Surgical excision (Mohs micrographic surgery) is the standard procedure to treat a melanoma, in which an in situ histologic examination of sectioned skin is carried out repeatedly until no cancer cells are detected. The possibility to identify melanoma from the surrounding skin by femtosecond laser-induced breakdown spectroscopy (fs-LIBS) is investigated. For experiments, melanoma induced on a hairless mouse by injection of B16/F10 murine melanoma cell was sampled in the form of frozen tissue sections as in Mohs surgery and analyzed by fs-LIBS (λ ¼ 1030 nm, τ ¼ 550 fs). For analysis, the magnesium signal normalized by carbon intensity was utilized to construct an intensity map around the cancer, including both melanoma and surrounding dermis. The intensity map showed a close match to the optically observed morphological and histological features near the cancer region. The results showed that when incorporated into the existing micrographic surgery procedure, fs-LIBS could be a useful tool for histopathologic interpretation of skin cancer possibly with significant reduction of histologic examination time.

In this study, efficient spectral line selection and weighted-averaging-based processing schemes are proposed for the classification of laser-induced breakdown spectroscopy (LIBS) measurements. For fast on-line classification, a set of representative spectral lines are selected and processed relying on the information metric, instead of the time consuming full spectrum based analysis. The most informative spectral line sets are investigated by the joint mutual information estimation (MIE) evaluated with the Gaussian kernel density, where dominant intensity peaks associated with the concentrated components are not necessarily most valuable for classification. In order to further distinguish the characteristic patterns of the LIBS measured spectrum, two-dimensional spectral images are synthesized through column-wise concatenation of the peaks along with their neighbors. For fast classification while preserving the effect of distinctive peak patterns, column-wise Gaussian weighted averaging is applied to the synthesized images, yielding a favorable trade-off between classification performance and computational complexity. To explore the applicability of the proposed schemes, two applications of alloy classification and skin cancer detection are investigated with the multi-class and binary support vector machines classifiers, respectively. The MIE measures associated with selected spectral lines in both applications show a strong correlation to the actual classification or detection accuracy, which enables to find out meaningful combinations of spectral lines. In addition, the peak patterns of the selected lines and their Gaussian weighted averaging with neighbors of the selected peaks efficiently distinguish different classes of LIBS measured spectrum.

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We report continuous-wave (CW) operation of an InAs/InGaAsP quantum-dot (QD) heterostructure laser diode (LD) with an output power of 1.1 W at room temperature. This is the first observation on the laser output over 1 W from InAs QD-LDs fabricated on InP substrates. Also, the high-power lasing emission was successfully achieved at up to 60 °C. The improvement in the lasing characteristics can be attributed to enhancement in modal gain of QD-LDs because of the increase in spatial carrier confinement around localized QD states by using a dot-in-a-well structure and a thin GaAs strain-modulating layer.

This work reports that the intensity ratio of spectral lines having similar self-absorption characteristics during laser induced breakdown spectroscopy (LIBS) analysis can become nearly constant over a wide range of irradiation conditions if the intensities are integrated over a sufficiently long time. It is shown that the plasma temperature and intensity ratio of these spectral lines have temporal similarity. The spectral lines with similar self-absorption properties may be selected to improve the accuracy and consistency of LIBS analysis results under an environment with fluctuating measurement conditions.