Experimental and Computational Study Toward Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials.

SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however; it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, CO2RR performance is enhanced by supporting SnO2 nanoparticles on two common supports, vulcan carbon and TiO2. Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C. Furthermore, this study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO2 reduction and establish a correlation between SnOx structures and their CO2RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2 -, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx-based electrocatalysts for CO2RR.

Enhancing electrocatalytic activity in metallic thin films through surface segregation of carbon.

Thin layers of commonly used adhesion metals i.e., Cr and Ti were annealed to investigate and estimate their impact on the electrochemical properties of the carbon nanomaterials grown on top of them. The microstructure, surface chemistry, and electrochemical activities of these materials were evaluated and compared with those of as-deposited thin films. The results from X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, grazing incidence X-ray diffraction (GIXRD), time-of-flight elastic recoil detection analysis (TOF-ERDA), and conductive atomic force microscopy (C-AFM) indicated the formation of a catalytic graphite layer on Cr following annealing, while no such layer was formed on Ti. This is attributed to the formation of the Cr2O3 layer on annealed Cr, which acts as a barrier to carbon diffusion into the underlying Cr. Conversely, Ti exhibits a high solubility for both carbon and oxygen, preventing the formation of the graphite layer. Cyclic voltammetry results showed that annealed Cr electrodes are electrochemically active towards both dopamine (DA) and ascorbic acid (AA) while no electrochemical activity is exhibited by annealed Ti. Quantum chemical calculations suggested that the presence of carbon as graphene or an amorphous form is critical for the oxidation reaction of probes. These results are significant for comprehending how the distinct solubilities of typical interstitial solutes influence the microstructure of adhesion metal layers and consequently yield diverse electrochemical properties.

Robust method for uniform coating of carbon nanotubes with V2O5 for next-generation transparent electrodes and Li-ion batteries.

Composites comprising vanadium-pentoxide (V2O5) and single-walled carbon nanotubes (SWCNTs) are promising components for emerging applications in optoelectronics, solar cells, chemical and electrochemical sensors, etc. We propose a novel, simple, and facile approach for SWCNT covering with V2O5 by spin coating under ambient conditions. With the hydrolysis-polycondensation of the precursor (vanadyl triisopropoxide) directly on the surface of SWCNTs, the nm-thick layer of oxide is amorphous with a work function of 4.8 eV. The material recrystallizes after thermal treatment at 600 °C, achieving the work function of 5.8 eV. The key advantages of the method are that the obtained coating is uniform with a tunable thickness and does not require vacuuming or heating during processing. We demonstrate the groundbreaking results for two V2O5/SWCNT applications: transparent electrode and cathode for Li-ion batteries. As a transparent electrode, the composite shows stable sheet resistance of 160 Ω sq-1 at a 90% transmittance (550 nm) - the best performance reported for SWCNTs doped by metal oxides. As a cathode material, the obtained specific capacity (330 mA h g-1) is the highest among all the other V2O5/SWCNT cathodes reported so far. This approach opens new horizons for the creation of the next generation of metal oxide composites for various applications, including optoelectronics and electrochemistry.

Modulating the Geometry of the Carbon Nanofiber Electrodes Provides Control over Dopamine Sensor Performance.

One of the major challenges for in vivo electrochemical measurements of dopamine (DA) is to achieve selectivity in the presence of interferents, such as ascorbic acid (AA) and uric acid (UA). Complicated multimaterial structures and ill-defined pretreatments have been frequently utilized to enhance selectivity. The lack of control over the realized structures has prevented establishing associations between the achieved selectivity and the electrode structure. Owing to their easily tailorable structure, carbon nanofiber (CNF) electrodes have become promising materials for neurobiological applications. Here, a novel yet simple strategy to control the sensitivity and selectivity of CNF electrodes toward DA is reported. It consists of adjusting the lengths of CNF by modulating the growth phase during the fabrication process while keeping the surface chemistries similar. It was observed that the sensitivity of the CNF electrodes toward DA was enhanced with the increase in the fiber lengths. More importantly, the increase in the fiber length induced (i) an anodic shift in the DA oxidation peak and (ii) a cathodic shift in the AA oxidation peak. As the UA oxidation peak remained unaffected at high anodic potentials, the electrodes with long CNFs showed excellent selectivity. Electrodes without proper fibers showed only a single broad peak in the solution of AA, DA, and UA, completely lacking the ability to discriminate DA. Hence, the simple strategy of controlling CNF length without the need to carry out any complex chemical treatments provides us a feasible and robust route to fabricate electrode materials for neurotransmitter detection with excellent sensitivity and selectivity.

Prediagnostic expressions in health records predict mortality in Parkinson's disease: A proof-of-concept study.

INTRODUCTION: The relationship of prodromal markers of PD with PD mortality is unclear. Electronic health records (EHRs) provide a large source of raw data that could be useful in the identification of novel relevant prognostic factors in PD. We aimed to provide a proof of concept for automated data mining and pattern recognition of EHRs of PD patients and to study associations between prodromal markers and PD mortality. METHODS: Data from EHRs of PD patients (n = 2522) were collected from the Turku University Hospital database between 2006 and 2016. The data contained >27 million words/numbers and >750000 unique expressions. The 5000 most common words were identified in three-year time period before PD diagnosis. Cox regression was used to investigate the association of expressions with the 5-year survival of PD patients. RESULTS: During the five-year period after PD diagnosis, 839 patients died (33.3%). If expressions associated with psychosis/hallucinations were identified within 3 years before the diagnosis, worse survival was observed (hazard ratio = 1.71, 95%CI = 1.46-1.99, p < 0.001). Similar effects were observed for words associated with cognition (1.23, 1.05-1.43, p = 0.009), constipation (1.34, 1.15-1.56, p = 0.0002) and pain (1.34, 1.12-1.60, p = 0.001). CONCLUSIONS: Automated mining of EHRs can predict relevant clinical outcomes in PD. The approach can identify factors that have previously been associated with survival and detect novel associations, as observed in the link between poor survival and prediagnostic pain. The significance of early pain in PD prognosis should be the focus of future studies with alternate methods.

Catalyst Support Effect on the Activity and Durability of Magnetic Nanoparticles: toward Design of Advanced Electrocatalyst for Full Water Splitting.

Earth-abundant element-based inorganic-organic hybrid materials are attractive alternatives for electrocatalyzing energy conversion reactions. Such material structures do not only increase the surface area and stability of metal nanoparticles (NPs) but also modify the electrocatalytic performance. Here, we introduce, for the first time, multiwall carbon nanotubes (MWNTs) functionalized with nitrogen-rich emeraldine salt (ES) (denoted as ES-MWNT) as a promising catalyst support to boost the electrocatalytic activity of magnetic maghemite (γ-Fe2O3) NPs. The latter component has been synthesized by a simple and upscalable one-step pulsed laser ablation method on Ni core forming the core-shell Ni@γ-Fe2O3 NPs. The catalyst (Ni@γ-Fe2O3/ES-MWNT) is formed via self-assembly as strong interaction between ES-MWNT and Ni@γ-Fe2O3 results in NPs' encapsulation in a thin C-N shell. We further show that Ni does not directly function as an active site in the electrocatalyst but it has a crucial role in synthesizing the maghemite shell. The strong interaction between the NPs and the support improves notably the NPs' catalytic activity toward oxygen evolution reaction (OER) in terms of both onset potential and current density, ranking it among the most active catalysts reported so far. Furthermore, this material shows a superior durability to most of the current excellent OER electrocatalysts as the activity, and the structure, remains almost intact after 5000 OER stability cycles. On further characterization, the same trend has been observed for hydrogen evolution reaction, the other half-reaction of water splitting.

Unusual Dual Superlyophobic Surfaces in Oil-Water Systems: The Design Principles.

Thermodynamically unusual surfaces that possess two contradictory wetting properties, i.e., underoil superhydrophobicity and underwater superoleophobicity, are prepared by the combination of re-entrant topography and delicately matched surface chemistry. The preparation of such extraordinary surfaces relies on two key design criteria and employs a metastable state effect in solid-oil-water systems.

Ultra-narrow metallic armchair graphene nanoribbons.

Graphene nanoribbons (GNRs)-narrow stripes of graphene-have emerged as promising building blocks for nanoelectronic devices. Recent advances in bottom-up synthesis have allowed production of atomically well-defined armchair GNRs with different widths and doping. While all experimentally studied GNRs have exhibited wide bandgaps, theory predicts that every third armchair GNR (widths of N=3m+2, where m is an integer) should be nearly metallic with a very small bandgap. Here, we synthesize the narrowest possible GNR belonging to this family (five carbon atoms wide, N=5). We study the evolution of the electronic bandgap and orbital structure of GNR segments as a function of their length using low-temperature scanning tunnelling microscopy and density-functional theory calculations. Already GNRs with lengths of 5 nm reach almost metallic behaviour with ∼100 meV bandgap. Finally, we show that defects (kinks) in the GNRs do not strongly modify their electronic structure.

Single-shell carbon-encapsulated iron nanoparticles: synthesis and high electrocatalytic activity for hydrogen evolution reaction.

Efficient hydrogen evolution reaction (HER) through effective and inexpensive electrocatalysts is a valuable approach for clean and renewable energy systems. Here, single-shell carbon-encapsulated iron nanoparticles (SCEINs) decorated on single-walled carbon nanotubes (SWNTs) are introduced as a novel highly active and durable non-noble-metal catalyst for the HER. This catalyst exhibits catalytic properties superior to previously studied nonprecious materials and comparable to those of platinum. The SCEIN/SWNT is synthesized by a novel fast and low-cost aerosol chemical vapor deposition method in a one-step synthesis. In SCEINs the single carbon layer does not prevent desired access of the reactants to the vicinity of the iron nanoparticles but protects the active metallic core from oxidation. This finding opens new avenues for utilizing active transition metals such as iron in a wide range of applications.

Halogen-bonded mesogens direct polymer self-assemblies up to millimetre length scale.

Aligning polymeric nanostructures up to macroscale in facile ways remains a challenge in materials science and technology. Here we show polymeric self-assemblies where nanoscale organization guides the macroscopic alignment up to millimetre scale. The concept is shown by halogen bonding mesogenic 1-iodoperfluoroalkanes to a star-shaped ethyleneglycol-based polymer, having chloride end-groups. The mesogens segregate and stack parallel into aligned domains. This leads to layers at ~10 nm periodicity. Combination of directionality of halogen bonding, mesogen parallel stacking and minimization of interfacial curvature translates into an overall alignment in bulk and films up to millimetre scale. Upon heating, novel supramolecular halogen-bonded polymeric liquid crystallinity is also shown. As many polymers present sites capable of receiving halogen bonding, we suggest generic potential of this strategy for aligning polymer self-assemblies.

Selective growth of SWNTs on partially reduced monometallic cobalt catalyst.

SiO(2) supported cobalt (Co) catalyst could be partially reduced and anchored by unreduced Co ions during a carbon monoxide (CO) chemical vapor deposition (CVD) process. This resulted in the formation of sub-nanometre metallic Co clusters catalyzing the growth of single-walled carbon nanotubes (SWNTs) with a narrow diameter distribution.

Predominant (6,5) single-walled carbon nanotube growth on a copper-promoted iron catalyst.

We have developed a magnesia (MgO)-supported iron-copper (FeCu) catalyst to accomplish the growth of single-walled carbon nanotubes (SWNTs) using carbon monoxide (CO) as the carbon source at ambient pressure. The FeCu catalyst system facilitates the growth of small-diameter SWNTs with a narrow diameter distribution. UV-vis-NIR optical absorption spectra and photoluminescence excitation (PLE) mapping were used to evaluate the relative quantities of the different (n,m) species. We have also demonstrated that the addition of Cu to the Fe catalyst can also cause a remarkable increase in the yield of SWNTs. Finally, a growth mechanism for the FeCu-catalyzed synthesis of SWNTs has been proposed.

A novel method for metal oxide nanowire synthesis.

Nanowires (NWs) of metal oxides (Fe(2)O(3), CuO, V(2)O(5) and ZnO) were grown by an efficient non-catalytic economically favorable method based on resistive heating of pure metal wires or foils at ambient conditions. The growth rate of iron oxide NWs exceeds 100 nm s(-1). Produced NWs were typically 1-5 microm long with diameters from 10 to 50 nm. The produced metal oxide NWs were characterized by means of SEM, TEM, EDX, XPS and Raman techniques. The field emission measurements from the as-produced CuO NWs were found to have a threshold field as low as 4 V microm(-1) at 0.01 mA cm(-2). The formation mechanism of the NWs is discussed.