BACKGROUND Glycerol, a by‐product of biodiesel production, is produced in large quantities, exceeding its demand. The saturation of glycerol resulted in a sharp reduction in its market value and the surplus waste may pose a risk to the environment. By means of electrochemical technologies, glycerol could be oxidized into value‐added products such as glycerate, tartronate and lactate. In the present work, carbon‐supported NiBi catalysts with different atomic ratios (NixBi1−x/C, wherex = 100, 95, 90 and 50 at%) were fabricated and utilized in a 25 cm2 electrolysis cell. RESULTS The as‐fabricated catalysts were characterized and analyzed by various physicochemical and electrochemical characterizations. Using a three‐electrode electrochemical cell, Ni95Bi5/C showed the highest current density of 104 mA cm−2, with an onset potential of 1.32 V versus a reversible hydrogen electrode. Long‐term chronoamperometry was performed in a glycerol electrolysis cell accompanied by the product analysis using high‐performance liquid chromatography. It was found that Ni95Bi5/C had higher selectivity to glycerate C3 product compared to Ni/C. Additionally, optimizing experimental conditions (applied potential, residence time and temperature) to achieve higher selectivity to C3 products was thoroughly studied. The selectivity to C3 value‐added products was enhanced by adjusting the operating conditions. CONCLUSION Small addition of bismuth to Ni/C enhanced both catalytic activity and selectivity to C3 products. The main products formed on NixBi1−x/C were formate and glycerate, while the secondary products were glycolate, tartronate, oxalate and lactate. By running electrolysis under optimal conditions, the selectivity to C3 products was significantly enhanced. © 2022 Society of Chemical Industry (SCI).

The surface plasmon response of a cross-sectional segment of a wrinkled gold film is studied using electron energy loss spectroscopy (EELS). EELS data demonstrate that wrinkled gold structures act as a suitable substrate for surface plasmons to propagate. The intense surface variations in these structures facilitate the resonance of a wide range of surface plasmons, leading to the broadband surface plasmon response of these geometries from the near-infrared to visible wavelengths. The metallic nanoparticle boundary element method toolbox is used to simulate plasmon eigenmodes in these structures. Eigenmode simulations show how the diverse morphology of the wrinkled structure leads to its high spectral complexity. Micron-sized structural features that do not provide interactions between segments of the wrinkle have only a small effect on the surface plasmon resonance response, whereas nanofeatures strongly affect the resonant modes of the geometry. According to eigenmode calculations, different eigenenergy shifts around the sharp folds contribute to the broadband response and infrared activity of these structures; these geometrical features also support higher energy (shorter wavelength) symmetric and anti-symmetric plasmon coupling across the two sides of the folds. It is also shown that additional plasmon eigenstates are introduced from hybridization of modes across nanogaps between structural features in close proximity to each other. All of these factors contribute to the broadband response of the wrinkled gold structures.

The energy resolution in hyperspectral imaging techniques has always been an important matter in data interpretation. In many cases, spectral information is distorted by elements such as instruments’ broad optical transfer function, and electronic high frequency noises. In the past decades, advances in artificial intelligence methods have provided robust tools to better study sophisticated system artifacts in spectral data and take steps towards removing these artifacts from the experimentally obtained data. This study evaluates the capability of a recently developed deep convolutional neural network script, EELSpecNet, in restoring the reality of a spectral data. The particular strength of the deep neural networks is to remove multiple instrumental artifacts such as random energy jitters of the source, signal convolution by the optical transfer function and high frequency noise at once using a single training data set. Here, EELSpecNet performance in reducing noise, and restoring the original reality of the spectra is evaluated for near zero-loss electron energy loss spectroscopy signals in Scanning Transmission Electron Microscopy. EELSpecNet demonstrates to be more efficient and more robust than the currently widely used Bayesian statistical method, even in harsh conditions (e.g. high signal broadening, intense high frequency noise).

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