Tuning the Local Environment of Pt Species at CNT@MO2-x (M = Sn and Ce) Heterointerfaces for Boosted Alkaline Hydrogen Evolution.

As the most promising hydrogen evolution reaction (HER) electrocatalysts, platinum (Pt)-based catalysts still struggle with sluggish kinetics and expensive costs in alkaline media. Herein, we accelerate the alkaline hydrogen evolution kinetics by optimizing the local environment of Pt species and metal oxide heterointerfaces. The well-dispersed PtRu bimetallic clusters with adjacent MO2-x (M = Sn and Ce) on carbon nanotubes (PtRu/CNT@MO2-x) are demonstrated to be a potential electrocatalyst for alkaline HER, exhibiting an overpotential of only 75 mV at 100 mA cm-2 in 1 M KOH. The excellent mass activity of 12.3 mA µg-1Pt+Ru and specific activity of 32.0 mA cm-2ECSA at an overpotential of 70 mV are 56 and 64 times higher than those of commercial Pt/C. Experimental and theoretical investigations reveal that the heterointerfaces between Pt clusters and MO2-x can simultaneously promote H2O adsorption and activation, while the modification with Ru further optimizes H adsorption and H2O dissociation energy barriers. Then, the matching kinetics between the accelerated elementary steps achieved superb hydrogen generation in alkaline media. This work provides new insight into catalytic local environment design to simultaneously optimize the elementary steps for obtaining ideal alkaline HER performance.

A Hybrid Quantum Image-Matching Algorithm.

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.

Multienzyme-Mediated Dual-Channel Magnetic Relaxation Switching Taste Biosensor (D-MRSTB) for Simultaneous Detection of Umami Compounds and Synergistic Enhancement in Food.

Umami substances play a significant role in the evaluation of food quality, and their synergistic enhancement is of great importance in improving and intensifying food flavors and tastes. Current biosensors available for umami detection still confront challenges in simultaneous quantification of multiple umami substances and umami intensities. In this study, an innovative dual-channel magnetic relaxation switching taste biosensor (D-MRSTB) was developed for the quantitative detection of representative umami substances. The multienzyme signal of D-MRSTB specifically catalyzes the umami substances of interest to generate hydrogen peroxide (H2O2), which is then used to oxidate Fe2+ to Fe3+. Such a valence-state transition of paramagnetic ions was utilized as a magnetic relaxation signaling switch to influence the transverse magnetic relaxation time (T2) within the reaction milieu, thus achieving simultaneous detection of monosodium glutamate (MSG) and inosine 5'-monophosphate (IMP). The biosensor showed good linearity (R2 > 0.99) in the concentration range of 50-1000 and 10-1000 µmol/L, with limits of detection (LOD) of 0.61 and 0.09 µmol/L for MSG and IMP, respectively. Furthermore, the biosensor accurately characterized the synergistic effect of the mixed solution of IMP and MSG, where ΔT2 showed a good linear relationship with the equivalent umami concentration (EUC) of the mixed solution (R2 = 0.998). Moreover, the D-MRSTB successfully achieved the quantitative detection of umami compounds in real samples. This sensing technology provides a powerful tool for achieving the detection of synergistic enhancement among umami compounds and demonstrates its potential for application in the food industry.

A general quantum algorithm for numerical integration.

Quantum algorithms have shown their superiority in many application fields. However, a general quantum algorithm for numerical integration, an indispensable tool for processing sophisticated science and engineering issues, is still missing. Here, we first proposed a quantum integration algorithm suitable for any continuous functions that can be approximated by polynomials. More impressively, the algorithm achieves quantum encoding of any integrable functions through polynomial approximation, then constructs a quantum oracle to mark the number of points in the integration area and finally converts the statistical results into the phase angle in the amplitude of the superposition state. The quantum algorithm introduced in this work exhibits quadratic acceleration over the classical integration algorithms by reducing computational complexity from O(N) to O(√N). Our work addresses the crucial impediments for improving the generality of quantum integration algorithm, which provides a meaningful guidance for expanding the superiority of quantum computing.

Piezo-photocatalytic reduction of nitrates to N2 over silver dispersed on BaTiO3@TiO2.

This communication first achieved piezo-photocatalytic reduction of nitrates to N2 through designing an Ag2O/BaTiO3@TiO2 core-shell catalyst. The built-in electric field induced by piezoelectric polarization suppresses photoexcited carrier recombination, and simultaneously causes energy band tilting, leading to the generation of electrons with higher reducibility to directly trigger the NO3- reduction to ˙NO32-, even without hole scavengers.

Low-Cytotoxic Core-Sheath ZnO NWs@TiO2-xNy Triggered Piezo-photocatalytic Antibacterial Activity.

Sonophotodynamic antimicrobial therapy (SPDAT) is recognized as a highly efficient biomedical treatment option, known for its versatility and remarkable healing outcomes. Nevertheless, there is a scarcity of sonophotosensitizers that demonstrate both low cytotoxicity and exceptional antibacterial effectiveness in clinical applications. In this paper, a novel ZnO nanowires (NWs)@TiO2-xNy core-sheath composite was developed, which integrates the piezoelectric effect and heterojunction to build dual built-in electric fields. Remarkably, it showed superb antibacterial effectiveness (achieving 95% within 60 min against S. aureus and ∼100% within 40 min against E. coli, respectively) when exposed to visible light and ultrasound. Due to the continuous interference caused by light and ultrasound, the material's electrostatic equilibrium gets disrupted. The modification in electrical properties facilitates the composite's ability to attract bacterial cells through electrostatic forces. Moreover, Zn-O-Ti and Zn-N-Ti bonds formed at the interface of ZnO NWs@TiO2-xNy, further enhancing the dual internal electric fields to accelerate the excited carrier separation to generate more reactive oxygen species (ROS), and thereby boosting the antimicrobial performance. In addition, the TiO2 layer limited Zn2+ dissolution into solution, leading to good biocompatibility and low cytotoxicity. Lastly, we suggest a mechanistic model to offer practical direction for the future development of antibacterial agents that are both low in toxicity and high in efficacy. In comparison to the traditional photodynamic therapy systems, ZnO NWs@TiO2-xNy composites exhibit super piezo-photocatalytic antibacterial activity with low toxicity, which shows great potential for clinical application as an antibacterial nanomaterial.

Room-temperature activation of the C-H bond in the dehydrogenation of ethane over a Cu/TiO2 catalyst.

A novel photocatalytic system of Cu/TiO2 for activation the C-H bond in the dehydrogenation of ethane to ethylene at room temperature is proposed. The optimized 1%-Cu/TiO2 catalyst achieved C2H6 conversion of 1.70%, C2H4 selectivity of 98.41%, and exhibited excellent stability. The active site Cuδ+ showed high dispersion on the TiO2 surface. Theoretical calculations and in situ diffuse reflectance infrared Fourier transform spectroscopy revealed a reaction mechanism: C2H6 is first activated by adsorption over the Cu4C/TiO2 catalyst with elongation of the C-H bond, attacked by h+/˙OH to form ethyl radicals, which are then converted to C2H4.

Development of sensitive and stable electrochemical impedimetric biosensor based on T1R1 receptor and its application to detection of umami substances.

Umami substances can provide a palatable flavour for food. In this study, an electrochemical impedimetric biosensor was developed for detecting umami substances. This biosensor was fabricated by immobilising T1R1 onto AuNPs/reduced graphene oxide/chitosan which was in advance electro-deposited onto a glassy carbon electrode. The evaluation by the electrochemical impedance spectrum method showed that the T1R1 biosensor performed well with low detection limits and wide linear ranges. Under the optimised incubation time (60 s), the electrochemical response was linearly related to the concentrations of the detected targets monosodium glutamate and inosine-5'-monophosphate within their respective linear range of 10-14 to 10-9 M and 10-16 to 10-13 M. The low detection limit of monosodium glutamate and inosine-5'-monophosphate was 10-15 M and 10-16 M, respectively. Moreover, the T1R1 biosensor exhibited high specificity to umami substances even in the real food sample. The developed biosensor still retained 89.24% signal intensity after 6-day storage, exhibiting a desirable storability.

Highly selective hydrogenation of dimethyl oxalate to methyl glycolate and ethylene glycol over an amino-assisted Ru-based catalyst.

A Ru/NH2-MCM-41 catalyst was prepared via a coordination-assisted strategy for chemoselective hydrogenation of dimethyl oxalate with a high selectivity of methyl glycolate (ca. 100%) and ethylene glycol (>90%) at reaction temperatures of 343 K and 433 K, respectively. The amino groups help to anchor and form stable electron-rich Ru active sites, which accounts for the excellent CO bond activation and hydrogenation selectivity.

Kinetics of a new porcine taste-bud tissue biosensor for the detection of umami substances and their synergistic effect.

Umami substances are nutrients to humans, and their synergistic effect is associated with food acceptance. In this study, a new biosensor was developed to detect umami substances, their synergistic effect, and detection kinetics. Porcine taste-bud tissues were used as the sensing element, and the umami substance signals were characterized using an electrochemical workstation. The responses of taste-bud tissue sensors to monosodium L-glutamate (MSG) were compared based on different tongue sites. The interaction law between MSG and receptors in the taste-bud tissues of the three sensors conforms to enzymatic-reaction kinetics, where rectangular hyperbola curves in the Michaelis-Menten equation were followed with fitting coefficients (>0.91). However, the taste-bud sensors respond differently to MSG stimuli, with those based on a tip and mediolateral tongue, producing the lowest detection limit of 10-16 mol/L. The number of receptors required for a single cell to achieve maximum output signal is 3.68, 30.42, and 7.27, respectively. Moreover, the taste-bud tissue sensors identified the synergistic effect of umami substances. In addition, they were sensitive to umami variations in soy sauce and mandarin fish. The developed porcine taste-bud tissue biosensor revealed the interaction law between umami substances and receptors, providing a new idea for umami evaluation.

In-Situ Fabricating V2O5/TiO2-Carbon Heterojunction from Ti3C2 MXene as Highly Active Visible-Light Photocatalyst.

Titanium dioxide is a mainstream photocatalyst, but it still confronts great obstacles of poor visible light absorption and rapid recombination rate of photogenerated carriers. Herein, we describe the design of a highly active visible-light photocatalytic system of graphited carbon layers anchored V2O5/TiO2 heterojunctions derived from Ti3C2 MXene, which demonstrates about 4.58 and 2.79 times higher degradation activity of MB under visible light (λ > 420 nm) than pure V2O5 and TiO2-carbon. Combined with the characterization results, the formed V2O5/TiO2 heterojunction promotes the separation of photogenerated carriers, while the graphitized carbon derived from MXene acts as an electronic reservoir to enhance the absorption of visible light. The ESR results show that superoxide radicals and hydroxyl radicals are the main active species in the reaction system. Therefore, we propose a possible mechanism model to provide a feasible idea for the subsequent design of high-efficiency photocatalysts for environmental treatment.

A new method of software vulnerability detection based on a quantum neural network.

In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.