XPS and HR TEM Elucidation of the Diversity of Titania-Supported Single-Site Ir Catalyst Performance in Spin-Selective Propene Hydrogenation.

Immobilized [Ir(COD)Cl]2-Linker/TiO2 catalysts with linkers containing Py, P(Ph)2 and N(CH3)2 functional groups were prepared. The catalysts were tested via propene hydrogenation with parahydrogen in a temperature range from 40 °C to 120 °C which was monitored via NMR. The catalytic behavior of [Ir(COD)Cl]2-Linker/TiO2 is explained on the basis of quantitative and qualitative XPS data analysis performed for the catalysts before and after the reaction at 120 °C. It is shown that the temperature dependence of propene conversion and the enhancement of the NMR signal are explained via a combination of the stabilities of both the linker and immobilized [Ir(COD)Cl]2 complex. It is demonstrated that the N(CH3)2-linker is the most stable at the surface of TiO2 under used reaction conditions. As a result, only this sample shows a rise in the enhancement of the NMR signal in the 100-120 °C temperature range.

DLgram cloud service for deep-learning analysis of microscopy images.

To analyze images in various fields of science and technology, it is often necessary to count observed objects and determine their parameters. This can be quite labor-intensive and time-consuming. This article presents DLgram, a universal, user-friendly cloud service that is developed for this purpose. It is based on deep learning technologies and does not require programming skills. The user labels several objects in the image and uploads it to the cloud where the neural network is trained to recognize the objects being studied. The user receives recognition results, which if necessary, can be corrected, errors removed, or missing objects added. In addition, it is possible to carry out mathematical processing of the data obtained to get information about the sizes, areas, and coordinates of the observed objects. The article describes the service features and discusses examples of its application. The DLgram service allows to reduce significantly the time spent on quantitative image analysis, reduce subjective factor influence, and increase the accuracy of analysis. RESEARCH HIGHLIGHTS: DLgram automatically recognizes and counts the number of objects in images and their parameters. DLgram is a universal service, which was created on the basis of the latest deep learning developments and does not require programming skills.

Synthesis of Chlorine- and Nitrogen-Containing Carbon Nanofibers for Water Purification from Chloroaromatic Compounds.

Chlorine- and nitrogen-containing carbon nanofibers (CNFs) were obtained by combined catalytic pyrolysis of trichloroethylene (C2HCl3) and acetonitrile (CH3CN). Their efficiency in the adsorption of 1,2-dichlorobenzene (1,2-DCB) from water has been studied. The synthesis of CNFs was carried out over self-dispersing nickel catalyst at 600 °C. The produced CNFs possess a well-defined segmented structure, high specific surface area (~300 m2/g) and high porosity (0.5-0.7 cm3/g). The addition of CH3CN into the reaction mixture allows the introduction of nitrogen into the CNF structure and increases the volume of mesopores. As a result, the capacity of CNF towards adsorption of 1,2-DCB from its aqueous solution increased from 0.41 to 0.57 cm3/g. Regardless of the presence of N, the CNF samples exhibited a degree of 1,2-DCB adsorption from water-organic emulsion exceeding 90%. The adsorption process was shown to be well described by the Dubinin-Astakhov equation. The regeneration of the used CNF adsorbent through liquid-phase hydrodechlorination was also investigated. For this purpose, Pd nanoparticles (1.5 wt%) were deposited on the CNF surface to form the adsorbent with catalytic function. The presence of palladium was found to have a slight effect on the adsorption capacity of CNF. Further regeneration of the adsorbent-catalyst via hydrodechlorination of adsorbed 1,2-DCB was completed within 1 h with 100% conversion. The repeated use of regenerated adsorbent-catalysts for purification of solutions after the first cycle of adsorption ensures almost complete removal of 1,2-DCB.

Effect of Pretreatment with Acids on the N-Functionalization of Carbon Nanofibers Using Melamine.

Nowadays, N-functionalized carbon nanomaterials attract a growing interest. The use of melamine as a functionalizing agent looks prospective from environmental and cost points of view. Moreover, the melamine molecule contains a high amount of nitrogen with an atomic ratio C/N of 1/2. In present work, the initial carbon nanofibers (CNFs) were synthesized via catalytic pyrolysis of ethylene over microdispersed Ni-Cu alloy. The CNF materials were pretreated with 12% hydrochloric acid or with a mixture of concentrated nitric and sulfuric acids, which allowed etching of the metals from the fibers and oxidizing of the fibers' surface. Finally, the CNFs were N-functionalized via their impregnation with a melamine solution and thermolysis in an inert atmosphere. According to the microscopic data, the initial structure of the CNFs remained the same after the pretreatment and post-functionalization procedures. At the same time, the surface of the N-functionalized CNFs became more defective. The textural properties of the materials were also affected. In the case of the oxidative treatment with a mixture of acids, the highest content of the surface oxygen of 11.8% was registered by X-ray photoelectron spectroscopy. The amount of nitrogen introduced during the post-functionalization of CNFs with melamine increased from 1.4 to 4.3%. Along with this, the surface oxygen concentration diminished to 6.4%.

Dataset N-doping of alumina thin film support of catalysts.

The search for the ways of thermal stabilization of supported metal catalysts is an important challenge in the modern catalysis. Chemical modification of support seems to be the most versatile approach to stabilize the metal particles against sintering and alter their catalytic performance. Also for such modification nitrogen doping can be used and is considered rather perspective. In a recent manuscript (A.M. Dmitrachkov, R.I. Kvon, A.V. Nartova, N-doping of alumina thin film support to improve the thermal stability of catalysts: preparation and investigation, Appl. Surf. Sci.) we have developed the procedure of N-doping of alumina thin film grown at the surface of metal substrate. Proposed N-doped model alumina support is suitable for catalysis - oriented surface science studies and improves the resistance of supported metal particles against thermal driven sintering. Herein, we provide useful complementary data for the characterization of the prepared materials in the form of: in situ / ex situ XPS (X-ray photoelectron spectroscopy) spectra at every stage of sample preparation, including angle resolved XPS experiments and thermal stability tests; STM (scanning tunneling microscopy) images of supported gold catalysts. Presented data support the proposed mechanism of film formation and modification.

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning.

Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87-0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service "ParticlesNN" based on the trained neural network, which can be used by any researcher in the world.

Palladium nanoparticle formation on TiO2(110) by thermal decomposition of palladium(II) hexafluoroacetylacetonate.

Palladium nanoparticles were synthesized by thermal decomposition of palladium(II) hexafluoroacetylacetonate (Pd(hfac)2), an atomic layer deposition (ALD) precursor, on a TiO2(110) surface. According to X-ray photoelectron spectroscopy (XPS), Pd(hfac)2 adsorbs on TiO2(110) dissociatively yielding Pd(hfac)(ads), hfac(ads), and adsorbed fragments of the hfac ligand at 300 K. A (2 × 1) surface overlayer was observed by scanning tunneling microscopy (STM), indicating that hfac adsorbs in a bidentate bridging fashion across two Ti 5-fold atoms and Pd(hfac) adsorbs between two bridging oxygen atoms on the surface. Annealing of the Pd(hfac)(ads) and hfac(ads) species at 525 K decomposed the adsorbed hfac ligands, leaving PdO-like species and/or Pd atoms or clusters. Above 575 K, the XPS Pd 3d peaks shift toward lower binding energies and Pd nanoparticles are observed by STM. These observations point to the sintering of Pd atoms and clusters to Pd nanoparticles. The average height of the Pd nanoparticles was 1.2 ± 0.6 nm at 575 K and increased to 1.7 ± 0.5 nm following annealing at 875 K. The Pd coverage was estimated from XPS and STM data to be 0.05 and 0.03 monolayers (ML), respectively, after the first adsorption/decomposition cycle. The amount of palladium deposited on the TiO2(110) surface increased linearly with the number of adsorption/decomposition cycles with a growth rate of 0.05 ML or 0.6 Å per cycle. We suggest that the removal of the hfac ligand and fragments eliminates the nucleation inhibition of Pd nanoparticles previously observed for the Pd(hfac)2 precursor on TiO2.