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Selective conversion of methane (CH 4 ) into value-added chemicals represents a grand challenge for the efficient utilization of rising hydrocarbon sources. We report here dimeric copper centers supported on graphitic carbon nitride (denoted as Cu 2 @C 3 N 4 ) as advanced catalysts for CH 4 partial oxidation. The copper-dimer catalysts demonstrate high selectivity for partial oxidation of methane under both thermo- and photocatalytic reaction conditions, with hydrogen peroxide (H 2 O 2 ) and oxygen (O 2 ) being used as the oxidizer, respectively. In particular, the photocatalytic oxidation of CH 4 with O 2 achieves >10% conversion, and >98% selectivity toward methyl oxygenates and a mass-specific activity of 1399.3 mmol g Cu −1 h −1 . Mechanistic studies reveal that the high reactivity of Cu 2 @C 3 N 4 can be ascribed to symphonic mechanisms among the bridging oxygen, the two copper sites and the semiconducting C 3 N 4 substrate, which do not only facilitate the heterolytic scission of C-H bond, but also promotes H 2 O 2 and O 2 activation in thermo- and photocatalysis, respectively. Selective conversion of methane into value-added chemicals is a promising approach for utilization of hydrocarbon sources. Here the authors develop dimeric copper centers supported on graphitic carbon nitride (denoted as Cu 2 @C 3 N 4 ) with >10% conversion and >98% selectivity toward methyl oxygenates in both thermo- and photo- catalytic reactions.

Metallic nanoparticles have been used to harvest energy from a light source and transfer it to adsorbed gas molecules, which results in a reduced chemical reaction temperature. However, most reported reactions, such as ethylene epoxidation, ammonia decomposition and H–D bond formation are exothermic, and only H–D bond formation has been achieved at room temperature. These reactions require low activation energies (<2 eV), which are readily attained using visible-frequency localized surface plasmons (from ~1.75 eV to ~3.1 eV). Here, we show that endothermic reactions that require higher activation energy (>3.1 eV) can be initiated at room temperature by using localized surface plasmons in the deep-UV range. As an example, by leveraging simultaneous excitation of multiple localized surface plasmon modes of Al nanoparticles by using high-energy electrons, we initiate the reduction of CO 2 to CO by carbon at room temperature. We employ an environmental transmission electron microscope to excite and characterize Al localized surface plasmon resonances, and simultaneously measure the spatial distribution of carbon gasification near the nanoparticles in a CO 2 environment. This approach opens a path towards exploring other industrially relevant chemical processes that are initiated by plasmonic fields at room temperature. Metallic nanoparticles used to harvest energy from a light source typically result in reduced chemical reaction temperature. Endothermic reactions requiring higher activation energy can now be initiated at room temperature using localized surface plasmons in the deep-UV range.

To improve the detection performance of epileptic electroencephalogram (EEG) signals and address their non-stationary characteristics,this paper compares the combined effects of continuous wavelet transform (CWT) and short-time Fourier transform (STFT) with three neural network models—EEGNet,AlexNet,and Shallow ConvNet—and incorporates targeted optimization designs. Specifically,Focal Loss,dynamic data augmentation,and an early stopping mechanism are introduced in the training phase to enhance model robustness. For EEGNet,optimizations are implemented by integrating a Squeeze-and-Excitation (SE) attention module,improving depthwise separable convolution,and dynamically adapting dimensions to reduce classification errors. For Shallow ConvNet,improvements include layered convolution for extracting “time-frequency” features and average pooling to adapt to long-duration data blocks. Experiments are conducted based on subject-independent validation,and the results show that the CWT-based feature extraction method outperforms STFT comprehensively. Among all combinations,the CWT+Shallow ConvNet pair exhibits the optimal overall performance,while the CWT+EEGNet combination follows closely with excellent precision. These findings verify the effectiveness of combining precise time-frequency features (extracted by CWT) with optimized neural network models,providing reliable technical support for clinical epileptic EEG signal detection.

Historic buildings exhibit coupled response characteristics during long-term service, characterized by slowly varying global inclination evolution superimposed with local component-level deformation. Meanwhile, multi-source measurements are susceptible to environmental noise and structural non-integrality, which poses challenges to obtaining stable and physically interpretable inclination measurements. To address these issues, this study proposes a multi-source fusion monitoring method for global inclination and local deformation of historic buildings using an extended Kalman filter with fractional-order state modeling (FEKF). A state-space model incorporating global inclination, local component-level additional deformation, and their projection relationships is established, in which global inclination information derived from Global Navigation Satellite System (GNSS) and local observations obtained from inclinometers are formulated within a unified measurement framework. Fractional-order dynamics are introduced into the state evolution model to represent the long-memory and non-stationary characteristics of structural responses in historic buildings. By adopting a finite-memory approximation, the fractional-order model is embedded into the extended Kalman filtering framework, enabling joint estimation and physical decoupling of multi-source measurements. Numerical simulation results demonstrate that the proposed method can stably separate global inclination and local deformation components under noisy conditions, while improving the stability of global inclination estimation. Further validation using measured data from a historic building shows that the fusion results effectively suppress high-frequency disturbances in GNSS measurements and allow reliable reconstruction of local component-level inclination responses, indicating good stability and practical applicability. These results demonstrate that the proposed approach provides a physically consistent and robust solution for long-term posture and deformation monitoring of historic buildings.

Recent reports of hot-electron-induced dissociation of small molecules, such as hydrogen, demonstrate the potential application of plasmonic nanostructures for harvesting light to initiate catalytic reactions. Theories have assumed that plasmonic catalysis is mediated by the energy transfer from nanoparticles to adsorbed molecules during the dephasing of localized surface plasmon (LSP) modes optically excited on plasmonic nanoparticles. However, LSP-induced chemical processes have not been resolved at a sub-nanoparticle scale to identify the active sites responsible for the energy transfer. Here, we exploit the LSP resonance excited by electron beam on gold nanoparticles to drive CO disproportionation at room temperature in an environmental scanning transmission electron microscope. Using in situ electron energy-loss spectroscopy with a combination of density functional theory and electromagnetic boundary element method calculations, we show at the subparticle level that the active sites on gold nanoparticles are where preferred gas adsorption sites and the locations of maximum LSP electric field amplitude (resonance antinodes) superimpose. Our findings provide insight into plasmonic catalysis and will be valuable in designing plasmonic antennas for low-temperature catalytic processes.Plasmonic catalysis is believed to be mediated by energy transfer from nanoparticles to adsorbed molecules. Localized surface plasmon resonance on gold nanoparticles excited by electron beam is shown to drive site-selective CO disproportionation at room temperature.

Algara-Siller et al. reported the observation of a new phase of water- 'square ice'-sandwiched between two graphene layers at room temperature. Their key evidence consists of transmission electron microscope (TEM) images of a square lattice from small encapsulated crystals, the detection of oxygen from relatively large regions containing such crystals and molecular dynamics (MD) simulations indicating 'square ice' formation inside hydrophobic nanochannels.

Environmental heat-to-electric energy conversion presents a promising solution for powering sensors in wearable and portable devices. However, the availability of near-room temperature thermoelectric (TE) materials is highly limited, posing a significant challenge in this field. Bi2Se3, as a room-temperature TE material, has attracted much attention. Here, we demonstrate a large-scale synthesis of Bi2Se3 nanoflakes used for the microflexible TE generator. A high-performance micro-TE generator module, utilizing a flexible printed circuit, has been designed and fabricated through the process of screen printing. The TE generator configuration comprises five pairs of PN TE legs. The p-type TE leg utilizes commercially available Sb2Te3 powder, while the n-type TE leg employs Bi2Se3 nanoflakes synthesized in this study. For comparative purposes, we also incorporate commercially available Bi2Se3 powder as an alternative n-type TE leg. The optimal performance of the single-layer microflexible TE generator, employing Bi2Se3 nanoflakes as the active material, is achieved when operating at a temperature differential of 109.5 K, the open-circuit voltage (VOC) is 0.11 V, the short circuit current (ISC) is 0.34 mA, and the maximum output power (PMAX) is 9.5 μW, much higher than the generator consisting of commercial Bi2Se3 powder, which is expected to provide an energy supply for flexible electronic devices.

Solid-state Li–S batteries (SSLSBs) are made of low-cost and abundant materials free of supply chain concerns. Owing to their high theoretical energy densities, they are highly desirable for electric vehicles 1 – 3 . However, the development of SSLSBs has been historically plagued by the insulating nature of sulfur 4 , 5 and the poor interfacial contacts induced by its large volume change during cycling 6 , 7 , impeding charge transfer among different solid components. Here we report an S 9.3 I molecular crystal with I 2 inserted in the crystalline sulfur structure, which shows a semiconductor-level electrical conductivity (approximately 5.9 × 10 −7  S cm −1 ) at 25 °C; an 11-order-of-magnitude increase over sulfur itself. Iodine introduces new states into the band gap of sulfur and promotes the formation of reactive polysulfides during electrochemical cycling. Further, the material features a low melting point of around 65 °C, which enables repairing of damaged interfaces due to cycling by periodical remelting of the cathode material. As a result, an Li–S 9.3 I battery demonstrates 400 stable cycles with a specific capacity retention of 87%. The design of this conductive, low-melting-point sulfur iodide material represents a substantial advancement in the chemistry of sulfur materials, and opens the door to the practical realization of SSLSBs. A conductive, low-melting-point and healable sulfur iodide material aids the practical realization of solid-state Li–S batteries, which have high theoretical energy densities and show potential in next-generation battery chemistry.

Grain boundaries can markedly affect the electronic, thermal, mechanical and optical properties of a polycrystalline graphene. While in many applications the presence of grain boundaries in graphene is undesired, here we show that they have an ideal structure for the detection of chemical analytes. We observe that an isolated graphene grain boundary has ~300 times higher sensitivity to the adsorbed gas molecules than a single-crystalline graphene grain. Our electronic structure and transport modelling reveal that the ultra-sensitivity in grain boundaries is caused by a synergetic combination of gas molecules accumulation at the grain boundary, together with the existence of a sharp onset energy in the transmission spectrum of its conduction channels. The discovered sensing platform opens up new pathways for the design of nanometre-scale highly sensitive chemical detectors. Grain boundaries in graphene are present between misaligned crystalline areas, and influence the resulting properties, often in a negative fashion. Here, the authors use these boundaries for chemical detection, observing markedly higher sensitivities as compared with single-crystalline domains.