The control of the physical, chemical, and electronic properties of laser-induced graphene (LIG) is crucial in the fabrication of flexible electronic devices. However, the optimization of LIG production is time-consuming and costly. Here, we demonstrate state-of-the-art automated parameter tuning techniques using Bayesian optimization to advance rapid singlestep laser patterning and structuring capabilities with a view to fabricate graphene-based electronic devices. In particular, a large search space of parameters for LIG explored efficiently. As a result, high-quality LIG patterns exhibiting high Raman G/D ratios at least a factor of four larger than those found in the literature were achieved within 50 optimization iterations in which the laser power, irradiation time, pressure and type of gas were optimized. Human-interpretable conclusions may be derived from our machine learning model to aid our understanding of the underlying mechanism for substrate-dependent LIG growth, e.g. highquality graphene patterns are obtained at low and high gas pressures for quartz and polyimide, respectively. Our Bayesian optimization search method allows for an efficient experimental design that is independent of the experience and skills of individual researchers, while reducing experimental time and cost and accelerating materials research.

The control of the physical, chemical, and electronic properties of laser-induced graphene (LIG) is crucial in the fabrication of flexible electronic devices. However, the optimization of LIG production is time-consuming and costly. Here, we demonstrate state-of-the-art automated parameter tuning techniques using Bayesian optimization to advance rapid single-step laser patterning and structuring capabilities with a view to fabricate graphene-based electronic devices. In particular, a large search space of parameters for LIG explored efficiently. As a result, high-quality LIG patterns exhibiting high Raman G/D ratios at least a factor of four larger than those found in the literature were achieved within 50 optimization iterations in which the laser power, irradiation time, pressure and type of gas were optimized. Human-interpretable conclusions may be derived from our machine learning model to aid our understanding of the underlying mechanism for substrate-dependent LIG growth, e.g. high-quality graphene patterns are obtained at low and high gas pressures for quartz and polyimide, respectively. Our Bayesian optimization search method allows for an efficient experimental design that is independent of the experience and skills of individual researchers, while reducing experimental time and cost and accelerating materials research.

Several laser systems in the infrared wavelength range, such as Nd:YAG, Er:YAG or CO 2 lasers are used for efficient ablation of bone tissue. Here the application of short pulses in coaction with a thin water film results in reduced thermal side effects. Nonetheless up to now there is no laser-process for bone cutting in a clinical environment due to lack of ablation efficiency. Investigations of laser ablation rates of bone tissue using a rinsing system and concerning bleedings have not been reported yet. In our study we investigated the ablation rates of bovine cortical bone tissue, placed 1.5 cm deep in water under laminar flow conditions, using a short pulsed (25 ps), frequency doubled (532 nm) Nd:YVO 4 laser with pulse energies of 1 mJ at 20 kHz repetition rate. The enhancement of the ablation rate due to debris removal by an additional water flow from a well-directed blast pipe as well as the negative effect of the admixture of bovine serum albumin to the water were examined. Optical Coherence Tomography (OCT) was used to measure the ablated volume. An experimental study of the depth dependence of the ablation rate confirms a simplified theoretical prediction regarding Beer-Lambert law, Fresnel reflection and a Gaussian beam profile. Conducting precise incisions with widths less than 1.5 mm the maximum ablation rate was found to be 0.2 mm 3 /s. At depths lower than 100 µm, while the maximum depth was 3.5 mm.

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In graphene oxide, the graphite lattice is intercalated with oxygen groups that bond to carbon atoms. These groups have a bearing on the possibility of using graphene oxide as a precursor to make graphene. The nature of carbon bonds in graphene oxide has been characterized with soft x‐ray reflection spectroscopy across the carbon K‐edge. Results distinguish graphene oxide synthesized with Hummers' method from that made using a method suggested by Tour. The observed spectra are consistent with those from near‐edge x‐ray absorption fine structure (NEXAFS) measurements. In particular, the expected carbon K‐edge resonances associated with excitations into molecular π*‐ and σ*‐states of CC bonds can be identified. Importantly, the greater oxidation efficiency of the method by Tour may be the reason for the observation of additional resonances that have been assigned to carbon bonding with molecular groups containing oxygen. The additional resonances have been interpreted as the excitations of carbon 1 s electrons into the carbonyl π*(CO) orbital in the molecular group –COOH and into the hydroxyl π*(COH) orbital, respectively. Copyright © 2015 John Wiley & Sons, Ltd.

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