Ni-Nanocluster Modified Black TiO2 with Dual Active Sites for Selective Photocatalytic CO2 Reduction.

One of the key challenges in artificial photosynthesis is to design a photocatalyst that can bind and activate the CO2 molecule with the smallest possible activation energy and produce selective hydrocarbon products. In this contribution, a combined experimental and computational study on Ni-nanocluster loaded black TiO2 (Ni/TiO2[Vo] ) with built-in dual active sites for selective photocatalytic CO2 conversion is reported. The findings reveal that the synergistic effects of deliberately induced Ni nanoclusters and oxygen vacancies provide (1) energetically stable CO2 binding sites with the lowest activation energy (0.08 eV), (2) highly reactive sites, (3) a fast electron transfer pathway, and (4) enhanced light harvesting by lowering the bandgap. The Ni/TiO2[Vo] photocatalyst has demonstrated highly selective and enhanced photocatalytic activity of more than 18 times higher solar fuel production than the commercial TiO2 (P-25). An insight into the mechanisms of interfacial charge transfer and product formation is explored.

Edge promoted ultrasensitive electrochemical detection of organic bio-molecules on epitaxial graphene nanowalls.

We report the simultaneous electrochemical detection of dopamine (DA), uric acid (UA) and ascorbic acid (AA) on three dimensional (3D) unmodified 'as-grown' epitaxial graphene nanowall arrays (EGNWs). The 3D few layer EGNWs, unlike the 2D planar graphene, offers an abundance of vertically oriented nano-graphitic-edges that exhibit fast electron-transfer kinetics and high electroactive surface area to geometrical area (EAA/GA≈134%), as evident from the Fe(CN)6(3-/4-) redox kinetic study. The hexagonal sp(2)-C domains, on the basal plane of the EGNWs, facilitate efficient adsorption via spontaneous π-π interaction with the aromatic rings in DA and UA. Such affinity together with the fast electron kinetics enables simultaneous and unambiguous identification of individual AA, DA and UA from their mixture. The unique edge dominant EGNWs result in an unprecedented low limit of detection (experimental) of 0.033 nM and highest sensitivity of 476.2 µA/µM/cm(2), for UA, which are orders of magnitude higher than comparable existing reports. A reaction kinetics based modeling of the edge-oriented 3D EGNW system is proposed to illustrate the superior electro-activity for bio-sensing applications.

Cobalt-phosphate-assisted photoelectrochemical water oxidation by arrays of molybdenum-doped zinc oxide nanorods.

We report the first demonstration of cobalt phosphate (Co-Pi)-assisted molybdenum-doped zinc oxide nanorods (Zn(1-x)Mo(x)O NRs) as visible-light-sensitive photofunctional electrodes to fundamentally improve the performance of ZnO NRs for photoelectrochemical (PEC) water splitting. A maximum photoconversion efficiency as high as 1.05% was achieved, at a photocurrent density of 1.4 mA cm(-2). More importantly, in addition to achieve the maximum incident photon to current conversion efficiency (IPCE) value of 86%, it could be noted that the IPCE of Zn(1-x)Mo(x)O photoanodes under monochromatic illumination (450 nm) is up to 12%. Our PEC performances are comparable to those of many oxide-based photoanodes in recent reports. The improvement in photoactivity of PEC water splitting may be attributed to the enhanced visible-light absorption, increased charge-carrier densities, and improved interfacial charge-transfer kinetics due to the combined effect of molybdenum incorporation and Co-Pi modification, contributing to photocatalysis. The new design of constructing highly photoactive Co-Pi-assisted Zn(1-x)Mo(x)O photoanodes enriches knowledge on doping and advances the development of high-efficiency photoelectrodes in the solar-hydrogen field.

Growth of ß-Ga2O3 and GaN nanowires on GaN for photoelectrochemical hydrogen generation.

Enhanced photoelectrochemical (PEC) performances of Ga(2)O(3) and GaN nanowires (NWs) grown in situ from GaN were demonstrated. The PEC conversion efficiencies of Ga(2)O(3) and GaN NWs have been shown to be 0.906% and 1.09% respectively, in contrast to their 0.581% GaN thin film counterpart under similar experimental conditions. A low crystallinity buffer layer between the grown NWs and the substrate was found to be detrimental to the PEC performance, but the layer can be avoided at suitable growth conditions. A band bending at the surface of the GaN NWs generates an electric field that drives the photogenerated electrons and holes away from each other, preventing recombination, and was found to be responsible for the enhanced PEC performance. The enhanced PEC efficiency of the Ga(2)O(3) NWs is aided by the optical absorption through a defect band centered 3.3 eV above the valence band of Ga(2)O(3). These findings are believed to have opened up possibilities for enabling visible absorption, either by tailoring ion doping into wide bandgap Ga(2)O(3) NWs, or by incorporation of indium to form InGaN NWs.

Imaging layer number and stacking order through formulating Raman fingerprints obtained from hexagonal single crystals of few layer graphene.

Quantitative mapping of layer number and stacking order for CVD-grown graphene layers is realized by formulating Raman fingerprints obtained on two stepwise stacked graphene single-crystal domains with AB Bernal and turbostratic stacking (with ~30°interlayer rotation), respectively. The integrated peak area ratio of the G band to the Si band, A(G)/A(Si), is proven to be a good fingerprint for layer number determination, while the area ratio of the 2D and G bands, A(2D)/A(G), is shown to differentiate effectively between the two different stacking orders. The two fingerprints are well formulated and resolve, quantitatively, the layer number and stacking type of various graphene domains that used to rely on tedious transmission electron microscopy for structural analysis. The approach is also noticeable in easy discrimination of the turbostratic graphene region (~30° rotation), the structure of which resembles the well known high-mobility graphene R30/R2(±) fault pairs found on the vacuum-annealed C-face SiC and suggests an electron mobility reaching 14,700 cm(3) V(-1) s(-1). The methodology may shed light on monitoring and control of high-quality graphene growth, and thereby facilitate future mass production of potential high-speed graphene applications.

Deep and alignment free patterned etching of GaN surface using an atomic force microscope.

Successful deep and alignment-free patterned etching on GaN using atomic force microscope (AFM) local oxidation followed by in-situ chemical etching is demonstrated. Oxide ridges are grown on GaN on an AFM by applying positive sample bias at 80% humidity, with the oxidation reaction expedited by UV light. The oxide ridges are then etched by HCl solution, leaving troughs in the GaN surface. A dripping strategy for the in-situ chemical etching is recommended that allows deep, alignment-free multiple AFM oxidation/etching works on the GaN surface without any need of substrate removal from the AFM platform. Repeated etching followed by AFM oxidation on a spot on a GaN surface resulting in a hole as deep as 800 nm was also demonstrated. Further, a preliminary evaluation of the porosity of the AFM-grown oxide indicates that the oxide ridges grown on GaN at an AFM cantilever moving speed of 300 nm/s are porous in structure, with an estimated porosity of 86%, which porosity could be reduced if longer resident time of the AFM cantilever on the target oxidation region was used.

The preparation of silver nanoparticle decorated silica nanowires on fused quartz as reusable versatile nanostructured surface-enhanced Raman scattering substrates.

We introduce a platform, comprised of silver nanoparticle decorated silica nanowires (SiONWs) dispersed on fused quartz substrates, for high sensitivity surface-enhanced Raman scattering (SERS) measurements using both frontal (through the analytes) and back-face (through the transparent substrate) excitation. Quasi-quantitative SERS performances on the specialized substrate, vis-à-vis a silver deposited bare fused quartz plate, showed: (i) the suitability of the Ag modified SiONW substrate for frontal as well as back-face excitation; (ii) a wider detection range with high sensitivity to Rhodamine 6G; and (iii) good underwater metal-oxide adhesion of the specialized substrates. Capable of surviving ultrasonic cleaning, the substrate introduced is one of the few reusable low-cost Ag-based nanostructured SERS substrates, requiring only a simple silver reload process (the silver mirror reaction).

Direct patterning of zinc oxide with control of reflected color through nano-oxidation using an atomic force microscope.

Atomic force microscope oxidation on Zn creating amorphous ZnO (a-ZnO) with the a-ZnO showing multiple colors under white light at different oxidation voltages was successfully demonstrated. Simulation of reflected colors at different thicknesses of a-ZnO was also conducted. The presented technique can not only be applied to near diffraction limit multilevel optical data storage, but also makes it possible to represent the color spectra observed in nature at near diffraction limits. It can also be used for device fabrication in situations exploiting the semiconductor nature of ZnO.

In-TFT-array-process micro defect inspection using nonlinear principal component analysis.

Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI). Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection in TFT array process is therefore considered a difficult task. This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. More importantly, the inspection time is less than 1 s per input image.