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This work utilizes the multi-task symbolic regression method to distill a universal descriptor for evaluating OER activity of perovskites from various experimental publications, resulting in two new nickel-based perovskites with good performance.

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electrochemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ∼7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

Two-dimensional carbon nitride (2D-C3N4) nanosheets are promising materials in photocatalytic water splitting, but still suffer from easy agglomeration and fast photogenerated electron–hole pairs recombination. To tackle this issue, herein, a hierarchical Nb2O5/2D-C3N4 heterostructure is precisely constructed and the built-in electric field between Nb2O5 and 2D-C3N4 can provide the driving force to separate/transfer the charge carriers efficiently. Moreover, the strongly Lewis acidic Nb2O5 can adsorb TEOA molecules on its surface at locally high concentrations to facilitate the oxidation reaction kinetics under irradiation, resulting in efficient photogenerated electrons-holes separation and exceptional photocatalytic hydrogen evolution. As expected, the champion Nb2O5/2D-C3N4 heterostructure achieves an exceptional H2 evolution rate of 31.6 mmol g–1 h−1, which is 213.6 times and 4.3 times higher than that of pristine Nb2O5 and 2D-C3N4, respectively. Moreover, the champion heterostructure possesses a high apparent quantum efficiency (AQE) of 45.08% at λ = 405 nm and superior cycling stability. Furthermore, a possible photocatalytic mechanism of the energy band alignment at the hetero-interface is proposed based on the systematical characterizations accompanied by density functional theory (DFT) calculations. This work paves the way for the precise construction of a high-quality heterostructured photocatalyst with efficient charge separation to boost hydrogen production.

Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.

Semiconductor chemiresistive gas sensors play critical roles in a smart and sustainable city where a safe and healthy environment is the foundation. However, the poor limits of detection and selectivity are the two bottleneck issues limiting their broad applications. Herein, a unique sensor design with a 3D tin oxide (SnOsub2/sub) nanotube array as the sensing layer and platinum (Pt) nanocluster decoration as the catalytic layer, is demonstrated. The Pt/SnOsub2/subsensor significantly enhances the sensitivity and selectivity of NOsub2/subdetection by strengthening the adsorption energy and lowering the activation energy toward NOsub2/sub. It not only leads to ultrahigh sensitivity to NOsub2/subwith a record limit of detection of 107 parts per trillion, but also enables selective NOsub2/subsensing while suppressing the responses to interfering gases. Furthermore, a wireless sensor system integrated with sensors, a microcontroller, and a Bluetooth unit is developed for the practical indoor and on-road NOsub2/subdetection applications. The rational design of the sensors and their successful demonstration pave the way for future real-time gas monitoring in smart home and smart city applications.

The development of redox-targeting co-catalysts is one of the important tasks in realizing hybrid photocatalytic systems for CO2 reduction reaction (CO2 RR), which has been sought after as a promis...

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties., Symbolic regression holds big promise for guiding materials design, yet its application in materials science is still limited. Here the authors use symbolic regression to introduce an activity descriptor predicting new oxide perovskites with improved oxygen evolution activity as corroborated by experimental validation.

Chemiresistive gas sensors with low power consumption, high sensitivity, and selectivity are of great significance for low-cost and efficient real-time gas detection. Herein, we present a highly se...

The accelerated evolution of communication platforms including Internet of Things (IoT) and the 5th generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks to monitor the chemical safety of our surroundings and personal health in real time. However, this application scenario requires a very challenging combination of characteristics of gas sensors including small formfactor, low-cost, ultra-low power consumption, superior sensitivity, and high intelligence. Herein, we demonstrate self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network for the first time. The SINGOR features ultra-high sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10 ppb, 2 ppb, 1 ppb and 1 ppb, respectively, obtained under room temperature sensor operation. Therefore, ultra-low power consumption of 68.6 μW at sensor level and 1.71 mW at the system level has been achieved. As the result, an individual SINGOR system can be self-driven by indoor light with a Si solar cell. Furthermore, each SINGOR consists of an array of nanostructured sensors powered by machine learning algorithm and thus a SINGOR has the capability of gas pattern recognition and classification. And multiple SINGOR systems are wirelessly connected together to form a sensor network for smart home application demonstrations. Intriguingly, the sensor network has successfully demonstrated flammable gas leakage detection and alarm function when they are deployed in multiple rooms. And when the SINGOR network is deployed in one single room, it can achieve gas leakage localization with satisfactory precision. These successes open up the possibility of using nanostructured-gas-sensor network for wide range applications including smart home, smart building and future smart city.

Although Sn-Pb perovskites sensing near-ultraviolet-visible-near-infrared light could be an attractive alternative to silicon in photodiodes and imaging, there have been no clear studies on such devices constructed on metal/silicon substrates, hindering their direct integration with complementary metal-oxide semiconductor (CMOS) and silicon electronics. Typically, high surface roughness and severe pinholes of Sn-rich binary perovskites make it difficult for them to fulfill the requirements of efficient photodiodes and imaging. These issues cause inherently high dark current and poor (dark and photo-) current uniformity. Herein, we propose and demonstrate the room-temperature crystallization in the Sn-rich binary perovskite system to effectively control film crystallization kinetics. With experimental and theoretical studies of the crystallization mechanism, we successfully tune the density and location of nanocrystals in precursor films to achieve compact nanocrystals, which coalesce into high-quality (smooth, dense, and pinhole-free) perovskites with intensified preferred orientation and decreased trap density. The high-quality perovskites reduce dark current and improve (dark and photo-) current uniformity of perovskite photodiodes on CMOS-compatible metal/silicon substrates. Meanwhile, self-powered devices achieve a high responsivity of 0.2 A/W at 940 nm, a large dynamic range of 100 dB, and a fast fall time of 2.27 μs, exceeding those of most silicon-based imaging sensors. Finally, a 6 × 6 pixel integrated photodiode array is successfully demonstrated to realize the imaging application. The work contributes to understanding the fundamentals of the crystallization of Sn-rich binary perovskites and advancing perovskite integration with Si-based electronics.

A novel oxygen defect-rich WO3−x nanoplates were synthesized using a solvothermal method followed by an annealing treatment under Ar–H2 gas mixture atmosphere. WO3−x nanoplates possess large numbers of oxygen defects systematically demonstrated by the elemental analysis, X-ray diffraction, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy, respectively. The adsorption capacity of defect-rich 2D WO3−x nanoplates was evaluated by removal of methyl orange (MO) and the adsorption capacity could reach to 63.7 µg mg−1 with good stability and reusability after. Their adsorption performance at the temperature of 298 K, 308 K and 318 K is carried out and proved to fit Langmuir isotherm model very well, suggestive of a monolayer adsorption on the materials surface sites. In addition, adsorption kinetics studies show that the adsorption process conforms to the pseudo-second-order equation model very well demonstrating a chemical process for MO adsorption. Compared to pure WO3 with annealing treatment at N2 and air atmospheres, the one treated under Ar–H2 gas mixture atmosphere (WO3−x) shows higher adsorption capacity and fast adsorption kinetics toward MO, which was attributed to deprivation of lattice oxygen of WO3 at the reducing atmosphere. Meanwhile, the 2D defect-rich WO3−x nanoplates also exhibit excellent adsorption properties toward methylene blue and rhodamine B, and they can be made into thin film by vacuum suction filtration for dynamically removing the organic pollutants. Our study shows that the as-synthesized defect-rich 2D WO3−x nanoplates have potential applications in real-time wastewater treatment.

Indium oxide (In2O3) nanomaterials have been investigated extensively as promising candidates for gas sensor applications. Here, we fabricate near room temperature operable H2S-sensitive gas sensors based on In2O3 colloidal quantum dots (CQDs). Room-temperature film deposition of In2O3 CQDs followed by surface ligand exchange treatment through Cu inorganic salts was employed to construct the sensor devices. Combined with a moderate annealing treatment, the long-chain ligands surrounding the quantum dot surfaces were completely removed to enhance gas adsorption and carrier transport, simultaneously resulting in the formation of CuO that may act as a catalytic promoter or form p-n heterojunction for selective H2S detection. The sensors exhibited high response up to 90 toward 5 ppm of H2S with response/recovery time of 72 s/200 s respectively, suggestive of a performance improvement compared to the In2O3-based H2S sensors reported in existing literature. The power law analysis indicated that the main oxygen species adsorbed on the surfaces were O2− and O− with the proportion of approximate 1:1 at near room temperature. The gas-sensing mechanism was attributed to the decisive role of surface states in determining the electrical conduction of quantum dot gas sensors.

Single-component graphitic carbon nitride (g-C3N4) are faced with inadequate visible light absorption and low quantum efficiency arising from the rapid charge recombination, limiting their efficient photocatalytic degradation of organic pollutant under visible light radiation. Here, we demonstrated a ternary photocatalyst composed of Ag3PO4, graphene oxide (GO), and g-C3N4 synthesized by chemical precipitation method, in which Ag3PO4 as the photosensitizer and GO as the cocatalyst that significantly promoted the photocatalytic activity of g-C3N4 for Rhodamine B (RhB) degradation under visible light radiation. The ternary photocatalysts (Ag3PO4/GO/g-C3N4) exhibited enhanced absorption in the visible region and superior photocatalytic activity compared with single-component or binary composite photocatalysts for RhB degradation. The degradation rate toward RhB could reach to 94.8% under visible light irradiation for 50 min, and we found that it was the hole (h+), superoxide radical (O2 −) and hydroxyl radical (OH ) that played a major role in RhB degradation. Meanwhile, the ternary photocatalyst showed enhanced photocatalytic stability attributing the synergistic effect among them, and the possible mechanism for the enhanced photocatalytic activity and stability was carefully discussed. The present work provides a facile development of a g-C3N4-based ternary photocatalyst system for highly improved photocatalytic activity by coupling a small amount of Ag-based photosensitizer and metal-free GO cocatalyst.

The accelerated evolution of communication platforms including Internet of Things (IoT) and the fifth generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks for real-time monitoring chemical safety and personal health. However, this application scenario requires a challenging combination of characteristics of gas sensors including small formfactor, low cost, ultralow power consumption, superior sensitivity, and high intelligence. Herein, self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network are demonstrated here. The room-temperature operated SINGOR system can be self-driven by indoor light with a Si solar cell, and it features ultrahigh sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10, 2, 1, and 1 ppb, respectively. Each SINGOR consisting of an array of nanostructured sensors has the capability of gas pattern recognition and classification. Furthermore, multiple SINGOR systems are wirelessly connected as a sensor network, which has successfully demonstrated flammable gas leakage detection and alarm function. They can also achieve gas leakage localization with satisfactory precision when deployed in one single room. These successes promote the development of using nanostructured-gas-sensor network for wide range applications including smart home/building and future smart city.

Colloidal quantum dots (CQDs) are considered as one of the ideal materials candidates for next-generation electronics and optoelectronics. Their large surface-to-volume ratio with exchangeable surface ligands offers a versatile freedom for the regulation of the semiconductor gas sensors. Here we demonstrated an atomic-ligand exchange strategy to construct highly sensitive and selective SnO2 quantum dot gas sensors. Both the cations and anions of the atomic ligands were engineered to enhance the gas adsorption and carrier transport via a film-level atomic ligand exchange treatment, among which the CuCl2-treated SnO2 CQD gas sensors exhibiting highest response of 1755 toward 50 ppm H2S with a fast response and recovery time (48 s/35 s) at 70 °C. The Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS) and UV–vis absorption spectra suggest that the long-chain organic ligands capping on the SnO2 CQDs were successfully exchanged by the atomic ligands and the quantum confinement of the CQDs were well conserved, which is favorable to preserve the catalytic activities of QD materials in real gas-sensing devices. Simultaneously, the copper (II) introduced on the surface of SnO2 CQD films can also act as a catalytic promoter by forming p-CuO/n-SnO2 heterojunctions, thereby enhancing the H2S-sensing performance at relatively low temperatures.

Stretchable gas sensors that accommodate the shape and motion characteristics of human body are indispensable to a wearable or attachable smart sensing system. However, these gas sensors usually have poor response and recovery kinetics when operated at room temperature, and especially suffer from humidity interference and mechanical robustness issues. Here, we demonstrate the first fully stretchable gas sensors which are operated at room temperature with enhanced stability against humidity. We created a crumpled quantum dot (QD) sensing layer on elastomeric substrate with flexible graphene as electrodes. Through the control over the prestrain of the flexible substrate, we achieved a 5.8 times improvement in NO2 response at room temperature with desirable stretchability even under 1000 stretch/relax cycles mechanism deformation. The uniformly wavy structural configuration of the crumpled QD gas-sensing layer enabled an improvement in the antihumidity interference. The sensor response shows a minor vibratio...

Graphitic carbon nitride (g-C3N4) is a promising material for photocatalytic water splitting but suffers from the self-agglomeration and fast recombination of photogenerated electron–hole pairs. Tin oxide (SnO2) has a high electron extraction ability and can play a key role in the charge separation and transfer dynamics of composites. Herein, we report a 0D/2D heterostructure of carbon-encapsulated SnO2 quantum dots (SnO2@C QDs) anchored on g-C3N4 nanosheets (SnO2@C/CN). The construction of interface between SnO2@C and g-C3N4 dramatically increases the surface area and the number of active sites for photocatalytic hydrogen evolution reaction (HER) and provides a driving force for efficient charge separation/transfer kinetics. The carbon layer encapsulating SnO2 QDs acts as a bridge that facilitates electron transfer from g-C3N4 to SnO2 QDs. The champion SnO2@C/CN achieves an exceptional HER rate of 2,544.3 μmol g−1 h−1 (with 3 wt% Pt) with an apparent quantum efficiency of 9.63 % (λ = 420 nm) and excellent photostability. A photoactivity enhancement mechanism is proposed based on the interfacial energy band alignment. This work provides insights into the designing of heterostructured photocatalysts of enhanced charge separation via interface engineering.

The computational screening of single‐atom alloys (SAAs) is challenging owing to their complicated electronic structure compared with traditional metal or alloy catalysts and the consequent lack of an accurate and low‐cost activity descriptor to replace expensive adsorption energy calculations. Herein, a data‐driven approach involving consecutive classification and regression is explored to identify the descriptor in the form of algebraic operators representing atomic information for predicting the adsorption energies of CO2 molecules on SAAs. The best descriptor significantly outperforms the d‐band center model in terms of accuracy and computational overhead. This study provides a fundamental understanding of the bonding strength on SAA surfaces and also an effective approach for the high‐throughput screening of promising catalysts.

Low dimensional nanomaterials have emerged as candidates for gas sensors owing to their unique size-dependent properties. In this paper, Bi2S3 nanobelts were synthesized via a facile solvothermal process and spin-coated onto alumina substrates at room temperature. The conductometric devices can even sensitively response to the relatively low concentrations of NO2 at room temperature, and their sensing performance can be effectively enhanced by the ligand exchange treatment with inorganic salts. The Pb(NO3)2-treated device exhibited superior sensing performance of 58.8 under 5ppm NO2 at room-temperature, with the response and recovery time of 28 and 106s. The competitive adsorption of NO2 against O2 on Bi2S3 nanobelts, with the enhancement both in gas adsorption and charge transfer caused by the porous network of the very thin Bi2S3 nanobelts, can be a reasonable explanation for the improved performance at room temperature. Their sensitive room-temperature response behaviors combined with the excellent solution processability, made Bi2S3 nanobelts very attractive for the construction of low-cost gas sensors with lower power consumption.

SnO 2 quantum wire/reduced graphene oxide nanocomposites (SnO 2 QW/rGO) were synthesized by a facile one-step hydrothermal method with rGO and SnCl 4 . 5H 2 O as the precursors. The SnO 2 QW/rGO nanocomposites well-dispersed in ethanol were spin-coated onto ceramics substrates to construct chemiresistive gas sensors. The H 2 S-sensing isotherm curves were obtained based on the real-time response curves of the SnO 2 QW/rGO gas sensors when operated at different temperatures ranging from 30 °C to 70 °C, from which the adsorbing/sensing performance of the specific materials were extracted and the kinetic parameters (such as response rate constant k and activation energy E a ) of the sensing-materials were quantitatively modeled. The H 2 S-sensing mechanism was found to follow Langmuir isotherm and pseudo-first-order model. Compared to pure SnO 2 QW sensors, the SnO 2 QW/rGO gas sensors exhibited higher sensitivity and faster response rate toward H 2 S, which was attributed to its lower activation energy. The SnO 2 QW/rGO gas sensors can even detect H 2 S at room temperature, highly attractive for the detection of H 2 S detection with lower power consumption.

Colloidal quantum dots (CQDs), due to the excellent solution processability, low cost and ease of integration, are emerging as promising candidates for gas sensors. Here, colloidal zinc oxide quantum dots were synthesized through a simple colloidal method with oleic acid (OA) as the surface-capping ligand. The well dispersed ZnO CQDs were spin-coated onto ceramic substrates to construct chemiresistive gas sensors. The film-level ligand exchange treatment was applied to remove the long-chain surface-capping which thereby facilitated efficient gas adsorption and carrier transport. The optimal sensor exhibited the best performance with a response of 113.5 upon 50 ppm of H2S exposure at room temperature, with a fast response time being 16s. The obtained results suggested that ZnO CQDs are promising materials candidate for high-performance gas sensors.

Tungsten oxide (WO3) has been intensely investigated as an important gas-sensing materials. Here the sensitive and selective H2S gas sensors based on WO3 quantum dots that were synthesized via a colloidal process using WCl6 ethanol solutions mixed with oleic acid and oleylamine were demonstrated. The solution processability of the colloidal WO3 quantum dots enabled a room-temperature sensor fabrication without high-temperature sintering. The optimal sensor response toward 50 ppm of H2S is 57 with the response time of 47 s, and the response was fully recoverable upon H2S release at 80 °C. The sensing mechanism was discussed by using first-principles calculation based on density functional theory (DFT). Their highly sensitive and fast response at lower operating temperature, combined with the benefit of ease fabrication, make the colloidal tungsten oxide quantum dots highly attractive for the construction of low cost gas sensor with reduced power consumption.

For the first time, herein this work, we have developed an effective and adaptable method to introduce defects onto the polymeric carbon nitride by simply grinding urea with urea nitrate which resulting new carbon nitride composite (UNU-C3N4) and melamine with urea nitrate which resulting new carbon nitride composite (UNM-C3N4). The UNU-C3N4 reveals high performance towards photocatalytic hydrogen production and as well as photocatalytic removal of contaminants. The results confirm that the defects enhanced the specific surface area, and improved performance of adsorbed oxygen which beneficial to generate more active radicals and more conducive sties to improve d the overall photocatalytic performance. The high N, H, and O content-enhanced electron polarization effects, by introducing the additional N, H, and O atoms into the g-C3N4 matrix, which will increase the charge transfer rate and charge separation efficiency. At the same time, the results of ESR also expression that the new type of as-prepared carbon nitride samples exhibit abundant of hydrogen radical (H ) formation, which is also assist to improve the photocatalytic hydrogen production performance. As expected, the H2 evolution rate of UNU-C3N4(or UNM-C3N4) underneath simulated solar light irradiation is 9.93 times (13.76 times) than that of U-C3N4 (urea as raw material) (or M-C3N4 (melamine as raw material)). The high hydrogen evolution rates of UNU-C3N4 and UNM-C3N4 are 830.94 and 556.79 μmol g−1 h−1 under the visible-light irradiation, respectively. Meanwhile, the synthesized UNU-C3N4 and UNM-C3N4 material are demonstrated an efficient ability to degrade pollutants. In general, this work provides a viable way to introduce defects and hydrogen bands into the structure of carbon nitride.

While red is one of the primary colors for display applications, the investigation of visible red emitting perovskites, particularly 2D perovskites, is relatively limited. In this work, we demonstrate a single-phase Ruddlesden–Popper quasi-2D (C3H7NH3)2CsPb2I7 perovskite for red color LEDs. Through increasing the annealing temperature of (C3H7NH3)2CsPb2I7 perovskite thin films, we have successfully achieved tunable emission wavelengths from 654 to 691 nm. Equally important, for all the quasi-2D perovskite LEDs, once the annealing temperature is fixed, the emission spectrum is independent of bias voltages, which is very important for their use in lighting and displays. With the analysis of the crystallinity, morphology, and thermodynamic stability of the quasi-2D perovskite, we find that the obtained (C3H7NH3)2CsPb2I7 perovskite is a single-phase quasi-2D perovskite with only n = 2 phase. Besides, we found that the red shifting of emission wavelength is caused by the increase of perovskite crystal size while increasing the annealing temperature. Our results also show that the temperature-induced color tunability can be applied to a series of quasi-2D perovskites with different alkylammonium cations. Importantly, we find that short alkylammonium spacers offer better electrical properties for efficient current transport and high performance in LED applications. This work contributes to controlling the optoelectronic properties of quasi-2D perovskites via controlling their crystal growth as well as paves the way to realize practical lighting and display applications of perovskite LEDs.

Colloidal quantum dots (CQDs) are emerging as ideal building blocks for the development of sensitive and low-cost gas sensors because of their large surface area and solution processability. So far, the construction of CQD gas sensor is limited to lab-scale, batch-processing methods such as spin coating. Here, we demonstrated highly sensitive and fast-response CQD gas sensors through a room-temperature spray-coating route in ambient air. Unlike the spin-coating process, a porous film morphology could be achieved by the spray-coating of ultrasmall PbS CQDs (4.0 nm in diameter) on the alumina ceramic substrates, possibly explaining their superior gas-sensing performance. Moreover, we investigated the effect of the dispersing solvent on the spray-coated PbS CQD films and found that organic solvents with lower boiling point were preferred in producing porous and sensitive CQD films. The response of the optimal gas sensors toward 50 ppm of NO2 at room temperature was high as 44, with response and recovery times short as 1 s and 28 s, respectively. The enhanced gas-sensing capabilities, coupled with the promise for mass production at room temperature, make the spray-coated CQD gas sensors very attractive in the future gas-sensing technology.

Solution-processed nanocrystalline semiconductors possess ease of processing, physical flexibility and large area coverage, which are beneficial for developing high performance gas sensors with low cost. Here we reported low-temperature chemiresistive gas sensors based on SnO 2 nanowires that were synthesized and dispersed in solution. The gas sensors were fabricated via a room-temperature spin-coating route and their sensing properties toward H 2 S at low temperatures were investigated. The sensors exhibited sensitive response upon H 2 S gas exposure at 70 °C with fast response (2 s at 50 ppm) and the response was fully recoverable upon H 2 S gas release. The highly sensitive response, combined with the benefit of room-temperature fabrication, make the solution-processed SnO 2 nanowires attractive candidates for the construction of gas sensors.

Metal oxide/graphene nanocomposites are emerging as one of the promising candidate materials for developing high-performance gas sensors. Here, we demonstrate sensitive room-temperature H2S gas sensors based on SnO2 quantum wires that are anchored on reduced graphene oxide (rGO) nanosheets. Using a one-step colloidal synthesis strategy, the morphology-related quantum confinement of SnO2 can be well-controlled by tuning the reaction time, because of the steric hindrance effect of rGO. The as-synthesized SnO2 quantum wire/rGO nanocomposites are spin-coated onto ceramics substrates without further sintering to construct chemiresistive gas sensors. The optimal sensor response toward 50 ppm of H2S is 33 in 2 s, and it is fully reversible upon H2S release at 22 °C. In addition to the excellent gas adsorption of ultrathin SnO2 quantum wires, the superior sensing performance of SnO2 quantum wire/rGO nanocomposites can be attributed to the enhanced electron transport resulting from the favorable charge transfer of...

Solution-processed SnO2 colloidal quantum dots (CQDs) have emerged as an important new class of gas-sensing materials due to their potential for low-cost and high-throughput fabrication. Here we employed the design strategy based on the synergetic effect from highly sensitive SnO2 CQDs and excellent conductive properties of multiwalled carbon nanotubes (MWCNTs) to overcome the transport barrier in CQD gas sensors. The attachment and coverage of SnO2 CQDs on the MWCNT surfaces were achieved by simply mixing the presynthesized SnO2 CQDs and MWCNTs at room temperature. Compared to the pristine SnO2 CQDs, the sensor based on SnO2 quantum dot/MWCNT nanocomposites exhibited a higher response upon exposure to H2S, and the response toward 50 ppm of H2S at 70 °C was 108 with the response and recovery time being 23 and 44 s. Because of the favorable energy band alignment, the MWCNTs can serve as the acceptor of the electrons that are injected from H2S into SnO2 quantum dots in addition to the charge transport highway to direct the electron flow to the electrode, thereby enhancing the sensor response. Our research results open an easy pathway for developing highly sensitive and low-cost gas sensors.

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning (ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm: (i) data acquisition → (ii) feature engineering → (iii) algorithm → (iv) ML model → (v) model evaluation → (vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’: (i) structure and composition → (ii) property → (iii) synthesis → (iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.

We present a general machine-learning-based approach to solve the inverse design problem of depth-graded multilayer structures (so-called supermirrors) for x-ray optics. Our model uses Monte Carlo tree search (MCTS) with policy gradient in combination with a reflectivity simulation. MCTS is an iterative design method that showed competitive efficiency in materials design and discovery problems. A policy gradient algorithm with a neural network was added to optimize the tree expansion. The policy gradient is a reinforcement learning method that optimizes parametrized policies toward an expected return using gradient descent. This approach is applied to design a depth-graded multilayer structure that maximizes mean reflectivity in an angular range for Cu K α radiation by selecting the optimal thickness and material for each layer in the structure. Mean reflectivity of 0.80 was achieved in an angular range of 0.45–0.55 °. Alternating materials are selected from a predetermined set of materials. We confirmed that the policy gradient enhances the efficiency of MCTS. This approach can be applied autonomously on several x-ray applications without any parameter tuning or pre-available data.

In this contribution, polythiophene (PTh)/Bi4O5I2 nanocomposites are successfully synthesized through a facile route at room temperature, and evaluated as photocatalysts for the photocatalytic degradation of bisphenol a (BPA). It was discovered that the introduction of polythiophene (PTh) obviously enhanced the separation efficiency of photocharge carriers, thus promote the photocatalytic ability of Bi4O5I2. Among the as-prepared PTh/Bi4O5I2 nanocomposites, 0.5% PTh/Bi4O5I2 exhibited the optimal photocatalytic activity, the BPA degradation rate was 99.2% in 30 min. The reaction rate is about 3.7 times as high as that of pure Bi4O5I2. Moreover, the degradation products of BPA were analyzed by high performance liquid chromatography. The good stability of the composite was discovered by cyclic experiments. Finally, a possible photocatalytic mechanism was proposed.

In this study, a simple procedure for the determination of λ-cyhalothrin was reported. CdTe quantum dots (QDs) capped by molecularly imprinted polymers (MIPs) were prepared and characterized by spectrofluorometer, Fourier transform infrared spectroscopy (FTIR), transmission electron microscope (TEM) and scanning electron microscope (SEM). Mercaptosuccinic acid (MSA) was chosen as a stabilizer for CdTe QDs synthesis. The MSA stabilizer which comprises both thioglycolic acid (TGA)-like and 3-mercaptopropionic acid (MPA)-like moieties could accelerate the whole growth process of CdTe QDs comparing with TGA-like or MPA-like stabilizer. Meanwhile, the spectrofluorometer was used to evaluate the optical stability, effect of pH, and selective and sensitive determination of λ-cyhalothrin (LC). Moreover, LC could quench the fluorescence of the molecularly imprinted silica nanospheres (CdTe@SiO 2 @MIPs) in a concentration-dependent manner, which was best described by a Stern–Volmer-type equation.

A metal–organic frameworks (MOFs) based on copper-benzenetricarboxylates was applied to the adsorption of methylene blue (MB) from aqueous solution. Cu-BTC [BTC = 1,3,5-benzenetricarboxylate] also known as HKUST-1 is a widely studied MOF. Cu-BTC mainly possessed mesopores, high surface area and big pore volume which is benefit for the adsorption capacity. Characterization of Cu-BTC were achieved by XRD, SEM micrographs, nitrogen adsorption–desorption analysis and FT-IR spectra. The kinetics characteristic and thermodynamic parameters were also analyzed. The experimental isotherms data were analyzed using Langmuir and Freundlich isotherm equations and the results indicated that the Langmuir isotherm showed a better fit for MB adsorption. Thermodynamic parameters were calculated by the Gibbs free energy function, confirming that the adsorption process was spontaneous and accompanied by exothermic. The maximum removal has been achieved at the pH = 7.0. The possible mechanism and the adsorption behavior of the adsorption of MB onto Cu-BTC were investigated. The adsorbent Cu-BTC could be easily regenerated after washing with ethanol. The experimental results suggested that Cu-BTC materials have potential application for the wastewater treatment containing MB dye.

Porous silica nanospheres were fabricated using a facile surface-protected etching strategy. Polyvinylpyrrolidone (PVP) was used as a protecting polymer adsorbed on the surface of silica nanospheres and NaOH was employed as an etching agent. Owing to the protective action of PVP and inhomogeneous etching, mesopores were created in the silica nanospheres. Then, based on a simple and economical wet-chemical route, highly luminescent YVO4:Eu3+ nanocrystals were then integrated onto the nanospheres to form a highly luminescent mSiO2/YVO4:Eu3+ composite material. This material, which combined the mesoporous structure of SiO2 and the strong red luminescence property of YVO4:Eu3+, could be used as a novel optosensing system. The structure, morphology, porosity, and optical properties of the materials were well characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, N2 adsorption/desorption, and photoluminescence spectra, and the optical stability and effect of pH on mSiO2/YVO4:Eu3+ were also evaluated. It was observed that λ-cyhalothrin (LC) could quench the luminescence of mSiO2/YVO4:Eu3+ in a concentration-dependent manner, which was best described by a Stern–Volmer-type equation. This study therefore demonstrated that the composite material could be potentially used as a probe to monitor the residues of LC in water.

Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision, hearing, touch, smell, and taste. A gas sensor array (GSA), also known as electronic nose, is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules. Artificial intelligence (AI) applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA. A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring, gas leakage detection, food and beverage production and storage, and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability, low-power-consumption and ease-of-operation. It is exciting to envisage robots equipped with a "nose" acting as family doctor who will guard every family member's health and keep their home safe. In this review, we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems, exploring their potential applications in disease diagnosis, environmental monitoring, and explosive detection. We also discuss the key limitations of gas sensor units and their possible solutions. Finally, we present the outlook of GSAs over the horizon of smart homes and cities.

A novel Co(II)-imprinted polymer (Co(II)-IIP) aimed at the selective adsorption and separation of Co(II) was prepared by modifying the surface of ordered mesoporous silica SBA-15 with triglycine (Gly–Gly–Gly) in the presence of Co(II). The prepared polymer was characterized by FT-IR, SEM, TEM and N2 adsorption–desorption techniques. The results showed that the synthesized polymer maintained high ordered mesoporous structure. The factors affecting the separation and preconcentration of the target Co(II), including solution pH, temperature, time for adsorption equilibrium, and conditions of elution were investigated in detail. The kinetics adsorption and adsorption capacity of Co(II)-IIP were estimated using batch mode experiments. The mechanism for static adsorption of Co(II) onto Co(II)-IIP was found to follow pseudo-second-order and Freundlich isotherm model. Compared with the non-imprinted polymer (NIP), the Co(II)-IIP exhibited faster adsorption rate and higher adsorption capacity. The adsorption equilibrium for Co(II)-IIP could be reached in 50 min, and the maximum static adsorption capacity obtained at 25 °C was 181.67 mg g−1. Furthermore, the Co(II)-IIP showed excellent recognition and binding affinity toward the target cobalt ions, and the relative selectivity coefficient (k′) values of Co(II)/Fe(II), Co(II)/Cu(II), Co(II)/Zn(II) and Co(II)/Ni(II) were 19.64, 18.29, 22.03 and 27.43, respectively. Desorption and regeneration studies suggested that the prepared Co(II)-IIP could be reused five times without an obvious decrease in adsorption capacity. The results showed that this novel adsorbent could be employed as an effective material for the selective removal of Co(II) from aqueous solutions.