| 1. |
- Nguyet, To Thi, et al.
(författare)
-
Enhanced response characteristics of NO2 gas sensor based on ultrathin SnS2 nanoplates : Experimental and DFT study
- 2024
-
Ingår i: Sensors and Actuators A-Physical. - : Elsevier. - 0924-4247 .- 1873-3069. ; 373
-
Tidskriftsartikel (refereegranskat)abstract
- Layered-metal dichalcogenides with extraordinary characteristics of vast surface area, tunable bandgap and superior adsorption capability enable the potential for application in gas sensors. However, the synthesis of effective material for enhanced response performance remains a challenge. Herein, we exploited a fascinating sensitivity and selectivity towards NO2 gas detection using SnS2 nanoflakes prepared via the hydrothermal method. SnS2 nanoflakes with a thickness of 25 nm and an average diameter of approximately 500 nm show the potential for the detection of NO2 gas at low concentrations of ppb levels. The sensing properties of the SnS2 sensors were investigated for different concentrations of NO2 at various operating temperatures. The sensor exhibits the highest gas-sensing response of 161 at 250 οC upon exposure to 5 ppm of NO2 gas with fast response and recovery times. In addition, the sensor shows excellent selectivity with a low detection limit of ppb level. The electronic structure and gas-sensing mechanism are elucidated via finding density of states, charge density, and band structure based on DFT study which is calculated by the Vienna ab-initio simulation package (VASP). The considerable small adsorption energy reveals a physisorption of the NO2 molecules on the SnS2 surface (-0.174 eV), indicating the SnS2 nanoflakes are intriguing candidates for the speedy detection of NO2 gas.
|
|
| 2. |
- Duy, Nguyen Van, et al.
(författare)
-
Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification
- 2022
-
Ingår i: Sensors and actuators. B, Chemical. - : Elsevier. - 0925-4005 .- 1873-3077. ; 351
-
Tidskriftsartikel (refereegranskat)abstract
- Classification of different gases is important, and it is possible to use different gas sensors for this purpose. Electronic noses, for example, combine separated gas sensors into an array for detecting different gases. However, the use of separated sensors in an array suffers from being bulky, high-energy consumption and complex fabrication processes. Generally, gas sensing properties, including gas selectivity, of semiconductor gas sensors are strongly dependent on their working temperature. It is therefore feasible to use a single device composed of identical sensors arranged in a temperature gradient for classification of multiple gases. Herein, we introduce a design for simple fabrication of gas sensor array based on bilayer Pt/SnO2 for real-time monitoring and classification of multiple gases. The study includes design simulation of the sensor array to find an effective gradient temperature, fabrication of the sensors and test of their performance. The array, composed of five sensors, was fabricated on a glass substrate without the need of backside etching to reduce heat loss. A SnO2 thin film sensitized with Pt on top deposited by sputtering was used as sensing material. The sensor array was tested against different gases including ethanol, methanol, isopropanol, acetone, ammonia, and hydrogen. Radar plots and principal component analysis were used to visualize the distinction of the tested gases and to enable effective classification.
|
|
| 3. |
- Nguyen, Xuan Thai, et al.
(författare)
-
Gas sensor array based on tin oxide nano structure for volatile organic compounds detection
- 2020
-
Ingår i: Vietnam Journal of Science and Technology. - : Vietnam Academy of Science and Technology. - 2525-2518. ; 58:2, s. 189-196
-
Tidskriftsartikel (refereegranskat)abstract
- The detection of volatile organic compounds (VOCs) is essential in practicalapplication in breath analysis. Thus, gas sensors based on metal oxide have been fabricated, butthey lacked selectivity. One approach to resolve this task is to use an array of highly sensitiveand selective sensors as an electronic nose. Here a gas sensor array based on Tin oxide nanostructurewith temperature modulation techniques was presented. A Platinum micro-heater isaccompanied with the array gas sensor. The gas sensor array was composed of five singlesensors, and that single sensor is located at different site from the micro heater and works atdifferent temperatures. The gas sensing properties of the gas array sensors were investigatedwith VOC gases such as Ethanol, Methanol, Iso-propanol, and Acetone as well as NH3, H2, andH2S. We also confirm the good selectivity of the array sensor for Ethanol, Methanol, Isopropanol,Acetone, NH3, H2, and H2S by using radar graphic method.
|
|
| 4. |
- Son, Dang Ngoc, et al.
(författare)
-
A novel design and fabrication of self-heated In2O3 nanowire gas sensor on for ethanol detection
- 2022
-
Ingår i: Sensors and Actuators A-Physical. - : Elsevier. - 0924-4247 .- 1873-3069. ; 345
-
Tidskriftsartikel (refereegranskat)abstract
- Many attempts have been made on the design and fabrication of low-power consumption gas sensor for application on the Internet of Things and portable devices. The performance of gas sensors includes sensitivity, selectivity, and power consumption, which are strongly dependent on the configuration of the device such as the gap size between two electrodes, the sensing material, and operation principle. Here, self-heated In2O3 nanowire-based gas sensors were designed and fabricated by on-chip growth technique via thermal evaporation to work at room temperature. The effect of electrode gap (10-40 mu m) on the power consumption and gas sensing performance of the In2O3 nanowire sensors was studied. With the large gap of 40 mu m, the sensor exhibited excellent sensing characteristics of low power consumption (1.06 mW) with ability to detect ethanol gas down to 20 ppm effectively. We also examined the role of nanowire conductivity in the performance of the self-heated sensor in the detection of reducing gas. The sensor demonstrated rapid response and recovery times of less than a minute, exceptional stability, and remarkable recovery.
|
|
| 5. |
- Thai, Nguyen Xuan, et al.
(författare)
-
Prototype edge-grown nanowire sensor array for the real-time monitoring and classification of multiple gases
- 2020
-
Ingår i: JOURNAL OF SCIENCE-ADVANCED MATERIALS AND DEVICES. - : VIETNAM NATL UNIV. - 2468-2284 .- 2468-2179. ; 5:3, s. 409-416
-
Tidskriftsartikel (refereegranskat)abstract
- The monitoring and classification of different gases using a single resistive semiconductor sensor are challenging because of the similar response characteristics. An array of separated sensors can be used as an electronic nose, but such arrays have a bulky structure and complex fabrication processes. Herein, we easily fabricated a gas-sensor array based on edge-grown SnO2 nanowires for the real-time monitoring and classification of multiple gases. The array comprised four sensors and was designed on a glass substrate. SnO2 nanowires were grown on-chip from the edge of electrodes, made contact together, and acted as sensing elements. This method was advantageous over the post-synthesis technique because the SnO2 nanowires were directly grown from the edge of the electrodes rather than on the surface. Accordingly, damage to the electrode was avoided by alloying Sn with Pt at a high growth temperature. The sensing characteristics of the sensor array were further examined for different gases, including methanol, isopropanol, ethanol, ammonia, hydrogen sulphide and hydrogen. Radar plots were used to improve the selective detection of different gases and enable effective classification. (C) 2020 The Authors. Publishing services by Elsevier B.V. on behalf of Vietnam National University, Hanoi.
|
|
| 6. |
- Thai, Nguyen Xuan, et al.
(författare)
-
Realization of a portable H2S sensing instrument based on SnO2 nanowires
- 2020
-
Ingår i: Journal of Science: Advanced Materials and Devices. - : Elsevier BV. - 2468-2284 .- 2468-2179. ; 5:1, s. 40-47
-
Tidskriftsartikel (refereegranskat)abstract
- Monitoring of toxic gas in air is important because air pollution, especially in developing countries, has rapidly become severe. The high cost of installation and maintenance of a stationary analysis system by using methods such as gas chromatography limits its applications. Low-power, portable devices with relatively low-cost gas sensors are effective for mapping pollution levels in real-time in urban areas and in other living environmentts. Herein, the realization of a portable H2S sensing instrument based on SnO2 nanowires is reported. The sensor chip was prepared by the on-chip growth of SnO2 nanowires directly from the edges of Pt electrodes. The electronic system and software for signal acquisition, data processing, data storage, and output of the instrument were developed. A prototype for zero series of the instrument was also realized. The instrument is capable of monitoring H2S gas in air at ppm level and in biogas production with satisfation.
|
|
| 7. |
- Duoc, Vo Thanh, et al.
(författare)
-
Room temperature highly toxic NO2 gas sensors based on rootstock/scion nanowires of SnO2/ZnO, ZnO/SnO2, SnO2/SnO2 and, ZnO/ZnO
- 2021
-
Ingår i: Sensors and actuators. B, Chemical. - : Elsevier. - 0925-4005 .- 1873-3077. ; 348
-
Tidskriftsartikel (refereegranskat)abstract
- Grafted structures between SnO2 and ZnO nanowires were realized in a two-step process of growth. First, the rootstocks of SnO2 or ZnO nanowires were synthesized by thermal evaporation technique. Second, a thin Au layer was sputter deposited on the sample and synthesis of nanowire scions of ZnO or SnO2, respectively, on the rootstocks was realized by thermal evaporation technique again. In both growth steps, SnO2 powder or a mixture of ZnO and carbon powders was use as source materials for the synthesis. Different rootstock/scion combinations of SnO2/ZnO, ZnO/SnO2 nanowires (called heterostructures) and ZnO/ZnO, SnO2/SnO2 nanowires (called homostructures) were synthesised. The fabricated grafted nanowires were examined by field-emission scanning electron microscope and their compositions were analyzed by energy dispersive spectroscopy and X-ray diffraction analysis. The test results indicate that this type of nanostructure material is very promising for NO2 gas sensing at ppt level at room temperature. Among the fabricated structures the SnO2/ZnO nanowires showed the best sensing performance with the high sensitivity and fast response and recovery time. We also discussed the gas sensing mechanism of the fabricated sensors based on the band diagram.
|
|
| 8. |
- Thai, Nguyen Xuan, et al.
(författare)
-
Multi gas sensors using one nanomaterial, temperature gradient, and machine learning algorithms for discrimination of gases and their concentration
- 2020
-
Ingår i: Analytica Chimica Acta. - : ELSEVIER. - 0003-2670 .- 1873-4324. ; 1124, s. 85-93
-
Tidskriftsartikel (refereegranskat)abstract
- In this work, four identical micro sensors on the same chip with noble metal decorated tin oxide nanowires as gas sensing material were located at different distances from an integrated heater to work at different temperatures. Their responses are combined in highly informative 4D points that can qualitatively (gas recognition) and quantitatively (concentration estimate) discriminate all the tested gases. Two identical chips were fabricated with tin oxide (SnO2) nanowires decorated with different metal nanoparticles: one decorated with Ag nanoparticles and one with Pt nanoparticles. Support Vector Machine was used as the "brain" of the sensing system. The results show that the systems using these multisensor chips were capable of achieving perfect classification (100%) and good estimation of the concentration of tested gases (errors in the range 8-28%). The Ag decorated sensors did not have a preferential gas, while Pt decorated sensors showed a lower error towards acetone, hydrogen and ammonia. Combination of the two sensor chips improved the overall estimation of gas concentrations, but the individual sensor chips were better for some specific target gases. (C) 2020 Elsevier B.V. All rights reserved.
|
|
| 9. |
- Thanh Hoan, Nguyen, et al.
(författare)
-
Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam
- 2022
-
Ingår i: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 131:3, s. 1431-1449
-
Tidskriftsartikel (refereegranskat)abstract
- Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.
|
|
| 10. |
- Tonezzer, Matteo, et al.
(författare)
-
Miniaturized multisensor system with a thermal gradient : Performance beyond the calibration range
- 2023
-
Ingår i: Journal of Science: Advanced Materials and Devices. - : Elsevier BV. - 2468-2179. ; 8:3
-
Tidskriftsartikel (refereegranskat)abstract
- Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensingmaterial (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as anano-electronic nose to distinguish five different gases and estimate their concentrations. This innovativeapproach uses identical sensors working at different operating temperatures thanks to the thermalgradient created by an integrated microheater. A system with in-house developed hardware and softwarewas used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitativediscrimination of all gases after calibration. The system worked perfectly within the calibrated range(100% correct classification, 6.9% average error on concentration value). This work focuses on minimizingthe number of points needed for calibration while maintaining good sensor performance, both forclassification and error in estimating concentration. Therefore, the calibration range (in terms of gasconcentration) was gradually reduced and further tests were performed with concentrations outsidethese new reduced limits. Although with only a few training points, down to just two per gas, the systemperformed well with 96% correct classifications and 31.7% average error for the gases at concentrationsup to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower thanthe calibration range, the system worked less well, with 93% correct classifications and 38.6% averageerror, probably due to proximity to the limit of detection of the sensors.
|
|
