Stoichiometry-Controlled Synthesis of Nanoparticulate Mixed-Metal Oxyhydroxide Oxygen Evolving Catalysts by Electrochemistry in Aqueous Nanodroplets

Mixed-metal oxyhydroxides—especially those of Ni and Fe—are one of the most active classes of materials known for catalyzing the oxygen evolution reaction (OER). Here, nanoparticulate mixed metal oxyhydroxides (of Ni, Fe, and Co) were prepared on an electrode surface by electrochemical reaction of a precursor solution encapsulated in aqueous nanodroplets (AnDs), with each of the droplets containing 10 s of attoliters of fluid. Electrode reactions and synthesis can be monitored in situ by electrochemistry as single AnD stochastically lands and interacts with the working electrode. Resultant metal oxyhydroxide nanoparticles can be size and composition controlled precisely by modulating the precursor solution stored in the AnD. Nanoparticulate metal oxyhydroxides were implemented as catalysts for the OER and exhibited superior catalysis compared to their thin-film counterparts, demonstrating a hundred-thousand-fold enhancement in atom efficiency at comparable turnover rates.     

Activation of α-Fe2O3 for Photoelectrochemical Water Splitting Strongly Enhanced by Low Temperature Annealing in Low Oxygen Containing Ambient

Photoelectrochemical (PEC) water splitting is a promising method for the conversion of solar energy into chemical energy stored in the form of hydrogen. Nanostructured hematite (α-Fe₂O₃) is one of the most attractive materials for a highly efficient charge carrier generation and collection due to its large specific surface area and the short minority carrier diffusion length. In the present work, the PEC water splitting performance of nanostructured α-Fe₂O₃ is investigated which was prepared by anodization followed by annealing in a low oxygen ambient (0.03 % O₂ in Ar). It was found that low oxygen annealing can activate a significant PEC response of α-Fe₂O₃ even at a low temperature of 400 °C and provide an excellent PEC performance compared with classic air annealing. The photocurrent of the α-Fe₂O₃ annealed in the low oxygen at 1.5 V vs. RHE results as 0.5 mA cm⁻², being 20 times higher than that of annealing in air. The obtained results show that the α-Fe₂O₃ annealed in low oxygen contains beneficial defects and promotes the transport of holes; it can be attributed to the improvement of conductivity due to the introduction of suitable oxygen vacancies in the α-Fe₂O₃. Additionally, we demonstrate the photocurrent of α-Fe₂O₃ annealed in low oxygen ambient can be further enhanced by Zn-Co LDH, which is a co-catalyst of oxygen evolution reaction. This indicates low oxygen annealing generates a promising method to obtain an excellent PEC water splitting performance from α-Fe₂O₃ photoanodes.     

Hydrodechlorination of chlorophenols over Pd catalysts supported on zeolite Y,MCM-41 and graphene

Hydrodechlorination (HDC) reaction of chlorophenols was carried out using Pd catalysts supported over zeolite Y, MCM-41 or graphene. Pd-MCM-41 and Pd-Y zeolite were prepared by impregnation and ion-exchange method, respectively. Pd-graphene (Pd-G) was prepared by hydrazine hydrate reduction of palladium ion dispersed on graphene oxide. The catalysts were characterized by several analytical tools such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). These catalysts were subjected to HDC reaction of chlorophenols, such as 4-chlorophenol (4-CP), 2,4-dichlorophenol (2,4-DCP), 2,6-dichlorophenol (2,6-DCP) and 3,4-dichlorophenol (3,4-DCP). The reaction rate of HDC of chlorophenols catalyzed by Pd catalysts with various solid bases, such as KF/Al₂O₃ (alumina), sodium acetate (NaOAc) and K₂CO₃ was compared. First, Pd-MCM-41 and Pd-Y catalysts were compared. 2,4- and 3,4-DCPs were completely decomposed within 6 h, in the case of Pd-MCM-41 with NaOAc. Using Pd-Y instead of Pd-MCM-41 with NaOAc, much faster decomposition was observed. Faster decomposition of 4-CP and DCPs was observed with NaOAc base than K₂CO₃ or KF/Al₂O₃ under the same condition. In the case of Pd-Y with KF/Al₂O₃, slower decomposition of 4-CP and DCPs was observed. These base effects were interpreted using the solubility of NaCl and KCl in alcohol and the basic sites of KF/Al₂O₃. Because the solubility of NaCl is known to be larger than KCl solubility in alcohol, byproduct NaCl could be easily dissolved and ionized in solvents. For Pd-Y with KF/Al₂O₃, the small pore size of Y zeolite can interfere with the diffusion of HCl to KF/Al₂O₃ basic site. Second, three catalysts, including Pd-graphene, were compared. 2,4-DCP was decomposed within 2 h using Pd-G with either K₂CO₃, NaOAc or KF/Al₂O₃. Pd-G catalyst showed the highest catalytic activity among Pd-G, Pd-MCM-41 and Pd-Y catalysts. The high activity and stability of the Pd-G could be attributed to the strong metal–support interaction with an electron-deficient site and a critical Pd particle size (ca. 3.5 nm) of Pd-G nanocatalyst with a stronger resistance to the deactivation and good affinity toward aromatic organic molecules, especially phenols. The progress of HDC reaction was monitored by gas chromatography with flame ionization detection (GC/FID), and a feasible degradation process could be explained by analyzing the degradation products such as phenol, cyclohexanone and cyclohexanol from resulting GC chromatograms. The effect of reaction temperature on HDC in Pd-G catalyst was also discussed. In conclusion, Pd-G is an efficient catalyst for decomposition of chlorophenols and can be applied to remediation of chlorophenol-contaminated water under mild conditions.     

Analysis of creep due to grain-boundary diffusion in hexagonal microstructures

For steady-state deformation caused by grain-boundary diffusion in hexagonal microstructures, the stress distribution on grain boundaries and the macroscopic strain rates are analysed by taking the effects of viscous grain-boundary sliding into account. The maximum normal stress and the extent of stress concentration are shown to decrease as the grain-boundary viscosity increases. For infinite viscosity and/or extremely small grain sizes, the distribution of the normal stress becomes uniform on grain boundaries. The strain rates are predicted by both the stress analysis and the energy balance method, and the two strain rates are consistent with each other. The predicted strain rates also decrease as the grain-boundary viscosity increases. The present analysis reveals that the grain-size exponent is dependent on the grain size and the grain-boundary viscosity: the exponent becomes unity for small grain sizes and/or high viscosity, while it is three for large grain sizes and/or low viscosity. Recent experimental observations that the strain rates of nano-sized grain are much lower than those predicted by grain-boundary diffusion are explained by the increasing contribution of viscous grain-boundary sliding with decreasing grain size.     

Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem

We propose a new algorithm for sparse estimation of eigenvectors in generalized eigenvalue problems (GEPs). The GEP arises in a number of modern data-analytic situations and statistical methods, including principal component analysis (PCA), multiclass linear discriminant analysis (LDA), canonical correlation analysis (CCA), sufficient dimension reduction (SDR), and invariant co-ordinate selection. We propose to modify the standard generalized orthogonal iteration with a sparsity-inducing penalty for the eigenvectors. To achieve this goal, we generalize the equation-solving step of orthogonal iteration to a penalized convex optimization problem. The resulting algorithm, called penalized orthogonal iteration, provides accurate estimation of the true eigenspace, when it is sparse. Also proposed is a computationally more efficient alternative, which works well for PCA and LDA problems. Numerical studies reveal that the proposed algorithms are competitive, and that our tuning procedure works well. We demonstrate applications of the proposed algorithm to obtain sparse estimates for PCA, multiclass LDA, CCA, and SDR. Supplementary materials for this article are available online.     

Spliced-fiber mode filter for 1060-nm single-mode transmission

We present a novel mode filter operating for the 1060-nm band, which selectively removes the power of the high-order mode (HOM) from a conventional single-mode fiber (C-SMF) that supports two modes at the short-wavelength band. Our mode filter is fabricated very easily by fusion-splicing a short section of short-wavelength single-mode fiber in the middle of the C-SMF link. By using our spliced-fiber mode filter (SF-MF), a high HOM suppression performance of > 18 dB has been achieved with a low insertion loss of < 0.3 dB over the full band of optical communications from 1050 nm to 1650 nm. We have also developed a new mode power measurement scheme that takes advantage of the bending sensitivity of the HOM guidance. This measurement scheme enabled us to evaluate accurately the performance factors of the fabricated mode filter.     

Machine learning identification of symmetrized base states of Rydberg atoms

Studying the complex quantum dynamics of interacting many-body systems is one of the most challenging areas in modern physics. Here, we use machine learning (ML) models to identify the symmetrized base states of interacting Rydberg atoms of various atom numbers (up to six) and geometric configurations. To obtain the data set for training the ML classifiers, we generate Rydberg excitation probability profiles that simulate experimental data by utilizing Lindblad equations that incorporate laser intensities and phase noise. Then, we classify the data sets using support vector machines (SVMs) and random forest classifiers (RFCs). With these ML models, we achieve high accuracy of up to 100% for data sets containing only a few hundred samples, especially for the closed atom configurations such as the pentagonal (five atoms) and hexagonal (six atoms) systems. The results demonstrate that computationally cost-effective ML models can be used in the identification of Rydberg atom configurations.     

Novel fabrication route for carbon-free and dense CuInSe2 (CIS) thin films via a non-vacuum process for solar cell application

Carbon-free CuInSe₂ (CIS) thin film with a dense microstructure has been prepared using a novel non-vacuum based fabrication route. Cu_xS_y and In₂Se₃ binary nanoparticles, approximately 10 nm in size, were synthesized by a low temperature colloidal process. The precursor film was deposited using the coating ink formulated with the binary nanoparticles and pyridine, and then annealed in the rapid thermal annealing (RTA) chamber at 540 °C for 15 min under selenium (Se) atmosphere. Scanning electron micrographs, X-ray diffraction patterns and Raman spectra showed a phase pure carbon-free and dense CIS thin film was prepared in this method. A solar cell device fabricated using this CIS thin film showed the following photovoltaic characteristics: V_OC = 350 mV, J_SC = 24.72 mA cm⁻², FF = 38.73% and η = 3.36% under standard AM 1.5 condition.