The recent discovery of spherical trihedral metallo-borospherenes Ln₃B₁₈⁻ (Ln = La, Tb) represents the onset of an unprecedented class of boron-metal binary nanomaterials, where heterogeneous La or Tb atoms, alongside B atoms, constitute integral components of the outer cage surface. Here, C_3v A₁-Sc₃B₁₆⁺ is theoretically predicted as the global minimum by the particle swarm optimization (PSO) algorithm, and its B₁₆-framework comprises unequal B₆ and B₇ triangles, connected by three B atoms between the vertexes of the B₆ triangle and the interval edges of the B₇ triangle. This arrangement forms three equivalent η⁷-B₇ rings, each centered with a Sc atom. The cage surface of the second low-lying A₂ isomer features an asymmetric triangle of B₃ and B₇, along with three B₂-linked units. This configuration gives rise to three equivalent octacoordinated Sc atoms, each centered within η⁸-B₈ rings. These two cationic isomers have been proven to be very stable in kinetics and thermodynamics. Our findings regarding unequal boron triangles and smaller odd-coordinated rings in the B-framework enrich the geometric patterns of trihedral metallo-borospherenes.

Hierarchical NiO-SnO₂ nanoflowers were prepared via a one-step hydrothermal method. The morphology, structure and components of the hierarchical NiO-SnO₂ nanoflowers were examined via scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray powder diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The ethanol gas-sensing performance was systematically analyzed between pure hierarchical SnO₂ nanoflowers and the hierarchical NiO-SnO₂ nanoflowers. The results indicated that the hierarchical NiO-SnO₂ nanoflowers showed better gas-sensing properties than the pure hierarchical SnO₂ nanoflowers at 164 °C. The enhanced gas-sensing performance was ascribed to the formation of p-n heterojunctions between p-type NiO and n-type SnO₂. Additionally, NiO has a catalytic role. Therefore, hierarchical NiO-SnO₂ nanoflowers could be a potential gas-sensing material for the fabrication of high-quality ethanol gas sensors.

This paper proposes a temperature-salt synchronous sensitisation detection technique based on data signal processing (DSP). The method first employs spectral analysis to decouple the independent interference spectra of each cavity from the combined interference spectrum of the parallel dual-cavity system. Subsequently, a virtual interferogram corresponding to each independent interferogram is constructed, and the two are superimposed to generate their respective vernier effect spectra. Finally, based on the relationship between the envelope peaks (or troughs) of the two vernier spectra and variations in temperature and salinity, a sensitivity matrix equation is established, thereby achieving highly sensitive simultaneous detection of temperature and salinity parameters. Experimental results indicate that within the temperature range of 21°C to 28°C, the temperature sensitivities of the dual-vernier spectrum are -2.0544 nm/°C and -4.9609 nm/°C, respectively, representing 8.08 times and 5.83 times the sensitivity of a single independent cavity. Within a salinity range of 20‰ to 50‰, the salinity sensitivities of the dual-vernier spectra were 1.4248 nm/‰ and 0, respectively, representing 7.98 times and 0 times the sensitivity of a single independent cavity. This method, requiring no physical interferometer and relying solely on algorithms, not only achieves simultaneous measurement of both temperature and salinity parameters but also realizes sensitivity amplification. This method offers simplicity of implementation, high sensitivity, and low cost, demonstrating promising application prospects in the field of high-sensitivity marine sensing.

In wireless sensor networks (WSNs), angle of arrival (AOA) and time difference of arrival (TDOA) localization systems relying on distributed sensor measurements degrade significantly under high-order time-varying noise. Although traditional zeroing neural networks (ZNNs) handle dynamic localization tasks, their insufficient robustness against such high-order noise often compromises convergence stability and accuracy. To address this limitation, this paper proposes a polynomial anti-noise compensation ZNN (PAC-ZNN) incorporating a polynomial anti-noise compensation (PAC) term and a logarithmic mapping activation function (LMAF). Specifically, the PAC term mitigates the adverse effects of cumulative high-order noise, while the LMAF further enhances the convergence speed and stability of the system. The global convergence and robustness of the proposed PAC-ZNN are rigorously proven based on Lyapunov stability theory. Simulation results demonstrate that when applied to AOA and TDOA-based dynamic localization tasks, the proposed PAC-ZNN outperforms traditional ZNN-based solutions in terms of anti-noise capability, convergence efficiency, and localization precision under high-order noise conditions. Furthermore, it maintains robust tracking performance even under complex multipath environments, verifying its superior performance and practical application value.