In traditional laser-induced breakdown spectroscopy (LIBS) applications, the line intensity and analysis capability are susceptible to plasma shielding. To investigate the shielding effects on the characteristics of Cu plasma in air, a ~120-picosecond laser with a wavelength of 1064 nm was employed to produce plasma. The plasma temperature and electron density were calculated under the condition of local thermal equilibrium (LTE) and optically thin, while the relationships between the line intensity, plasma temperature and electron density were analyzed. Moreover, the LTE condition was validated by the McWhirter relation, plasma relaxation time and diffusion length, and the optically thin condition was observed through the variation in line intensity. The results indicated that when the focal point was below the target surface, the plasma shielding was the weakest, and the highest line intensity could be obtained. In addition, there was a positive correlation between the increased plasma temperature and the degree of shielding effect. When the focal point was above the target surface, the high-irradiance pulse directly broke down the free air and produced a shock wave. Under the high pressure of the over-heated shock wave, the line intensity, plasma temperature and electron density increased again. This study provides an important insight into the experiments and applications of picosecond LIBS.

Terahertz metamaterial dual-band absorbers are used for multi-target detection and high-sensitivity sensing in complex environments by enhancing information that reflects differences in the measured substances. Traditional design processes are complex and time-consuming. Machine learning-based methods, such as neural networks and deep learning, require a large number of simulations to gather training samples. Existing design methods based on single-objective optimization often result in uneven multi-objective optimization, which restricts practical applications. In this study, we developed a metamaterial absorber featuring a circular split-ring resonator with four gaps nested in a “卍” structure and used the Multi-Objective Firefly Algorithm based on Multiple Cooperative Strategies to achieve fast optimization of the absorber’s structural parameters. A comparison revealed that our approach requires fewer iterations than the Multi-Objective Particle Swarm Optimization and reduces design time by nearly half. The absorber designed using this method exhibited two resonant peaks at 0.607 THz and 0.936 THz, with absorptivity exceeding 99%, indicating near-perfect absorption and quality factors of 31.42 and 30.08, respectively. Additionally, we validated the absorber’s wave-absorbing mechanism by applying impedance-matching theory. Finally, we elucidated the resonance-peak formation mechanism of the absorber based on the surface current and electric-field distribution at the resonance frequencies. These results confirmed that the proposed dual-band metamaterial absorber design is efficient, representing a significant step toward the development of metamaterial devices.

Efficient forward prediction and inverse design can bypass the complexity and computational cost of traditional full-wave simulations, accelerating the development of terahertz metamaterial absorbers. Most current machine-learning approaches for spectral prediction or inverse design are limited to absorbers with a single topology, requiring retraining whenever the topology changes. Simultaneous forward prediction and inverse design also require separate models that are trained independently. This study proposes a resonance-anchored residual surrogate framework for resonance-frequency prediction and goal-oriented inverse design across multiple absorber topologies. Unlike conventional spectral prediction, the proposed spectral representation consists of a resonance frequency and a continuous local absorption spectrum centered on the resonance peak. The residual network is trained using a physics-guided dynamic anchor loss to improve the accuracy of resonance-frequency prediction, local spectral reconstruction, peak alignment, and absorptivity estimation. On the test set, the model achieves a normalized resonance-frequency MSE of 6.64 × 10⁻⁵, a normalized spectral-waveform MSE of 3.58 × 10⁻³, and a physical-domain absorptivity RMSE of 0.0054. Ablation experiments verify the effectiveness of the residual connections and the dynamic anchor loss. The trained surrogate is then frozen and integrated into a projected gradient descent loop for inverse design. Full-wave simulations demonstrate that the optimized structures successfully reproduce the target resonance responses within the sampled design space.