Gigahertz (GHz) femtosecond (fs) lasers have opened possibilities for enhancing and controlling the laser machining quality to engineer the physicochemical properties of materials. However, fundamental understanding of laser-material interactions by GHz fs laser has remained unsolved due to the complexity of associated ablation dynamics. Here, we study the ablation dynamics of copper (Cu) by GHz fs bursts using in situ multimodal diagnostics, time-resolved scattering imaging, emission imaging, and emission spectroscopy. A combination of probing techniques reveals that GHz fs bursts rapidly remove molten Cu from the irradiated spot due to the recoil pressure exerted by following fs pulses. Material ejection essentially stops right after the burst irradiation due to the limited amount of remnant matter, combined with the suppressed heat conduction into the target material. Our work provides insights into the complex ablation mechanisms incurred by GHz fs bursts, which are critical in selecting optimal laser conditions in cross-cutting processing, micro/nano-fabrication, and spectroscopy applications.

This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

The bark of hardwood trees contains abundant polyphenols, which can rapidly transform into a graphite layer that acts as a thermal barrier, minimizing fire damage. Inspired by this natural fire resistance mechanism, we developed an eco-friendly, cost-effective fire-retardant coating system for forest fire prevention. Comprising only pyrogallol (PG) and polyethyleneimine (PEI), the system forms a polyphenolic layer through oxygen-mediated oxidative crosslinking when exposed to air. This method uses water as the sole solvent and requires no additional catalysts, allowing easy, material-independent application via spray-coating. Heat resistance tests showed that the PG–PEI coating improved the wood's inherent fire resistance by approximately threefold, attributed to the rapid coating conversion into a graphite layer at high temperatures, as confirmed by X-ray photoelectron and Raman spectroscopies. Furthermore, a 70-day colorimetric analysis under simulated weathering conditions exposure demonstrated the coating's durability against environmental stresses. The PG–PEI coating also preserved wood's natural functionality, supporting tree health, as evidenced by the high survival rates of the treated trees. These findings suggest the PG–PEI coating is a promising solution for mitigating forest fire damage while maintaining eco-friendliness and practicality.

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

In this work, we studied single-pulse ablation dynamics of a temporally modulated continuous wave laser–material interaction with Al using in situ multimodal time-resolved diagnostics that describe in detail the associated physical and chemical processes. Time-resolved scattering, emission imaging, and optical emission spectroscopy unveiled a sequence of events spread out across three distinct phases: (i) early phase ablation process, associated with particle generation and liquid Al column formation (<20 $\mu$ s), (ii) secondary detonation when sufficient ejected material is accumulated over the surface (20–50 $\mu$ s), and (iii) molten liquid Al pool oscillation on the surface, followed by large droplet ejection from the liquid pool (100–500 $\mu$ s). Atomic Al and AlO were observed with optical emission spectroscopy at different ratios during the entire lifetime of the event, verifying the formation of oxidized Al vapor upon its interaction with air. Morphological and compositional characterization confirmed surface oxidation and material re-solidification in the form of protrusions produced during the irradiation process. This work provides insights into the complex physical and chemical mechanisms of single-pulse ablation in the sub-millisecond laser pulse regime, which are critically important for parameter optimization in a variety of laser processing, microfabrication, and deposition applications.