In high-temperature testing scenarios that rely on contact, fine-wire thermocouples demonstrate commendable dynamic performance. Nonetheless, their thermal inertia leads to notable dynamic nonlinear inaccuracies, including response delays and amplitude reduction. To mitigate these challenges, a novel dynamic error correction approach is introduced, which combines a Continuous Restricted Boltzmann Machine, Deep Belief Network, and Physics-Informed Neural Network (CDBN-PINN). The unique heat transfer properties of the thermocouple’s bimetallic structure are represented through an Inverse Heat Conduction Equation (IHCP). An analysis is conducted to explore the connection between the analytical solution’s ill-posed nature and the thermocouple’s dynamic errors. The transient temperature response’s nonlinear characteristics are captured using CRBM-DBN. To maintain physical validity and minimize noise amplification, filtered kernel regularization is applied as a constraint within the PINN framework. This approach was tested and confirmed through laser pulse calibration on thermocouples with butt-welded and ball-welded configurations of 0.25 mm and 0.38 mm. Findings reveal that the proposed method achieved a peak relative error of merely 0.83%, superior to Tikhonov regularization by −2.2%, Wiener deconvolution by 20.40%, FBPINNs by 1.40%, and the ablation technique by 2.05%. In detonation tests, the corrected temperature peak reached 1045.7 °C, with the relative error decreasing from 77.7% to 5.1%. Additionally, this method improves response times, with the rise time in laser calibration enhanced by up to 31 ms and in explosion testing by 26 ms. By merging physical constraints with data-driven methodologies, this technique successfully corrected dynamic errors even with limited sample sizes.

In high-temperature testing scenarios that rely on contact, fine-wire thermocouples demonstrate commendable dynamic performance. Nonetheless, their thermal inertia leads to notable dynamic nonlinear inaccuracies, including response delays and amplitude reduction. To mitigate these challenges, a novel dynamic error correction approach is introduced, which combines a Continuous Restricted Boltzmann Machine, Deep Belief Network, and Physics-Informed Neural Network (CDBN-PINN). The unique heat transfer properties of the thermocouple's bimetallic structure are represented through an Inverse Heat Conduction Equation (IHCP). An analysis is conducted to explore the connection between the analytical solution's ill-posed nature and the thermocouple's dynamic errors. The transient temperature response's nonlinear characteristics are captured using CRBM-DBN. To maintain physical validity and minimize noise amplification, filtered kernel regularization is applied as a constraint within the PINN framework. This approach was tested and confirmed through laser pulse calibration on thermocouples with butt-welded and ball-welded configurations of 0.25mm and 0.38mm. Findings reveal that the proposed method achieved a peak relative error of merely 0.83%, surpassing the 2.05% of the ablation technique and the 2.2% of Tikhonov regularization. In detonation tests, the corrected temperature peak reached 1045.7°C, with the relative error decreasing from 77.6% to 4.9%, and the rise time enhanced by 26 milliseconds. By merging physical constraints with data-driven methodologies, this technique successfully corrected dynamic errors even with limited sample sizes.

ZnO is widely used as an electron transport layer in quantum-dot light-emitting diodes (QLEDs) because of its high electron mobility and suitable energy levels. However, rapid electron transport may cause excessive electron injection, leading to charge accumulation and parasitic recombination. SnO₂ provides weaker electron transport, but using it alone limits device performance. Here, a SnO₂/ZnO bilayer electron transport layer was introduced to regulate electron injection. Unlike previously reported structures in which SnO₂ directly contacts the QDs, the present configuration places SnO₂ on ITO and ZnO adjacent to the QD layer. The low-concentration ZnO overlayer reduced the RMS roughness of the SnO₂ film from 1.79 to 1.07 nm and facilitated electron injection, while the underlying SnO₂ layer moderated the electron supply. The bilayer also suppressed leakage current and showed the lowest capacitance peak, consistent with improved charge balance. The bilayer QLED achieved a maximum current efficiency of 13.40 cd/A and a maximum luminance of 33,210 cd/m². Its current efficiency was 100% and 32.7% higher than those of the SnO₂ and ZnO devices, respectively. These results demonstrate that the bilayer improves charge balance through facilitated electron injection and controlled electron supply.

A novel multi-nested anti-resonant fiber for reducing propagation losses in the terahertz band was proposed. The transmission properties of the fibers were analyzed using the finite element method. High-resistivity silicon was used as the fiber material, and the confinement loss of the fiber was reduced by employing eight circumferentially symmetric multi-nested ice-cream-shaped tubes. By optimizing the structural parameters of cladding tubes, the transmission loss of the fiber was effectively reduced. Simulation results show that the total loss of the designed fiber was 1.53 × 10⁻⁴ dB/m. The low loss transmission bandwidth (less than 10^–3 dB/m) was 0.26 THz in the 0.6–1.4 THz range. The proposed fiber offered a new possibility for low-loss communication in the terahertz band, demonstrating huge commercial potential.