Scale-up study of electrochemical carbon dioxide reduction process through data-driven modelling

Efficient electrochemical carbon dioxide reduction (eCO2RR) depends on addressing mass transfer kinetics hindering CO2 diffusion to the cathode surface. Gas diffusion electrodes (GDE) have enhanced this process, but the shift from lab-scale research to industrial use is to be explored, and we systematically assessed four variable factors: electrode area, gas flow rate, catalytic layer (CL) thickness and gas diffusion layer (GDL) porosity for scaling-up the electrolyser with a comprehensive two-dimensional physical model was developed to investigate the concentration, distribution, and consumption of CO2. Random Forest (RF) coupled with Latin Hypercube Sampling (LHS) data collection method demonstrate a prediction accuracy of 98.67 % and a RMSE of 0.00058 for the average CO2 concentration. A maximum CO2 consumption rate of 98 % was achieved at a CL thickness of 73 μm and a GDL with a porosity of 0.8, for an electrode area of 100 cm2 and a gas flow rate of 91 mL/min. This high level of CO2 consumption was sustained throughout the scaling-up process, consistently at 96.7 %, as the evidence attests to the reliability and feasibility of the scale-up approach

Porous Bilayer Electrode‐Guided Gas Diffusion for Enhanced CO 2 Electrochemical Reduction

Comparing with the massive efforts in developing innovative catalyst materials system and technologies, structural design of cells has attracted less attention on the road toward high‐performance electrochemical CO2 reduction reaction (eCO2RR). Herein, a hybrid gas diffusion electrode‐based reaction cell is proposed using highly porous carbon paper (CP) and graphene aerogels (GAs), which is expected to offer directional diffusion of gas molecules onto the catalyst bed, to sustain a high performance in CO2 conversion. The above‐mentioned hypothesis is supported by the experimental and simulation results, which show that the CP + GA combined configuration increases the Faraday efficiency (FE) from ≈60% to over 94% toward carbon monoxide (CO) and formate production compared with a CP only cell with Cu2O as the catalyst. It also suppresses the undesirable side reaction–hydrogen evolution over 65 times than the conventional H‐type cell (H‐cell). By combining with advanced catalysts with high selectivity, a 100% FE of the cell with a high current density can be realized. The described strategy sheds an extra light on future development of eCO2RR with a structural design of cell‐enabled high CO2 conversion

Simulation and machine learning assisted discovery of performance enhancement for CO2 reduction electrolyser and fuel cell

The escalation of carbon emissions poses a significant challenge for societies across the globe. As the levels of CO2 continue to surge, they create a greenhouse effect that traps heat in the Earth's atmosphere, leading to a gradual rise in the planet's temperature. reducing carbon emissions and developing new energy sources such as hydrogen are the solutions with visible benefits and considerable potential. This study explores the simulation application in the electrochemical catalysis of CO2RR and Porton Exchange membrane Fuel Cell (PEMFC). CO2RR is a process that generates low-carbon compounds, including CO and formic acid, which have considerable economic potential. Meanwhile, PEMFCs are known for their high efficiency, quick start-up, and relative ease of operation, making them a commercially viable option. Despite these advantages, these technologies face several challenges, including low electrocatalyst activity, mass transfer limitations, and low reaction rates, which have impeded their large-scale adoption. To overcome these hurdles and make these technologies viable for industrial use, eCO2RR must address issues such as poor catalyst selectivity, durability, and resistance to hydrogen evolution reactions. In Chapter 3, we studied the impact of different GDL structures on eCO2RR. Three cell types were designed: a carbon paper (CP) GDL type cell, a graphene aerogels (GA) GDL type cell, and a combined CP and GA GACP type cell. The study showed that GDL structural variations significantly influenced CO2RR efficiency. The FE increased from 60% to 94% (CP cell to GACP cell). The FE reduction relative to the Reversible Hydrogen Electrode (RHE) was approximately 65 times less. Highly selective catalysts can achieve 100% FE and high current density. Chapter 4 presented the impact of different catalyst structures on eCO2RR. Morphological changes in the catalyst effectively increased the porosity of all three interfaces and the total contact area, significantly enhancing CO2 mass transfer. In 1M KOH, at a voltage of -1.0 V relative to the RHE, the highest Faradaic efficiency for CO reached 93.20%. Chapter 5 examined the industrial aspects of Chapters 3 and 4. Simulation results showed that the thickness of the catalyst layer had the most significant impact on CO2 concentration during the cell scaling-up process. Optimal parameters achieved a CO2 consumption rate of over 98%

Cu2O Nano-flowers/Graphene Enabled Scaffolding Structure Catalyst Layer for Enhanced CO2 Electrochemical Reduction

Nanosized Cu2O catalysts with precisely controlled bud-to-blooming flower shapes are synthesised using modified polyol method. The evolution of the shape when the catalysts are applied to the gas diffusion electrodes improves the key factors influencing the catalyst layer, e.g. volume porosity and triple-phase boundary contact areas. Numerical and experimental studies revealed increased reactant molar concentration and improved CO2 mass transfer due to the structural changes, which influenced the electrochemical CO2 reduction reaction (eCO2RR). The fully bloomed Cu2O nanoflower catalyst, combined with the two-dimensional (2D) structured graphene sheet, formed a catalyst layer with scaffolding structure that exhibited the highest Faradaic efficiency (FE) of 93.20 towards CO at an applied potential of −1.0 V vs. RHE in 1M KOH. These findings established the relationship between the catalyst layer properties and mass transfer, based on which we could describe the effect of the structural design of the catalyst layer on the eCO2RR performance

Designing graded fuel cell electrodes for proton exchange membrane (PEM) fuel cells with recurrent neural network (RNN) approaches

The graded distribution of Pt loading in the catalyst layer (CL) and the porosity of the gas diffusion layer (GDL) significantly affect the spatial distributions of electrochemical reaction and mass transport rates, thus influencing the cell performance and durability. A sophisticated physics-based model is established to study the influence of graded Pt loading and GDL porosity at the cathode, with their distribution function obeying the elliptic equation along the in-plane and through-plane directions, on the current density and its uniformity at a given cell voltage. To reduce the computational time and resources, an RNN algorithm-based data-driven surrogate model is developed to assist in the identification of the relationship between the design parameters and the objective functions. Latin hypercube sampling (LHS) method is implemented for sampling and then the initial data acquisition is conducted for training and testing the surrogate model. Results show that the machine learning (ML) algorithm could effectively assist the optimal design of the functionally graded electrode, and the surrogate model achieves > 97.9 prediction accuracy for current density and < 0.13 root mean square error (RMSE) for current homogeneity. Both the individual variation of Pt loading and GDL porosity and their interaction are respectively analysed. Results also indicate that the inhomogeneous Pt distribution improves the current density. On the contrary, GDL porosity has a greater impact on the cell performance since current density monotonically increases with the homogeneous GDL porosity. When both the inhomogeneous distributions of Pt loading and GDL porosity are simultaneously considered, the homogeneity of current density is improved. However, the improvement of the homogeneity of current density (increases by 54) sacrifices the maximum current density (reduces by 22)

Mass Transfer Effect to Electrochemical Reduction of CO2: Electrode, Electrocatalyst and Electrolyte

Electrochemical carbon dioxide reduction reaction (eCO2RR) to value-added chemicals is considered as a promising strategy for CO2 conversion with economic and environmental benefits. Recently, investigations in eCO2RR to produce chemicals as energy or chemical industrial feedstock has received much attention. The eCO2RR generally occurs at the interface between electrode/electrocatalyst and electrolyte including charge transfer, phase transformation and mass transport. One of key problems in the electrochemical reaction is mass transfer limitation owing to the gaseous property of CO2 with low concentration on the surface of electrode/electrocatalyst. Several strategies were employed to improve mass transfer in the past years, including electrochemical reactors, electrodes, electrocatalysts and electrolytes, etc. which could low reaction barriers so adequately that reaction rates can be realized that are sufficient for eCO2RR. This article comprehensively reviewed development related to mass transfer study of CO2, including the mechanism of mass transfer of CO2, and main factors (electrodes, electrocatalysts and electrolytes) on two-phase or multi-phase interface during eCO2RR. The article is not aim at providing a comprehensive review of technical achievements towards eCO2RR technology, but rather to highlight electrode, catalyst, electrolyte, and other factors, which can understand the above components or factors’ effects toward mass transfer investigations, to decouple mass transfer limitations and improve the performance of electrochemical CO2 conversion. Furthermore, the challenges and perspectives for mass transfer to electrochemical eCO2RR are proposed

Mass transfer effect to electrochemical reduction of CO₂: Electrode, electrocatalyst and electrolyte

Electrochemical carbon dioxide reduction reaction (eCO2RR) to value-added chemicals is considered as a promising strategy for CO2 conversion with economic and environmental benefits. Recently, investigations in eCO2RR to produce chemicals as energy or chemical industrial feedstock have received much attention. The eCO2RR generally occurs at the interface between electrode/electrocatalyst and electrolyte including charge transfer, phase transformation and mass transport. One of key problems in the electrochemical reaction is mass transfer limitation owing to the gaseous property of CO2 with low concentration on the surface of electrode/electrocatalyst. Several strategies were employed to improve mass transfer in the past years, including electrochemical reactors, electrodes, electrocatalysts and electrolytes, etc. which could low reaction barriers so adequately that reaction rates can be realized that are sufficient for eCO2RR. This article comprehensively reviewed development related to mass transfer study of CO2, including the mechanism of mass transfer of CO2, and main factors (electrodes, electrocatalysts and electrolytes) on two-phase or multi-phase interface during eCO2RR. The article is not aim at providing a comprehensive review of technical achievements towards eCO2RR technology, but rather to highlight electrode, catalyst, electrolyte, and other factors, which can understand the above components or factors' effects towards mass transfer investigations, to decouple mass transfer limitations and improve the performance of electrochemical CO2 conversion. Furthermore, the challenges and perspectives for mass transfer to eCO2RR are proposed.</p

Endoscopic biopsy in gastrointestinal neuroendocrine neoplasms: a retrospective study.

Gastrointestinal neuroendocrine neoplasms (GI-NENs) are often located in the deep mucosa or submucosa, and the efficacy of endoscopic biopsy for diagnosis and treatment of GI-NENs is not fully understood.The current study analyzed GI-NENs, especially those diagnosed pathologically and resected endoscopically, and focused on the biopsy and cold biopsy forceps polypectomy (CBP) to analyze their roles in diagnosing and treating GI-NENs.Clinical data of all GI-NENs were reviewed from January 2006 to March 2012. Histopathology was used to diagnose GI-NENs, which were confirmed by immunohistochemistry.67.96% GI-NENs were diagnosed pathologically by endoscopy. Only 26.21% were diagnosed pathologically by biopsies before treatment. The diagnostic rate was significantly higher in polypoid (76.47%) and submucosal lesions (68.75%), than in ulcerative lesions (12.00%). However, biopsies were only taken in 56.31% patients, including 51.52% of polypoid lesions, 35.56% of submucosal lesions and 100.00% of ulcerative lesions. Endoscopic resection removed 61.76% of GI-NENs, including six by CBP, 14 by snare polypectomy with electrocauterization, 28 by endoscopic mucosal resection (EMR) and 15 by endoscopic submucosal dissection (ESD). 51.52% polypoid GI-NENs had infiltrated the submucosa under microscopic examination. CBP had a significantly higher rate of remnant (33.33%) than snare polypectomy with electrocauterization, EMR and ESD (all 0.00%).Biopsies for all polypoid and submucosal lesions will improve pre-operative diagnosis. The high rate of submucosal infiltration of polypoid GI-NENs determined that CBP was inadequate in the treatment of GI-NENs. Diminutive polypoid GI-NENs that disappeared after CBP had a high risk of remnant and should be closely followed up over the long term

Upregulation of a KN1 homolog by transposon insertion promotes leafy head development in lettuce

Leafy head is a unique type of plant architecture found in some vegetable crops, with leaves bending inward to form a compact head. The genetic and molecular mechanisms underlying leafy head in vegetables remain poorly understood. We genetically fine-mapped and cloned a major quantitative trait locus controlling heading in lettuce. The candidate gene (LsKN1) is a homolog of knotted 1 (KN1) from Zea mays Complementation and CRISPR/Cas9 knockout experiments confirmed the role of LsKN1 in heading. In heading lettuce, there is a CACTA-like transposon inserted into the first exon of LsKN1 (LsKN1▽). The transposon sequences act as a promoter rather than an enhancer and drive high expression of LsKN1▽. The enhanced expression of LsKN1▽ is necessary but not sufficient for heading in lettuce. Data from ChIP-sequencing, electrophoretic mobility shift assays, and dual luciferase assays indicate that the LsKN1▽ protein binds the promoter of LsAS1 and down-regulates its expression to alter leaf dorsoventrality. This study provides insight into plant leaf development and will be useful for studies on heading in other vegetable crops