environment can be obtained. Then, we made this fiber a micro bending as S-shape between the two dislocation points, and the S-shaped micro bending part could release stress with the change in temperature and reduce the effect of stress on the temperature measurement. This structure could solve the problem of sensor distortion caused by the cross response of temperature and stress. We measured the S-shaped dislocation fiber sensor and the dislocation fiber without S-shape under the same environment and conditions, and the S-shaped dislocation fiber had the advantages of the stable reliability and good linearity.

Pneumonia is a lower respiratory tract infection caused by the inability to clear pathogens from the lower airway and alveoli. Cytokines and local inflammatory markers are released, causing further damage to the lungs through the accumulation of white blood cells and fluid congestion, leading to pus in the parenchyma. The Infectious Diseases Society of America defines pneumonia as the presence of new lung infiltrate with other clinical evidence supporting infection, including new fever, purulent sputum, leukocytosis, and decline in oxygenation. Importantly, lower respiratory infections remain the most deadly communicable disease. Pneumonia is subdivided into three categories: (1) community acquired, (2) hospital acquired, and (3) ventilator associated. Therapy for each differs based on the severity of the disease and the presence of risk factors for methicillin-resistant Staphylococcus aureus or Pseudomonas aeruginosa., (Published by Elsevier Inc.)

This article provides an overview of the pathogenesis and risk factors associated with antiepileptic drug (AED) hypersensitivity reactions, provides prescribing guidelines that may minimize the risk of antiepileptic induced rashes, and discusses treatment options for rashes. Articles indexed in PubMed, Science Citation, and Google Scholar (January 1946-March 2019) were systematic searched using the following key terms: hypersensitivity, rash, antiepileptic, epilepsy, cross-sensitivity, desensitization, patch testing and supplemented with our clinical experiences. Additional references were identified from a review of literature citations. AEDs are associated with cutaneous adverse reactions. Aromatic AEDs and higher titration rates are associated with increased risk of hypersensitivity reaction. Patient characteristics, underlying health conditions, and genetic variations may increase the likelihood of a hypersensitivity reaction. Once a hypersensitivity reaction occurs, the likelihood of cross sensitivity to another AED increases, especially among other aromatic AEDs. Withdrawal of the causal agent and initiation of a lower risk agent usually leads to resolution of symptoms. Desensitization protocols may be an option for patients whose seizures only respond to the AED causing the rash. [ABSTRACT FROM AUTHOR]

We extend the method of half-blind calibration (HBC) to sensor systems with a nonlinear response. HBC ensures the efficient compensation of undesired cross-sensitivities in sensor systems despite a reduced calibration effort. During HBC, one applies load combinations covering the ranges of the measurands of primary interest of the system and the undesired disturbances. However, only the measurand values and the output signals of the individual sensors of the system are recorded. In the proposed nonlinear HBC, these data are fitted using multiple polynomial regression, treating the sensor data as predictor variables and each measurand of interest as the dependent variable. For each such measurand, the extracted polynomial function of the sensor signals predicts cross-sensitivity-compensated values. The method is demonstrated using a silicon-based magnetic sensor system, where stress and temperature constitute disturbances. During calibration, controlled values of the out-of-plane component of the magnetic induction are applied, while stress and temperature are varied without accurate knowledge of their values. Output signals of the Hall sensor and of co-integrated stress and temperature sensors provide the sensor signals. All respond nonlinearly to stress and temperature. Using a polynomial regression of degree 4, the system achieves an accuracy of the measured magnetic induction of $136~\mu \text{T}$ in the ranges of ±21 mT, −40 °C to 125 °C, and up to −80 MPa of in-plane stress. This performance was achieved without quantitative knowledge of the disturbances during calibration. Nonlinear HBC has the potential to considerably simplify calibration equipment and procedures. [ABSTRACT FROM AUTHOR]

Highlights • A novel Photonic Crystal Fiber (PCF) structure with dual Surface Plasmon Resonance (SPR) was proposed. • Temperature and strain can be simultaneously measured. • The sensor has high sensitivity, simple and robust structure. Abstract A novel Photonic Crystal Fiber (PCF) structure with dual Surface Plasmon Resonance (SPR) was proposed to implement simultaneous measurement of temperature and strain and resolve the cross-sensitivity problem between temperature and strain. Two symmetrical holes of the PCF was coated with golden layer and one of which was infiltrated with temperature-sensitive liquid. Through theory and numerical analyses, the influences of temperature and strain on structural parameters of the PCF were discussed and calculated. Simulation results demonstrated that the temperature and strain could be simultaneously measured with sensitivities of −6.83 nm/°C and 1.30 × 10−3 nm/με, respectively, when diethylene glycol was chosen as the temperature sensitive liquid. In particular, the influence of strain variation on the temperature sensing characters was analyzed and compensated, which could further guarantee the measurement precision of temperature. [ABSTRACT FROM AUTHOR]

Highlights • A multifunctional sensor based on GFET is studied with a decoupling technique. • A decoupling technique is designed to precisely identify various environment signals. • The decoupling technique can identify two environment values from outputs of the single GFET. Abstract Graphene is one of good candidates for application to multifunctional sensors due to its unique material properties, extremely high surface-to-volume ratio and ability to be effectively co-integrated on various substrates. However, the electrical properties of graphene are sensitive to most typical environment signals such as temperature, light, humidity, and gas. Therefore, to develop graphene-based multifunctional sensors, it is essential to identify multiple environment information values from the output characteristics of a single graphene sensor. In this study, we developed a temperature–illuminance multifunctional graphene sensor that can identify both temperature and illuminance values. We also propose a decoupling technique to enable the precise identification of temperature and illuminance values from the output characteristics of a sensor that has cross-sensitivity to the both inputs. This decoupling technique is developed based on the different gate-voltage dependence of the temperature- and illumination-induced modulations of the graphene conductivity. The results show that graphene can be used as a material for multifunctional sensors when combined with an appropriate decoupling technique for identifying various types of environment signals. [ABSTRACT FROM AUTHOR]