Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.The online version contains supplementary material available at 10.1007/s11831-023-09922-z. 

/pdf/artificial-intelligence-in-physical-sciences-symbolic-199i2ew9.pdf

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

The electrochemical conversion of carbon dioxide by means of renewable electricity holds great promise. However, despite significant progress in current literature, there remains a significant gap between fundamental research and the industrial demands to establish new disruptive technologies in real world applications. This gap primarily arises from a mismatch between performance parameters and requirements in both areas, leading to significant challenges in technology transfer. We herein suggest pathways to bridge this gap and outline current limitations in the field, proposing key parameters and procedures towards accelerated and streamlined technology development. Despite significant progress in CO2 conversion field, there remains a significant gap between fundamental research and the industrial demands. This Comment discusses key performance parameters for industrial applications and outlines current limitations in the field. 

Accelerating CO2 electrochemical conversion towards industrial implementation

CO2 electroreduction is a promising technique to convert renewable electricity and CO2 to high-value fuels and chemicals. Selectivity, energy efficiency, carbon efficiency and sustainability are the criteria for CO2 electroreduction techniques suitable for industrial application. With alkaline and neutral electrolytes, carbonate formation from CO2 leads to low carbon efficiency. High energy consumption to regenerate alkaline electrolyte and high resistance of neutral electrolyte cause low energy efficiency. Recently, CO2 reduction with acidic electrolyte becomes a hot topic due to its potential to increase carbon efficiency and energy efficiency. Improving the selectivity towards CO2 reduction is challenging in acidic condition. Diverse approaches were proposed to suppress H+ reduction and promote CO2 reduction. However, fundamental issues about cation effect and local pH effect on CO2 reduction in acidic condition are still under debate. Moreover, bicarbonate precipitation in gas diffusion electrode limits the sustainability with acidic electrolyte. This review tries to rationalize the reported strategies to improve the selectivity towards CO2 reduction in acidic condition from mass transport and electrode reactions. Different approaches, including adding alkali cations, surface decoration, nanostructuring, and electronic structure modulation, are designed based on these two aspects. This review also introduces the recent progress in CO2 electroreduction with metal cation-free acidic electrolyte. This strategy is deemed to improve the sustainability. 

Strategies for efficient CO2 electroreduction in acidic conditions

Analysis of the CO production cost from CO2via gliding arc plasma reactors with embedded carbon beds versus low-temperature electrolysers.

CO2 conversion to CO via plasma and electrolysis: a techno-economic and energy cost analysis

This work explores and provides new understanding how catholyte flow compartment design and catholyte bubble flow characteristics of a Gas Diffusion Electrode inside a CO2 flow cell electrolyzer affect its...

Understanding the Impact of Catholyte Flow Compartment Design on the Efficiency of CO2 Electrolyzers

The use of gas diffusion electrodes that supply gaseous CO2 directly to the catalyst layer has greatly improved the performance of electrochemical CO2 conversion. However, reports of high current densities and Faradaic efficiencies primarily come from small lab scale electrolysers. Such electrolysers typically have a geometric area of 5 cm2, while an industrial electrolyser would require an area closer to 1 m2. The difference in scales means that many limitations that manifest only for larger electrolysers are not captured in lab scale setups. We develop a 2D computational model of both a lab scale and upscaled CO2 electrolyser to determine performance limitations at larger scales and how they compare to the performance limitations observed at the lab scale. We find that for the same current density larger electrolysers exhibit much greater reaction and local environment inhomogeneity. Increasing catalyst layer pH and widening concentration boundary layers of the KHCO3 buffer in the electrolyte channel lead to higher activation overpotential and increased parasitic loss of reactant CO2 to the electrolyte solution. We show that a variable catalyst loading along the direction of the flow channel may improve the economics of a large scale CO2 electrolyser.

Inhomogeneities in the Catholyte Channel Limit the Upscaling of CO2 Flow Electrolysers

Do Logarithmic Terms Exist in the Drag Coefficient of a Single Sphere at High Reynolds Numbers?

/pdf/a-note-on-the-modelling-of-lubrication-forces-in-unresolved-3cno2fg1.pdf

A note on the modelling of lubrication forces in unresolved simulations

Hydrodynamics of expanded bed adsorption studied through CFD-DEM

Sodium borohydride (NaBH4) has a high hydrogen (H2 ) gravimetric capacity of 10.7 wt %. NaBH4 releases H2 through a hydrolysis reaction in which aqueous NaB(OH)4 is formed as a byproduct. NaB(OH)4 strongly influences the thermophysical properties of aqueous solutions (i.e., densities, viscosities, and electrical conductivities) and the hydrolysis reaction kinetics and conversion of NaBH4. Here, molecular dynamics (MD) simulations are performed to compute viscosities, electrical conductivities, and self-diffusivities of H2 , Na+, and B(OH)4– for a temperature and concentration range of 298–353 K and 0–5 mol NaB(OH)4/kg water, respectively. Continuous fractional component Monte Carlo (CFCMC) simulations are used to compute the solubilities of H2 and activities of water in aqueous NaB(OH)4 solutions for the same temperature and concentration range. A new force field is developed (Delft force field of B(OH)4–: DFF/B(OH)4–) in which B(OH)4– is modeled as a tetrahedral structure with a scaled charge of −0.85. The OH group in B(OH)4– is modeled as a single interaction site. This force field is based on TIP4P/2005 water and the Madrid-2019 Na+ force field. The MD simulations can accurately capture the densities and viscosities within 2.5% deviation from available experimental data at 298 K up to a concentration of 5 mol NaB(OH)4/kg water. The computed electrical conductivities deviate by ca. 10% from experimental data at 298 K for the same concentration range. Based on the molecular simulations results, engineering equations are developed for shear viscosities, self-diffusivities of H2, Na+, and B(OH)4–, and solubilities of H2, which can be used to design and model NaBH4 hydrolysis reactors. 

Thermodynamic and Transport Properties of H2/H2O/NaB(OH)4 Mixtures Using the Delft Force Field (DFF/B(OH)4–)

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