Highly Sensitive NH3 Wireless Sensor Based on Ag-RGO Composite Operated at Room-temperature

Wide-range and high-accuracy wireless sensor with self-humidity compensation for real-time ammonia monitoring

Real-time and accurate biomarker detection is highly desired in point-of-care diagnosis, food freshness monitoring, and hazardous leakage warning. However, achieving such an objective with existing technologies is still challenging. Herein, we demonstrate a wireless inductor-capacitor (LC) chemical sensor based on platinum-doped partially deprotonated-polypyrrole (Pt-PPy+ and PPy0) for real-time and accurate ammonia (NH3) detection. With the chemically wide-range tunability of PPy in conductivity to modulate the impedance, the LC sensor exhibits an up-to-180% improvement in return loss (S11). The Pt-PPy+ and PPy0 shows the p-type semiconductor nature with greatly-manifested adsorption-charge transfer dynamics toward NH3, leading to an unprecedented NH3 sensing range. The S11 and frequency of the Pt-PPy+ and PPy0-based sensor exhibit discriminative response behaviors to humidity and NH3, enabling the without-external-calibration compensation and accurate NH3 detection. A portable system combining the proposed wireless chemical sensor and a handheld instrument is validated, which aids in rationalizing strategies for individuals toward various scenarios.

ZnO Decorated Graphene-Based NFC Tag for Personal NO2 Exposure Monitoring during a Workday

This paper presents the integration of a sensing layer over interdigitated electrodes and an electronic circuit on the same flexible printed circuit board. This integration provides an effective technique to use this design as a wearable gas measuring system in a target application, exhibiting high performance, low power consumption, and being lightweight for on-site monitoring. The wearable system proves the concept of using an NFC tag combined with a chemoresistive gas sensor as a cumulative gas sensor, having the possibility of holding the data for a working day, and completely capturing the exposure of a person to NO2 concentrations. Three different types of sensors were tested, depositing the sensing layers on gold electrodes over Kapton substrate: bare graphene, graphene decorated with 5 wt.% zinc oxide nanoflowers, or nanopillars. The deposited layers were characterized using FESEM, EDX, XRD, and Raman spectroscopy to determine their crystalline structure, morphological and chemical compositions. The gas sensing performance of the sensors was analyzed against NO2 (dry and humid conditions) and other interfering species (dry conditions) to check their sensitivity and selectivity. The resultant-built wearable NFC tag system accumulates the data in a non-volatile memory every minute and has an average low power consumption of 24.9 µW in dynamic operation. Also, it can be easily attached to a work vest.

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Breath VOC analysis and machine learning approaches for disease screening: a review

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

RFID-based sensing in smart packaging for food applications: A review

The global pandemic COVID-19 has led to an increase in the number of people purchasing food online, which has brought to a higher demand on the food supply chain. Such as the need to collect more information related to food safety and quality in real-time. Strengthening management of food logistics information flow can reduce food loss and waste and bring better quality and safety of food to consumers. In this review, the importance and applicability of RFID (Radio Frequency Identification) technology to smart food packaging are described. This study emphasizes the recent advancement of the RFID tags in humidity, temperature, gas, pH, integrity, and traceability sensor applications in connection with food packaging. RFID sensors are more suitable for smart packaging both in terms of sensing ability and data transmission. A simpler, low-cost, more robust and less power-demanding sensors network is the development direction of smart packaging in the future. Chipless RFID sensors have the potential to achieve these functions. But it still faces many challenges to be overcome. For example, biocompatible, cost, reading range, multi-tag collision, multi-parameter sensors, recycling issues, security and privacy of RFID system should be solved.

A Novel Activated Biochar-Based Immunosensor for Rapid Detection of E. coli O157:H7

E. coli O157:H7, one of the major foodborne pathogens, can cause a significant threat to the safety of foods. The aim of this research is to develop an activated biochar-based immunosensor that can rapidly detect E. coli O157:H7 cells without incubation in pure culture. Biochar was developed from corn stalks using proprietary reactors and then activated using steam-activation treatment. The developed activated biochar presented an enhanced surface area of 830.78 m²/g. To develop the biosensor, the gold electrode of the sensor was first coated with activated biochar and then functionalized with streptavidin as a linker and further immobilized with biotin-labeled anti-E. coli polyclonal antibodies (pAbs). The optimum concentration of activated biochar for sensor development was determined to be 20 mg/mL. Binding of anti-E. coli pAbs with E. coli O157:H7 resulted in a significant increase in impedance amplitude from 3.5 to 8.5 kΩ when compared to an only activated biochar-coated electrode. The developed immunosensor was able to detect E. coli O157:H7 cells with a limit of detection of 4 log CFU/mL without incubation. Successful binding of E. coli O157:H7 onto an activated biochar-based immunosensor was observed on the microelectrode surface in scanning electron microscopy (SEM) images.

NH3 Sensor Based on rGO-PANI Composite with Improved Sensitivity

This work reports on a reduced graphene oxide and poly(aniline) composite (rGO-PANI), with rGO clusters inserted between PANI chains. These clusters were formed due the plasticizing effect of N-methyl-2-pyrrolidone (NMP) solvent, which was added during the synthesis. Further, this composite was processed as thin film onto an interdigitated electrode array and used as the sensitive layer for ammonia gas, presenting sensitivity of 250% at 100 ppm, a response time of 97 s, and a lowest detection limit of 5 ppm. The PANI deprotonation process, upon exposure to NH3, rGO, also contributed by improving the sensitivity due its higher surface area and the presence of carboxylic acids. This allowed for the interaction between the hydrogen of NH3 (nucleophilic character) and the -COOH groups (electrophilic character) from the rGO surface, thereby introducing a promising sensing composite for amine-based gases.

Ammonia Gas Sensing Characteristic of P3HT-rGO-MWCNT Composite Films

In this work, the P3HT:rGO:MWCNTs (PGC) nanocomposite film applied to the ammonia gas sensor was successfully fabricated by a drop-casting technique. The results demonstrated that the optimum weight ratio of the PGC nanocomposite gas sensor is 20%:60%:20% as the weight ratio of P3HT:rGO:MWCNTs (called PGC-60). This weight ratio leads to the formation of nanostructured composites, causing the efficient adsorption/desorption of ammonia gas in/out of the film surface. The sensor based on PGC-60 possessed a response time of 30 s, sensitivity up to 3.6% at ammonia gas concentration of 10 ppm, and relative sensitivity of 0.031%/ppm. These results could be attributed to excellent electron transportation of rGO, the main adsorption activator to NH3 gas of P3HT, and holes move from P3HT to the cathodes, which works as charge “nano-bridges” carriers of Multi-Walled Carbon Nanotubes (MWCNTs). In general, these three components of PGC sensors have significantly contributed to the improvement of both the sensitivity and response time in the NH3 gas sensor.

Wireless Passive LC Temperature and Strain Dual-Parameter Sensor

There is an increasing demand for bearing temperature and strain monitoring in high-speed rotating systems. This study proposes a new multiresonance, multiplexing, wireless, passive inductance capacitance (LC) temperature and strain sensor. The sensor has two capacitors connected at different locations (turns) on the same inductor to achieve simultaneous temperature and strain measurements. The plate capacitor is connected to the inner part of the inductor and the other interdigital capacitor is connected to the outer part of the inductor to form two LC loops. The structure of the sensor is optimized through High Frequency Structure Simulator (HFSS) simulations to realize frequency separation of the two parameters and avoid mutual interference between the two signals. The sensor is fabricated on a polyimide film using electroplating technology. The experimental results show that the temperature–strain sensor can operate stably from 25 °C to 85 °C with an average sensitivity of 27.3 kHz/°C within this temperature range. The sensor can detect strains in the range of 1000–5000 με with a strain sensitivity of 100 Hz/με at 25 °C. Therefore, the proposed wireless passive LC temperature-strain sensor exhibits stable performance. In addition, the use of a single inductor effectively reduces the sensor’s area. The flexible substrate provides advantageous surface conformal attachment characteristics suitable for monitoring high-temperature rotating parts in adverse environments.

Highly Deformable Fabric Gas Sensors Integrating Multidimensional Functional Nanostructures

Highly strain-endurable gas sensors were implemented on fabric, which was taken from a real T-shirt, employing a sequential coating method. Multidimensional, functional nanostructures such as reduced graphene oxide, ZnO nanorods, palladium nanoparticles, and silver nanowires were integrated for their realization. It was revealed that the fabric gas sensors could detect both oxidizing and reducing gases at room temperature with differing signs and magnitudes of responses. Noticeably, the fabric gas sensors could normally work even under large strains up to 100%, which represents the highest strain tolerance in the gas sensor field. Furthermore, the fabric gas sensors turned out to bear harsh bending and twisting stresses. It was also demonstrated that the sequential coating method is an effective and facile way to control the size of the fabric gas sensor.

Printed gas sensors

This review presents the recent development of printed gas sensors based on functional inks.