Direct Selective Epitaxy of 2D Sb2Te3 onto Monolayer WS2 for Vertical p-n Heterojunction Photodetectors.

Two-dimensional transition metal dichalcogenides (2D-TMDs) possess appropriate bandgaps and interact via van der Waals (vdW) forces between layers, effectively overcoming lattice compatibility challenges inherent in traditional heterojunctions. This property facilitates the creation of heterojunctions with customizable bandgap alignments. However, the prevailing method for creating heterojunctions with 2D-TMDs relies on the low-efficiency technique of mechanical exfoliation. Sb2Te3, recognized as a notable p-type semiconductor, emerges as a versatile component for constructing diverse vertical p-n heterostructures with 2D-TMDs. This study presents the successful large-scale deposition of 2D Sb2Te3 onto inert mica substrates, providing valuable insights into the integration of Sb2Te3 with 2D-TMDs to form heterostructures. Building upon this initial advancement, a precise epitaxial growth method for Sb2Te3 on pre-existing WS2 surfaces on SiO2/Si substrates is achieved through a two-step chemical vapor deposition process, resulting in the formation of Sb2Te3/WS2 heterojunctions. Finally, the development of 2D Sb2Te3/WS2 optoelectronic devices is accomplished, showing rapid response times, with a rise/decay time of 305 µs/503 µs, respectively.

Long-range order enabled stability in quantum dot light-emitting diodes.

Light-emitting diodes (LEDs) based on perovskite quantum dots (QDs) have produced external quantum efficiencies (EQEs) of more than 25% with narrowband emission1,2, but these LEDs have limited operating lifetimes. We posit that poor long-range ordering in perovskite QD films-variations in dot size, surface ligand density and dot-to-dot stacking-inhibits carrier injection, resulting in inferior operating stability because of the large bias required to produce emission in these LEDs. Here we report a chemical treatment to improve the long-range order of perovskite QD films: the diffraction intensity from the repeating QD units increases three-fold compared with that of controls. We achieve this using a synergistic dual-ligand approach: an iodide-rich agent (aniline hydroiodide) for anion exchange and a chemically reactive agent (bromotrimethylsilane) that produces a strong acid that in situ dissolves smaller QDs to regulate size and more effectively removes less conductive ligands to enable compact, uniform and defect-free films. These films exhibit high conductivity (4 × 10-4 S m-1), which is 2.5-fold higher than that of the control, and represents the highest conductivity recorded so far among perovskite QDs. The high conductivity ensures efficient charge transportation, enabling red perovskite QD-LEDs that generate a luminance of 1,000 cd m-2 at a record-low voltage of 2.8 V. The EQE at this luminance is more than 20%. Furthermore, the stability of the operating device is 100 times better than previous red perovskite LEDs at EQEs of more than 20%.

Conductive MXene/Polymer Composites for Transparent Flexible Supercapacitors.

Transparent flexible energy storage devices are limited by the trade-off among flexibility, transparency, and charge storage capability of their electrode materials. Conductive polymers are intrinsically flexible, but limited by small capacitance. Pseudocapacitive MXene provides high capacitance, yet their opaque and brittle nature hinders their flexibility and transparency. Herein, the development of synergistically interacting conductive polymer Ti3C2Tx MXene/PEDOT:PSS composites is reported for transparent flexible all-solid-state supercapacitors, with an outstanding areal capacitance of 3.1 mF cm-2, a high optical transparency of 61.6%, and excellent flexibility and durability. The high capacitance and high transparency of the devices stem from the uniform and thorough blending of PEDOT:PSS and Ti3C2Tx, which is associated with the formation of O─H…O H-bonds in the composites. The conductive MXene/polymer composite electrodes demonstrate a rational means to achieve high-capacity, transparent and flexible supercapacitors in an easy and scalable manner.

Diagnostic significance of HRCT imaging features in adult mycoplasma pneumonia: a retrospective study.

Mycoplasma pneumoniae pneumonia (MPP) often overlaps with the clinical manifestations and chest imaging manifestations of other types of community-acquired pneumonia (CAP). We retrospectively analyzed the clinical and imaging data of a group of patients with CAP, summarized their clinical and imaging characteristics, and discussed the diagnostic significance of their certain HRCT findings. The HRCT findings of CAP researched in our study included tree-in-bud sign (TIB), ground-glass opacity (GGO), tree fog sign (TIB + GGO), bronchial wall thickening, air-bronchogram, pleural effusion and cavity. The HRCT findings of all cases were analyzed. Among the 200 cases of MPP, 174 cases showed the TIB, 193 showed the GGO, 175 showed the tree fog sign, 181 lacked air-bronchogram. In case taking the tree fog sign and lack of air-bronchogram simultaneously as an index to distinguish MPP from OCAP, the sensitivity was 87.5%, the specificity was 97.5%, the accuracy was 92.5%. This study showed that that specific HRCT findings could be used to distinguish MPP from OCAP. The combined HRCT findings including the tree fog sign and lacked air-bronchogram simultaneously would contribute to a more accurate diagnosis of MPP.

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis.

Objective: This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC). Methods: In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared. Results: The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases. Conclusion: The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.

Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing.

Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.

ColonNet: A novel polyp segmentation framework based on LK-RFB and GPPD.

Colorectal cancer (CRC) holds the distinction of being the most prevalent malignant tumor affecting the digestive system. It is a formidable global health challenge, as it ranks as the fourth leading cause of cancer-related fatalities around the world. Despite considerable advancements in comprehending and addressing colorectal cancer (CRC), the likelihood of recurring tumors and metastasis remains a major cause of high morbidity and mortality rates during treatment. Currently, colonoscopy is the predominant method for CRC screening. Artificial intelligence has emerged as a promising tool in aiding the diagnosis of polyps, which have demonstrated significant potential. Unfortunately, most segmentation methods face challenges in terms of limited accuracy and generalization to different datasets, especially the slow processing and analysis speed has become a major obstacle. In this study, we propose a fast and efficient polyp segmentation framework based on the Large-Kernel Receptive Field Block (LK-RFB) and Global Parallel Partial Decoder(GPPD). Our proposed ColonNet has been extensively tested and proven effective, achieving a DICE coefficient of over 0.910 and an FPS of over 102 on the CVC-300 dataset. In comparison to the state-of-the-art (SOTA) methods, ColonNet outperforms or achieves comparable performance on five publicly available datasets, establishing a new SOTA. Compared to state-of-the-art methods, ColonNet achieves the highest FPS (over 102 FPS) while maintaining excellent segmentation results, achieving the best or comparable performance on the five public datasets. The code will be released at: https://github.com/SPECTRELWF/ColonNet.

Ternary Organic Solar Cells with Power Conversion Efficiency Approaching 15% by Fine-Selecting the Third Component.

Nonfullerene acceptors with mediate bandgap play a crucial role in ternary devices as the third component, further boosting the performance of organic solar cells (OSCs). Herein, three F-series acceptors (F-H, F-Cl, and F-2Cl) with mediate bandgap are selected and introduced into the PM6:BDT-Br binary system as third component to find the detailed influence of end groups with chlorine (Cl) atom substitution on the performance of ternary organic solar cells. Due to the increased substitution of Cl atoms on the end groups, F-Cl and F-2Cl as guest acceptors reveal a superior ability to regulate the morphology of blend films, contributing to the ordered packing properties and high crystallinity. As a result, F-Cl and F-2Cl based ternary OSCs achieve significantly improved PCEs of 13.89% and 14.67%, respectively, compared with the binary devices (12.70%). On the contrary, F-H without Cl atom displays a poor compatibility with the host system, resulting in an inferior ternary device with a low PCE of 10.79%. This work indicates that F-series acceptors with mediate bandgap are a promising class of third component for high-performance ternary OSCs. And introducing more Cl atoms substitution on the end groups, especially F-2Cl, will own a broad applicability for other binary devices.

A Comprehensive Review on Metal Catalysts for the Production of Cyclopentanone Derivatives from Furfural and HMF.

The catalytic transformation of biomass-based furan compounds (furfural and HMF) for the synthesis of organic chemicals is one of the important ways to utilize renewable biomass resources. Among the numerous high-value products, cyclopentanone derivatives are a kind of valuable compound obtained by the hydrogenation rearrangement of furfural and HMF in the aqueous phase of metal-hydrogen catalysis. Following the vast application of cyclopentanone derivatives, this reaction has attracted wide attention since its discovery, and a large number of catalytic systems have been reported to be effective in this transformation. Among them, the design and synthesis of metal catalysts are at the core of the reaction. This review briefly introduces the application of cyclopentanone derivatives, the transformation mechanism, and the pathway of biomass-based furan compounds for the synthesis of cyclopentanone derivatives. The important progress of metal catalysts in the reaction since the first report in 2012 up to now is emphasized, the characteristics and catalytic performance of different metal catalysts are introduced, and the critical role of metal catalysts in the reaction is discussed. Finally, the future development of this transformation process was prospected.

Inkjet-printed h-BN memristors for hardware security.

Inkjet printing electronics is a growing market that reached 7.8 billion USD in 2020 and that is expected to grow to ∼23 billion USD by 2026, driven by applications like displays, photovoltaics, lighting, and radiofrequency identification. Incorporating two-dimensional (2D) materials into this technology could further enhance the properties of the existing devices and/or circuits, as well as enable the development of new concept applications. Along these lines, here we report an easy and cheap process to synthesize inks made of multilayer hexagonal boron nitride (h-BN)-an insulating 2D layered material-by the liquid-phase exfoliation method and use them to fabricate memristors. The devices exhibit multiple stochastic phenomena that are very attractive for use as entropy sources in electronic circuits for data encryption (physical unclonable functions [PUFs], true random number generators [TRNGs]), such as: (i) a very disperse initial resistance and dielectric breakdown voltage, (ii) volatile unipolar and non-volatile bipolar resistive switching (RS) with a high cycle-to-cycle variability of the state resistances, and (iii) random telegraph noise (RTN) current fluctuations. The clue for the observation of these stochastic phenomena resides on the unpredictable nature of the device structure derived from the inkjet printing process (i.e., thickness fluctuations, random flake orientations), which allows fabricating electronic devices with different electronic properties. The easy-to-make and cheap memristors here developed are ideal to encrypt the information produced by multiple types of objects and/or products, and the versatility of the inkjet printing method, which allows effortless deposition on any substrate, makes our devices especially attractive for flexible and wearable devices within the internet-of-things.

Reducing Contact Resistance in Organic Field-Effect Transistors: A Comprehensive Comparison between 2D and Microrod Single Crystals.

A comprehensive comparison of organic single crystals based on a single material but with different dimensions provides a unique approach to probe their carrier injection mechanism. In this report, both two-dimensional (2D) and microrod single crystals with the same crystalline structure of an identical thiopyran derivative, 7,14-dioctylnaphtho[2,1-f:6,5-f']bis(cyclopentane[b]thiopyran) (C8-SS), are grown on a glycerol surface with the space-confined method. Organic field-effect transistors (OFETs) based on the 2D C8-SS single crystal exhibit superior performance compared with those based on the microrod single crystal, particularly in their contact resistance (RC). It is demonstrated that the resistance of the crystal bulk in the contact region plays a key role in RC of the OFETs. Thus, among the 30 devices tested, the microrod OFETs typically appear contact-limited, whereas the 2D OFETs possess significantly reduced RC arising from the tiny thickness of the 2D single crystal. The 2D OFETs show high operational stability and high channel mobility up to 5.7 cm2/V·s. The elucidation of the contact behavior highlights the merits and great potential of 2D molecular single crystals in organic electronics.

Advances of Carbon Materials for Dual-Carbon Lithium-Ion Capacitors: A Review.

Lithium-ion capacitors (LICs) have drawn increasing attention, due to their appealing potential for bridging the performance gap between lithium-ion batteries and supercapacitors. Especially, dual-carbon lithium-ion capacitors (DC-LICs) are even more attractive because of the low cost, high conductivity, and tunable nanostructure/surface chemistry/composition, as well as excellent chemical/electrochemical stability of carbon materials. Based on the well-matched capacity and rate between the cathode and anode, DC-LICs show superior electrochemical performances over traditional LICs and are considered to be one of the most promising alternatives to the current energy storage devices. In particular, the mismatch between the cathode and anode could be further suppressed by applying carbon nanomaterials. Although great progresses of DC-LICs have been achieved, a comprehensive review about the advances of electrode materials is still absent. Herein, in this review, the progresses of traditional and nanosized carbons as cathode/anode materials for DC-LICs are systematically summarized, with an emphasis on their synthesis, structure, morphology, and electrochemical performances. Furthermore, an outlook is tentatively presented, aiming to develop advanced DC-LICs for commercial applications.

Pulmonary Function in Metabolic Syndrome: A Meta-Analysis.

Background: This study aims to systematically evaluate the association between metabolic syndrome (MS) and pulmonary function through meta-analysis. Methods: Electronic databases, including PubMed, Embase, Web of Science, and Cochrane Library, were systematically searched to obtain articles associated with MS and lung function published before December 31, 2021. According to the including and excluding criteria, certain studies were obtained and data were extracted. The Newcastle Ottawa Scale was used to evaluate the quality of the studies. A pooled standardized mean difference (SMD) was calculated by means of random-effects meta-analysis. Different effect models were used according to the heterogeneity. Meta-regression and sensitivity analyses were performed to examine the possible sources of heterogeneity. The Begg's funnel plot and Egger's test were used to evaluate publication bias. Analyses were performed using Stata MP, version14.0 (StataCorp LP, College Station, TX, USA). Results: A total of 15 studies, involving 10,285 cases of MS and 25,416 cases of control, were included in this meta-analysis on the relationship between MS and forced vital capacity (FVC). The pooled SMD for FVC was -0.247 (95% CI = -0.327 to -0.2167, P < 0.001) using random effect model, indicating the decrease of FVC in the patients with MS. In the same studies, the pooled SMD for forced expiratory volume in 1 sec (FEV1) was -0.205 (95% CI = -0.3278 to -0.133, P < 0.001), indicating the decrease of FEV1 also existed in the MS cases. A total of 13 studies, involving 8167 cases of MS and 19,788 cases of control, were included in this meta-analysis on the relationship between MS and FEV1/FVC. The pooled SMD for FEV1/FVC was 0.011 (95% CI = -0.072 to 0.093, P = 0.798) using random effect model, indicating that there was no significant difference between the patients with MS and the control. After introducing the diastolic blood pressure and glycemia into the regression model of the relationship between MS and FVC, the variance of the studies (tau2) decreased from 0.0190 to 0.006694 and 0.007205, which could explain 66.70% and 78.04% of the sources of heterogeneity, and the P values were 0.038 and 0.023. The results suggested that hypertension (diastolic pressure) and hyperglycemia were the factors linked to the heterogeneity among the included studies on both FVC and FEV1. The Begg's funnel plot and Egger's test both showed no evidence of publication bias. Conclusions: Our results show that FVC and FEV1 decrease in MS patients, while FEV1/FVC has no significant difference compared with the control group. It indicates that the patients with MS have restrictive ventilatory functional disturbance. Meta-regression analysis suggests that hypertension (diastolic pressure) and hyperglycemia are the factors linked to the heterogeneity among the included studies on both FVC and FEV1.

ZnO nanowire optoelectronic synapse for neuromorphic computing.

Artificial synapses that integrate functions of sensing, memory and computing are highly desired for developing brain-inspired neuromorphic hardware. In this work, an optoelectronic synapse based on the ZnO nanowire (NW) transistor is achieved, which can be used to emulate both the short-term and long-term synaptic plasticity. Synaptic potentiation is present when the device is stimulated by light pulses, arising from the light-induced O2desorption and the persistent photoconductivity behavior of the ZnO NW. On the other hand, synaptic depression occurs when the device is stimulated by electrical pulses in dark, which is realized by introducing a charge trapping layer in the gate dielectric to trap carriers. Simulation of a neural network utilizing the ZnO NW synapses is carried out, demonstrating a high recognition accuracy over 90% after only 20 training epochs for recognizing the Modified National Institute of Standards and Technology digits. The present nanoscale optoelectronic synapse has great potential in the development of neuromorphic visual systems.

A Pyramid Architecture-Based Deep Learning Framework for Breast Cancer Detection.

Breast cancer diagnosis is a critical step in clinical decision making, and this is achieved by making a pathological slide and gives a decision by the doctors, which is the method of final decision making for cancer diagnosis. Traditionally, the doctors usually check the pathological images by visual inspection under the microscope. Whole-slide images (WSIs) have supported the state-of-the-art diagnosis results and have been admitted as the gold standard clinically. However, this task is time-consuming and labour-intensive, and all of these limitations make low efficiency in decision making. Medical image processing protocols have been used for this task during the last decades and have obtained satisfactory results under some conditions; especially in the deep learning era, it has exhibited the advantages than those in the shallow learning period. In this paper, we proposed a novel breast cancer region mining framework based on deep pyramid architecture from multilevel and multiscale breast pathological WSIs. We incorporate the tissue- and cell-level information together and integrate these into a LSTM model for the final sequence modelling, which successfully keeps the WSIs' integration and is not mentioned by the prevalence frameworks. The experiment results demonstrated that our proposed framework greatly improved the detection accuracy than that only using tissue-level information.

CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer.

Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis of the tumour region is critical for the doctors. But with data accumulation, this task takes lots of time and labor with large variances between different doctors. With the development of computer vision, detection and segmentation of the colorectal cancer region from CT or MRI image series are a great challenge in the past decades, and there still have great demands on automatic diagnosis. In this paper, we proposed a novel transfer learning protocol, called CST, that is, a union framework for colorectal cancer region detection and segmentation task based on the transformer model, which effectively constructs the cancer region detection and its segmentation jointly. To make a higher detection accuracy, we incorporate an autoencoder-based image-level decision approach that leverages the image-level decision of a cancer slice. We also compared our framework with one-stage and two-stage object detection methods; the results show that our proposed method achieves better results on detection and segmentation tasks. And this proposed framework will give another pathway for colorectal cancer screen by way of artificial intelligence.

Graphene-Based Cathode Materials for Lithium-Ion Capacitors: A Review.

Lithium-ion capacitors (LICs) are attracting increasing attention because of their potential to bridge the electrochemical performance gap between batteries and supercapacitors. However, the commercial application of current LICs is still impeded by their inferior energy density, which is mainly due to the low capacity of the cathode. Therefore, tremendous efforts have been made in developing novel cathode materials with high capacity and excellent rate capability. Graphene-based nanomaterials have been recognized as one of the most promising cathodes for LICs due to their unique properties, and exciting progress has been achieved. Herein, in this review, the recent advances of graphene-based cathode materials for LICs are systematically summarized. Especially, the synthesis method, structure characterization and electrochemical performance of various graphene-based cathodes are comprehensively discussed and compared. Furthermore, their merits and limitations are also emphasized. Finally, a summary and outlook are presented to highlight some challenges of graphene-based cathode materials in the future applications of LICs.

Selective construction and stability studies of a molecular trefoil knot and Solomon link.

Two novel compounds, a molecular trefoil knot and a Solomon link, were constructed successfully through the cooperation of multiple π-π stacking interactions. A reversible transformation between the trefoil knot and the corresponding [2 + 2] macrocycle could be achieved by solvent- and guest-induced effects. However, the Solomon link maintains its stability in different concentrations, solvents and guest molecules. Single-crystal X-ray crystallographic data, NMR spectroscopic experiments and ESI-MS support the synthesis and structural assignments. These synthesis methods open the door to the further development of smart materials, which will push the advancement of rational design of biomaterials.

Amine-Assisted Delaminated 2D Ti3C2Tx MXenes for High Specific Capacitance in Neutral Aqueous Electrolytes.

Electrochemical capacitors using neutral aqueous electrolytes are safer and cheaper and allow diverse current collectors compared with the counterparts using organic or acidic/alkaline electrolytes. Two-dimensional (2D) MXenes have been demonstrated as the high-capacitive materials with high rate performance. However, MXene electrodes often exhibit a limited capacitance in neutral electrolytes, where the reversible electrochemical reactions rely greatly on the structural and surface properties of MXenes depending on their synthesis methods. Herein, a simple and highly efficient strategy, which combines HF etching of Ti3AlC2 powder and subsequent amine-assisted delamination at a low temperature, is developed to synthesize 2D Ti3C2Tx MXenes. The comprehensive results demonstrate that the enlarged interlayer spacing and the presence of more -O-containing functional groups synergistically contribute to the improvement of capacitive performance in neutral electrolytes. The 2D Ti3C2Tx MXenes show excellent electrochemical performance in various neutral electrolytes, and a high specific gravimetric capacitance of 149.8 F/g is achieved in 1.0 M Li2SO4. Furthermore, the flexible solid-state supercapacitors (SCs) with a neutral PVA/LiCl gel electrolyte possess a superior areal capacitance (163.1 mF/cm2) and high energy density (17.6 µWh/cm2 at 0.07 mW/cm2), together with high user safety. This work provides a promising guideline of synthesis strategy for high-capacitive MXenes used in neutral electrolytes, which may promote the development of safe and flexible power sources with a high energy density.

Conducting polymer-inorganic nanocomposite-based gas sensors: a review.

With the rapid development of conductive polymers, they have shown great potential in room-temperature chemical gas detection, as their electrical conductivity can be changed upon exposure to oxidative or reductive gas molecules at room temperature. However, due to their relatively low conductivity and high affinity toward volatile organic compounds and water molecules, they always exhibit low sensitivity, poor stability, and gas selectivity, which hinder their practical gas sensor applications. In addition, inorganic sensitive materials show totally different advantages in gas sensors, such as high sensitivity, fast response to low concentration analytes, high surface area, and versatile surface chemistry, which could complement the conducting polymers in terms of the sensing characteristics. It seems to be a win-win choice to combine inorganic sensitive materials with polymers for gas detection due to their synergistic effects, which has attracted extensive interests in gas-sensing applications. In this review, we summarize the recent development in polymer-inorganic nanocomposite based gas sensors. The roles of inorganic nanomaterials in improving the gas-sensing performances of conducting polymers are introduced and the progress of conducting polymer-inorganic nanocomposites including metal oxides, metal, carbon (carbon nanotube, graphene), and ternary composites are presented. Finally, a conclusion and a perspective in the field of gas sensors incorporating conducting polymer-inorganic nanocomposite are summarized.