To restore the natural nitrogen cycle (N-cycle), artificial N-cycle electrocatalysis with flexibility, sustainability, and compatibility can convert intermittent renewable energy (e.g., wind) to harmful or value-added chemicals with minimal carbon emissions. The background of such N-cycles, such as nitrogen fixation, ammonia oxidation, and nitrate reduction, is briefly introduced here. The discussion of emerging nanostructures in various conversion reactions is focused on the architecture/compositional design, electrochemical performances, reaction mechanisms, and instructive tests. Energy device advancements for achieving more functions as well as <i>in situ</i>/<i>operando</i> characterizations toward understanding key steps are also highlighted. Furthermore, some recently proposed reactions as well as less discussed C–N coupling reactions are also summarized. We classify inorganic nitrogen sources that convert to each other under an applied voltage into three types, namely, abundant nitrogen, toxic nitrate (nitrite), and nitrogen oxides, and useful compounds such as ammonia, hydrazine, and hydroxylamine, with the goal of providing more critical insights into strategies to facilitate the development of our circular nitrogen economy. 

https://www.sciopen.com/article_pdf/1531201054523838466.pdf

Recent advances in nanostructured heterogeneous catalysts for N-cycle electrocatalysis

The dynamic recrystallization as well as meta‐dynamic and static recrystallization of the nickel‐based alloy 80A was investigated by means of electron backscatter diffraction (EBSD). Specimens were hot compressed at a temperature of 1120°C and a strain rate of 0.1/s at varying strain and soak times to describe the recrystallization behaviour. Various approaches were tested in order to differentiate between recrystallized and deformed grains based on EBSD data. The grain orientation spread was clearly found to be the most reliable procedure. A high twinning of the recrystallized grains was observed, and as a consequence the measured grain size was strongly dependent on whether the coherent and incoherent twin boundaries were regarded as genuine boundaries or removed.

Recrystallization behaviour of the nickel based alloy 80A during hot forming

ABSTRACT Machine learning (ML) is widely used in many industries, resulting in recent interests to explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms while this paper advocates an agenda to put data at the core, to develop novel algorithms that are effective for geotechnical data (existing and new), to address the needs of current practice, to exploit new opportunities from emerging technologies or to meet new needs from digital transformation, and to take advantage of current knowledge and accumulated experience. This agenda is called data-centric geotechnics and it contains three core elements: data centricity, fit for (and transform) practice, and geotechnical context. The future of machine learning in geotechnics should be envisioned with this “data first practice central” agenda in mind. Data-driven site characterization (DDSC) is an active research topic in this agenda because an understanding of the ground is crucial in all projects. Examples of DDSC challenges are ugly data and explainable site recognition. Additional challenges include making ML indispensable (ML supremacy), learning how to learn (meta-learning), and becoming smart (digital twin).

Future of machine learning in geotechnics

Electrochemical neuron-specific enolase (NSE) immunosensor based on CoFe2O4@Ag nanocomposite and AuNPs@MoS2/rGO.

A brief discussion on offshore wind turbine hydrodynamics problem

Aiming to investigate the impacts of n-octanol addition on the oxidation reactivity, morphology and graphitization of diesel exhaust particles, soot samples were collected from a four-cylinder turbocharged diesel engine fueled with D100 (neat diesel fuel), DO15 (85% diesel and 15% n-octanol, V/V) and DO30 (70% diesel and 30% n-octanol, V/V). All tests were conducted at two engine speeds of 1370 and 2150 r/min under a fixed torque of 125 N·m. The soot properties were characterized by thermogravimetric analyzer (TGA), transmission electron microscopy (TEM) and Raman spectroscopy (RS). The higher volatile organic fraction content, lower soot oxidation temperatures and lower activation energy from TGA results indicated that both the increasing n-octanol concentration and engine speed enhanced the soot oxidation reactivity. Additionally, quantitative analysis of TEM images showed that the soot derived from DO30 had the smallest primary particle diameters and fractal dimension, followed by those of soot produced by DO15 and D100. The RS results demonstrated that the n-octanol addition and higher engine speed led to a larger D1-FWHM (D1-full width at half maximum), AD1/AT (area ratio of D1 band and the total spectral) and AD3/AT (area ratio of D3 band and the total spectral) as well as a smaller La (crystallite width), revealing a lower degree of graphitization. Furthermore, the correlations between characterization parameters of soot properties and reactivity were nonlinear.

Basil Varkey

Papers

Experimental investigation into the oxidation reactivity, morphology and graphitization of soot particles from diesel/n-octanol mixtures

Recrystallization process of hot-extruded 6A02 aluminum alloy in solid and semi-solid temperature ranges

Selective electrocatalytic reduction of nitrate to dinitrogen by Cu2O nanowires with mixed oxidation-state

Selective electrocatalytic reduction of nitrate to dinitrogen by Cu2O nanowires with mixed oxidation-state

Aerodynamic performance improvement analysis of Savonius Vertical Axis Wind Turbine utilizing plasma excitation flow control