An official website of the United States government
Abstract Soil sensors and plant wearables play a critical role in smart and precision agriculture via monitoring real‐time physical and chemical signals in the soil, such as temperature, moisture, pH, and pollutants and providing key information to optimize crop growth circumstances, fight against biotic and abiotic stresses, and enhance crop yields. Herein, the recent advances of the important soil sensors in agricultural applications, including temperature sensors, moisture sensors, organic matter compounds sensors, pH sensors, insect/pest sensors, and soil pollutant sensors are reviewed. Major sensing technologies, designs, performance, and pros and cons of each sensor category are highlighted. Emerging technologies such as plant wearables and wireless sensor networks are also discussed in terms of their applications in precision agriculture. The research directions and challenges of soil sensors and intelligent agriculture are finally presented.
more »
« less
Road Map of Semiconductor Metal-Oxide-Based Sensors: A Review
Identifying disease biomarkers and detecting hazardous, explosive, flammable, and polluting gases and chemicals with extremely sensitive and selective sensor devices remains a challenging and time-consuming research challenge. Due to their exceptional characteristics, semiconducting metal oxides (SMOxs) have received a lot of attention in terms of the development of various types of sensors in recent years. The key performance indicators of SMOx-based sensors are their sensitivity, selectivity, recovery time, and steady response over time. SMOx-based sensors are discussed in this review based on their different properties. Surface properties of the functional material, such as its (nano)structure, morphology, and crystallinity, greatly influence sensor performance. A few examples of the complicated and poorly understood processes involved in SMOx sensing systems are adsorption and chemisorption, charge transfers, and oxygen migration. The future prospects of SMOx-based gas sensors, chemical sensors, and biological sensors are also discussed.
more »
« less
- Award ID(s):
- 2138701
- PAR ID:
- 10506466
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 23
- Issue:
- 15
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 6849
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by other groups on the advances in GaN nanostructures-based sensors for detection of gases such as hydrogen (H2), alcohols (R-OH), methane (CH4), benzene and its derivatives, nitric oxide (NO), nitrogen dioxide (NO2), sulfur-dioxide (SO2), ammonia (NH3), hydrogen sulfide (H2S) and carbon dioxide (CO2). The important sensing performance parameters like limit of detection, response/recovery time and operating temperature for different type of sensors have been summarized and tabulated to provide a thorough performance comparison. A novel metric, the product of response time and limit of detection, has been established, to quantify and compare the overall sensing performance of GaN nanostructure-based devices reported so far. According to this metric, it was found that the InGaN/GaN NW-based sensor exhibits superior overall sensing performance for H2 gas sensing, whereas the GaN/(TiO2–Pt) nanowire-nanoclusters (NWNCs)-based sensor is better for ethanol sensing. The GaN/TiO2 NWNC-based sensor is also well suited for TNT sensing. This paper has also reviewed density-functional theory (DFT)-based first principle studies on the interaction between gas molecules and GaN. The implementation of machine learning algorithms on GaN nanostructured sensors and sensor array has been analyzed as well. Finally, gas sensing mechanism on GaN nanostructure-based sensors at room temperature has been discussed.more » « less
-
Abstract In the modern industrial setting, there is an increasing demand for all types of sensors. The demand for both the quantity and quality of sensors is increasing annually. Our research focuses on thin-film nitrate sensors in particular, and it seeks to provide a robust method to monitor the quality of the sensors while reducing the cost of production. We are researching an image-based machine learning method to allow for real-time quality assessment of every sensor in the manufacturing pipeline. It opens up the possibility of real-time production parameter adjustments to enhance sensor performance. This technology has the potential to significantly reduce the cost of quality control and improve sensor quality at the same time. Previous research has proven that the texture of the topical layer (ion-selective membrane (ISM) layer) of the sensor directly correlates with the performance of the sensor. Our method seeks to use the correlation so established to train a learning-based system to predict the performance of any given sensor from a still photo of the sensor active region, i.e. the ISM. This will allow for the real-time assessment of every sensor instead of sample testing. Random sample testing is both costly in time and labor, and therefore, it does not account for all of the individual sensors. Sensor measurement is a crucial portion of the data collection process. To measure the performance of the sensors, the sensors are taken to a specialized lab to be measured for performance. During the measurement process, noise and error are unavoidable; therefore, we generated credibility data based on the performance data to show the reliability of each sensor performance signal at each sample time. In this paper, we propose a machine learning based method to predict sensor performance using image features extracted from the non-contact sensor images guided by the credibility data. This will eliminate the need to test every sensor as it is manufactured, which is not practical in a high-speed roll-to-roll setting, thus truely enabling a certify as built framework.more » « less
-
Abstract 2D materials have exceptional physical and chemical characteristics, which makes them attractive for wearable technology. These characteristics include high carrier mobility, outstanding mechanical performance, abundant chemistry, and excellent electrostatic tunability. However, due to the high electron doping effect of interfacial charge impurities and intrinsic defects, most reported 2D materials are n‐type. Complementary electronic devices and high‐performance wearable sensors necessitate the development ofp‐type 2D semiconductors, which have superior electrocatalytic performance in oxidative processes compared to their n‐type counterparts. This review paper thoroughly accounts for recent advancements in 2Dp‐type semiconductor‐based wearable sensors, covering basic understandings, synthesis and fabrication, functional devices, and sensor performance insights. Finally, challenges and future opportunities for 2Dp‐type semiconductor‐based wearable sensors are discussed.more » « less
-
This article presents a study of two types of on-chip FPGA voltage sensors based on ring oscillators (ROs) and time-to-digital converter (TDCs), respectively. It has previously been shown that these sensors are often used to extract side-channel information from FPGAs without physical access. The performance of the sensors is evaluated in the presence of circuits that deliberately waste power, resulting in localized voltage drops. The effects of FPGA power supply features and sensor sensitivity in detecting voltage drops in an FPGA power distribution network (PDN) are evaluated for Xilinx Artix-7, Zynq 7000, and Zynq UltraScale+ FPGAs. We show that both sensor types are able to detect supply voltage drops, and that their measurements are consistent with each other. Our findings show that TDC-based sensors are more sensitive and can detect voltage drops that are shorter in duration, while RO sensors are easier to implement because calibration is not required. Furthermore, we present a new time-interleaved TDC design that sweeps the sensor phase. The new sensor generates data that can reconstruct voltage transients on the order of tens of picoseconds.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s23156849
