PtS ₂ monolayer with desirable gas sensing property is suggested in this work based on the first-principles theory for detection of three SF ₆ decomposed species, in order to evaluate the operation status of the SF ₆ insulation devices in power systems, and Pd-doping is proposed to enhance the sensing performance of PtS ₂ monolayer. Results indicate that Pd dopant is stably anchored on the $\text{T}_{\rm Pt}$ site of the PtS ₂ surface with a ${E}_{\rm b}$ of −2.34 eV. The adsorption performance of the Pd-doped PtS ₂ (Pd-PtS ₂ ) monolayer upon three SF ₆ decomposed species is in order as SO ₂ > SOF ₂ > SO ₂ F ₂ , with ${E}_{\rm ad}$ of −1.04, −0.84 and −0.19 eV, respectively. Thus, chemisorption is identified for the SO ₂ and SOF ₂ systems with remarkable increase of bandgap for the Pd-PtS ₂ monolayer by 0.044 and 0.041 eV, respectively, while physisorption is identified for the SO ₂ F ₂ system with relatively smaller increase of bandgap by 0.029 eV. Moreover, an applied electric field can effectively modulate the charge-transfer for the gas adsorption systems, which manifests the strong potential of Pd-PtS ₂ monolayer as a field-effect transistor gas sensor for detection of SO ₂ , SOF ₂ and SO ₂ F ₂ with high sensing response. Moreover, I-V curves in Pd-PtS ₂ system with and without gas adsorption are also analyzed to verify the above findings. Our theoretical report is meaningful to explore novel sensing materials for application in the field of electrical engineering and to guarantee the good operation of power systems.

Electronic noses (E-noses) have been successfully applied in various fields. However, as a result of the inherent variability of chemical sensors, a signal processing algorithm well trained with the data from the existing domain often cannot be directly applied to the domain of interest. This severely limits the large-scale use of E-noses. In this paper, an unsupervised discriminative domain reconstruction based extreme learning machine (DDR-ELM) is proposed to compensate for such drifts/shifts and address the domain adaptation problem. Specifically, the method learns a domain-invariant space to minimize the distribution difference between different domains by discriminatively handling the different domain data using an extreme learning machine (ELM) framework. This method retains as many of the useful spatial characteristics of the source domain as possible and reduces the divergence between domains without any labeled target domain data. It avoids the cost and labor of obtaining access to the labels of data from the domain of interest. In addition, both the domain reconstruction and classification processes utilize the ELM, which is solved by pseudoinverse operations without error back-propagation iterations, consequently keeping computational complexity low. Experiments on different sensor datasets demonstrate that the proposed method is superior to several state-of-the-art drift/shift compensation methods not only in classification accuracy but also maintaining higher efficiency.

The gas sensor drift problem arises from the bias of data, which is known as a significant problem in the artificial olfactory community. Traditionally, hardware calibration methods are laborious and ineffective due to frequent recalibration actions involving different gases, and some calibration transfer and baseline calibration methods are not effective enough. In this work, a local manifold embedding cross-domain subspace learning (LME-CDSL) model is proposed based on domain distribution alignment. It is a unified subspace learning model combined with manifold learning and domain adaptation, which tends to explore a latent transform matrix that not only enforces the drifted target domain data to learn the manifold of nondrifted source domain data but also adopts the domain adaptation method to align the domain data distribution. In general, the LME-CDSL model has three features: 1) the unsupervised and adaptation distribution subspace projection can be easily computed through eigenvector decomposition; 2) the local linear manifold learns to achieve the compact representations of high-dimensional data and is capable of preserving the local features of nondrifted samples; and 3) the domain adaptation part utilizes the maximum mean discrepancy (MMD) and variance maximization to make the sample distributions of different domains more similar and preserve the intrinsic properties. For long-term and short-term drift compensation on a single E-nose system, the local manifold embedding cross-domain subspace learning (LME-CDSL) model obtains the average recognition accuracy of 70.95% and 74.09%, respectively, while 71.71% and 73.96%, respectively for multiple identical E-nose systems with both long-term and interplate drift, which are higher than several comparative methods and proves the its effectiveness and superiority on anti-drift and gas recognition.

When the electronic nose (E-nose) is used to predict the concentration of mixed gas, the traditional regression prediction algorithm may lead to unsatisfactory prediction results and long training time. In order to improve the accuracy of regression prediction and reduce the training time, we proposed a regression prediction algorithm based on broad learning system (BLS) to predict the concentration of mixed gas. To further improve the accuracy of model predictions, we optimize the various parameters existing in the model to improve the performance of the model. Then, we change the initial random mapping weight assignment method of the model to further improve the data processing ability of the model, and the improved model is called GBLS. In the data processing experiment, we use the mixed gas of methane and ethylene as the test gas to test the GBLS model proposed in this article. We have compared GBLS with other existing methods including back propagation neural networks (BPNN), least squares support vector machines (LSSVM), extremely learning machine (ELM), linear regression (LR). Experimental results show that the proposed GBLS outperforms the other methods.

The poor response time and incomplete recovery of MoS ₂ sensor at room temperature (RT) have been a serious issue to obstruct its widely sensing applications. In present work, we comprehensively studied three methods: rising temperature, building field-effect transistors (FETs), and adding light illumination to improve the performance of the device. The related sensing mechanism of the three processes was also analyzed. The high response rate up to 2510%, low theoretical concentration detection limit down to 693 ppb, and excellent recovery at RT to NO ₂ were achieved for the MoS ₂ FET sensor with the aid of light illumination. This significant improvement in sensing performances is mainly attributed to the activated sensing layer by phonon energy transfer and enhanced population of the charge carrier raised from optical energy. Schottky barrier modulation was also an important factor to impact sensing performance. Furthermore, the MoS ₂ nanosheets exhibit good photosensing characteristics such as rapid photon response, high light-induced photocurrent, and so on. It has been demonstrated that the light illuminated FET configuration can effectively accelerate response time and promote recovery rate of MoS ₂ -based sensor operating at RT, which provides a strategy to design an optoelectronic device with optimal sensing properties.

Novel sensing applications benefit from multifunctional nanomaterials responsive to various external stimuli such as mechanics, electricity, light, humidit