Template-assisted fabrications of nanostructure arrays for gas-sensing applications

The highly-sensitive gas-sensing proposes a high requirement to a sensing platform, for which the nanostructure-array-based platform is a promising candidate. Template-assisted method is an effective strategy to prepare various nanostructure arrays. Herein, by using templates of ultrathin alumina membranes and colloidal monolayers, two kinds of nanostructure array gas sensors (i.e., nanorod arrays and films with arrayed triangular convexes) are prepared and exhibit a series of morphology origin of enhanced performances. In gas-sensing works using SnO2 nanorod arrays, the optimized gas-sensing is achieved by adjustments of the nanorod length from 20 to 340 nm and characterized by a low detection limit of 3 ppm ethanol gas at room temperature when the nanorod length is 40 nm. For the SnO2 film gas sensor, its triangular convex adsorption active sites can enhance surface adsorptions. By ensembles of these adsorption active sites with different periods (289, 433, 577, and 1154 nm), these samples present an adsorption active site origin of sensitivity, and being capable of detecting a low concentration of 6 ppm ethanol gas. The above morphology-to-performance correlations confirm that the template-assisted fabrications of nanostructure arrays are efficient to the fabrication of high-performance gas sensors.Hochempfindliche Gasdetektion stellt hohe Anforderungen an die zu verwendende Messplattform, für welche Nanostrukturarray-basierte Messfühler vielversprechende Kandidaten sind. Die Template gestützte Methode stellt eine effektive Grundlage zur Herstellung verschiedener Nanostrukturarrays dar. In dieser Arbeit werden mit Hilfe von ultradünnen Aluminiumoxid-Membranen oder kolloidalen Monolayern als Templat zwei verschiedene Arten von Nanostrukturarray-Gassensoren (Nanorod-Arrays und dünne Schichten mit angeordneten dreieckigen Wölbungen) hergestellt, welche aufgrund ihrer Morphologie eine erhöhte Leistungsfähigkeit aufweisen. Bei der Gasdetektion mit SnO2-Nanorod-Arrays wurde die optimierte Gasmessung durch Anpassung der Nanorod-Länge auf 20 bis 340 nm erreicht. Charakterisiert wird sie durch eine niedrige Detektionsschwelle von 3 ppm Ethanol-Gas bei Raumtemperatur und einer Nanorod-Länge von 40 nm. Bei den SnO2-Dünnschicht-Gassensoren erhöhen die dreieckigen konvexen Wölbungen die aktive Adsorptionsfläche für die Gasmessempfindlichkeit. Die Anordnung dieser adsorptionsaktiven Punkte mit unterschiedlicher Periodizität (289, 433, 577 und 1154 nm) zeigt eine Sensitivitätsabhängigkeit auf, wobei eine niedrige Detektionsschwelle von 6 ppm Ethanol-Gas erreicht wird. Die obigen Korrelationen zwischen Morphologie und Leistungsfähigkeit bestätigen, dass die Template gestützte Herstellung von Nanostrukturarrays zur Produktion von hochleistungsfähigen Gassensoren effizient genutzt werden kann

An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Three-dimensional convolutional neural networks (3DCNNs), a rapidly evolving modality of deep learning, has gained popularity in many fields. For oral cancers, CT images are traditionally processed using two-dimensional input, without considering information between lesion slices. In this paper, we established a 3DCNNs-based image processing algorithm for the early diagnosis of oral cancers, which was compared with a 2DCNNs-based algorithm. The 3D and 2D CNNs were constructed using the same hierarchical structure to profile oral tumors as benign or malignant. Our results showed that 3DCNNs with dynamic characteristics of the enhancement rate image performed better than 2DCNNS with single enhancement sequence for the discrimination of oral cancer lesions. Our data indicate that spatial features and spatial dynamics extracted from 3DCNNs may inform future design of CT-assisted diagnosis system

The Global Research Trend in Electrochemical Microfluidic Technology: A Bibliometric Review

Electrochemical microfluidic technology has been extensively studied in many fields due to its significant advantages. Despite the increasing number of publications on the subject, there are no studies based on bibliometric analysis in this area. In this study, we performed a bibliometric analysis of 457 publications related to electrochemical microfluidic technology during 2012–2021 through the Web of Science core database. Results show that the hot topics include the development of label-free immunosensors, biosensors, microfluidic device performance, and low cost of equipment, and the major contributors to these publications are from China, USA, Spain, India, and Germany. In addition, applications of electrochemical microfluidics in biomedicine, food safety, and environmental monitoring are summarized and analyzed. Finally, the future challenges as well as strategies for future research are discussed. This bibliometric review will be useful for researchers in gaining new insights into the electrochemical microfluidic technology

Effect of Nitrogen Addition on Shape Memory Characteristics of Fe-Mn-Si-Cr Alloy

Nitrogen-microalloying and partial substitution of Cr for Mn have been employed to enhance the shape memory effect and corrosion resistance of Fe-Mn-Si based alloys. Typically, the tested alloys with nominal composition Fe-25Mn-6Si-5Cr-(0.12-0.14)N in mass% exhibit perfect shape recovery for a 3% pre-strain after only one cycle of thermomechanical training. The related mechanism has been discussed, taking account of the effect of nitrogen on the stacking fault energy (SFE) or the stacking fault probability (P sf ) of the alloy and the strengthening of the austenite matrix. Thermodynamic calculation and P sf measurement showed that the SFE increases with increasing N-content in the concentration range investigated, e.g. less than 0.3 mass%. Thus, the critical stress for the formation of stress-induced martensite increases with N-content. It is believed that the interstitial strengthening of the matrix by nitrogen predominantly contributes to the improvement of shape memory effect. Besides, nitrogen-microalloying remarkably improves the corrosion resistance of the alloys in aqueous solutions containing NaOH and NaCl, but not in HCl solution as indicated by the long-term immersion tests

Carrier Mobility-Dominated Gas Sensing: A Room-Temperature Gas-Sensing Mode for SnO₂ Nanorod Array Sensors

Adsorption-induced change of carrier density is presently dominating inorganic semiconductor gas sensing, which is usually operated at a high temperature. Besides carrier density, other carrier characteristics might also play a critical role in gas sensing. Here, we show that carrier mobility can be an efficient parameter to dominate gas sensing, by which room-temperature gas sensing of inorganic semiconductors is realized via a carrier mobility-dominated gas-sensing (CMDGS) mode. To demonstrate CMDGS, we design and prepare a gas sensor based on a regular array of SnO₂ nanorods on a bottom film. It is found that the key for determining the gas-sensing mode is adjusting the length of the arrayed nanorods. With the change in the nanorod length from 340 to 40 nm, the gas-sensing behavior changes from the conventional carrier-density mode to a complete carrier-mobility mode. Moreover, compared to the carrier density-dominating gas sensing, the proposed CMDGS mode enhances the sensor sensitivity. CMDGS proves to be an emerging gas-sensing mode for designing inorganic semiconductor gas sensors with high performances at room temperature

Cross‐class pest and disease vegetation detection based on small sample registration

Abstract This paper introduces few‐shot anomaly detection (FSAD), a practical and less anomaly detection (AD) method, which can provide a limited number of normal images for each class during training. So far, studies on FSAD have been carried out according to each model, and there is no discussion of commonalities between different types. Depending on how people detect unusual lies, the problematic images are compared to the normal ones. The image alignment method based on different classifications is used to train the target detection model independent of classification, and performed ablation experiments on the pest and disease datasets in different environments for verification. This is the first time the FSAD method has been used to train a single scalable model without the need to train new classifications or adjust parameters. The experimental results show that the application of AUC based on vegetation disease data set and vegetation pest data set in FSAD algorithm is improved by 19.5% compared with the existing algorithm

Optimization algorithm comparison.

It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper.</div

Three-Dimensional Plasmonic Nanostructure Design for Boosting Photoelectrochemical Activity

Plasmonic nanostructures have been widely incorporated into different semiconductor materials to improve solar energy conversion. An important point is how to manipulate the incident light so that more light can be efficiently scattered and absorbed within the semiconductors. Here, by using a tunable three-dimensional Au pillar/truncated-pyramid (PTP) array as a plasmonic coupler, a superior optical absorption of about 95% within a wide wavelength range is demonstrated from an assembled CdS/Au PTP photoanode. Based on incident photon to current efficiency measurements and the corresponding finite difference time domain simulations, it is concluded that the enhancement is mainly attributed to an appropriate spectral complementation between surface plasmon resonance modes and photonic modes in the Au PTP structure over the operational spectrum. Because both of them are wavelength-dependent, the Au PTP profile and CdS thickness are further adjusted to take full advantage of the complementary effect, and subsequently, an angle-independent photocurrent with an enhancement of about 400% was obtained. The designed plasmonic PTP nanostructure of Au is highly robust, and it could be easily extended to other plasmonic metals equipped with semiconductor thin films for photovoltaic and photoelectrochemical cells

Attention weight ratio diagram.

It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper.</div

Event-Sensitive Network: A Construction Algorithm of Agricultural Sensor Network Driven by Environmental Change

In a wireless sensor network, the sensor nodes transmit the acquired information to the server through the data transmission link. On the serverside, the data are processed, fused, and expressed to serve the user. Sensor deployment is a key factor related to the stability and security of wireless networks. This article uses environmental changes to drive related technologies to deploy wireless sensors. In this article, environmental change-driven means that through certain deployment cost model assumptions and problem descriptions, network deployment is artificially divided into two stages: initial deployment and redeployment. In the deployment phase, by referring to the idea of virtual force, a new sensor deployment algorithm is proposed in the redeployment phase, which can well solve the stability- and security-related issues encountered in agricultural wireless sensor networks. In this algorithm, the moving distance of the mobile receiver and the average coverage of the network are calculated based on the virtual force, the direction, and the number of adjacent clusters. Finally, the algorithm model was simulated in MATLAB, and the feasibility of the algorithm was verified by analyzing the event coverage and the moving distance of nodes. The final simulation results show that the algorithm proposed in this paper can achieve better performance than existing algorithms in terms of average coverage and moving distance