Artificial photosynthesis relies on the availability of synthetic photocatalysts that can drive CO reduction in the presence of water and light. From the standpoint of solar fuel production, it is desirable that these photocatalysts perform under visible light and produce energy-rich hydrocarbons from CO reduction. However, the multistep nature of CO-to-hydrocarbon conversion poses a significant kinetic bottleneck when compared to CO production and H evolution. Here, we show that plasmonic Au nanoparticle photocatalysts can harvest visible light for multielectron, multiproton reduction of CO to yield C (methane) and C (ethane) hydrocarbons. The light-excitation attributes influence the C and C selectivity. The observed trends in activity and selectivity follow Poisson statistics of electron harvesting. Higher photon energies and flux favor simultaneous harvesting of more than one electron from the photocharged Au nanoparticle catalyst, inducing the C-C coupling required for C production. These findings elucidate the nature of plasmonic photocatalysis, which involves strong light-matter coupling, and set the stage for the controlled chemical bond formation by light excitation.

Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.

Photocatalytic reduction of carbon dioxide (CO) by visible light has the potential to mimic plant photosynthesis and facilitate the renewable production of storable fuels. Accomplishing desirable efficiency and selectivity in artificial photosynthesis requires an understanding of light-driven pathways on photocatalyst surfaces. Here, we probe with single-nanoparticle spatial resolution the dynamics of a plasmonic silver (Ag) photocatalyst under conditions of visible light-driven CO reduction. In situ surface-enhanced Raman spectroscopy captures discrete adsorbates and products formed dynamically on single photocatalytic nanoparticles, most prominent among which is a surface-adsorbed hydrocarboxyl (HOCO*) intermediate critical to further reduction of CO to carbon monoxide (CO) and formic acid (HCOOH). Density functional theory simulations of the captured adsorbates reveal the mechanism by which plasmonic excitation activates physisorbed CO leading to the formation of HOCO*, indicating close interplay between photoexcited states and adsorbate/metal interactions.

The understanding and rational design of heterogeneous catalysts for complex reactions, such as CO2 reduction, requires knowledge of elementary steps and chemical species prevalent on the catalyst surface under operating conditions. Using in situ nanoscale surface-enhanced Raman scattering, we probe the surface of a Ag nanoparticle during plasmon-excitation-driven CO2 reduction in water. Enabled by the high spatiotemporal resolution and surface sensitivity of our method, we detect a rich array of C1–C4 species formed on the photocatalytically active surface. The abundance of multi-carbon compounds, such as butanol, suggests the favorability of kinetically challenging C–C coupling on the photoexcited Ag surface. Another advance of this work is the use of isotope labeling in nanoscale probing, which allows confirmation that detected species are the intermediates and products of the catalytic reaction rather than spurious contaminants. The surface chemical knowledge made accessible by our approach will inform the modeling and engineering of catalysts.

This paper describes a new software-based registration and fusion of visible and thermal infrared (IR) image data for face recognition in challenging operating environments that involve illumination variations. The combined use of visible and thermal IR imaging sensors offers a viable means for improving the performance of face recognition techniques based on a single imaging modality. Despite successes in indoor access control applications, imaging in the visible spectrum demonstrates difficulties in recognizing the faces in varying illumination conditions. Thermal IR sensors measure energy radiations from the object, which is less sensitive to illumination changes, and are even operable in darkness. However, thermal images do not provide high-resolution data. Data fusion of visible and thermal images can produce face images robust to illumination variations. However, thermal face images with eyeglasses may fail to provide useful information around the eyes since glass blocks a large portion of thermal energy. In this paper, eyeglass regions are detected using an ellipse fitting method, and replaced with eye template patterns to preserve the details useful for face recognition in the fused image. Software registration of images replaces a special-purpose imaging sensor assembly and produces co-registered image pairs at a reasonable cost for large-scale deployment. Face recognition techniques using visible, thermal IR, and data-fused visiblethermal images are compared using a commercial face recognition software (FaceIt R ) and two visible-thermal face image databases (the NIST/Equinox and the UTK-IRIS databases). The proposed multiscale data-fusion technique improved the recognition accuracy under a wide range of illumination changes. Experimental results showed that the eyeglass replacement increased the number of correct first match subjects by 85% (NIST/Equinox) and 67% (UTK-IRIS).Keywords: face recognition, visible-thermal image fusion, multisensor image registration, thermal infrared imaging, eyeglass replacement, personal identification, security 216 Kong et al.