Efficient Process Research Articles

Mission-oriented scenarios: a new method for urban foresight

PurposeThe purpose of this study is to propose an innovative and efficient process in urban policy-making that combines a divergent and creative method with a convergent and strategic one. At the same time, the purpose is also to propose a useful innovation to enforce the usability of both methods. On the one hand, mission-oriented policies run the risk of being overly focused on the present and of not being able to develop preparedness in organization. On the other hand, scenario development has the reverse problem it often does not point out how to use scenario narratives to inform and devise short-term strategic actions.Design/methodology/approachThe paper proposes an innovative methodological approach, the mission-oriented scenarios, which hybridizes Mazzucato's mission-oriented public policy framework with Jim Dator's Manoa school four futures method. The proposed methodological innovation emerges from a urban foresight academic-led project carried out in the context of the Metropolitan City of Turin, Italy, where a first application of the mission-oriented scenarios was tested on six different focal issues (from reindustrialization to cultural policies) and the scenario narratives were used as sources for the grounding of 12 missions and 48 strategic actions towards 2030.FindingsMission-oriented scenarios can contribute to the generation of more sustainable and inclusive urban public policies. This methodological proposal is based on an original mix of knowledge exchange procedures borrowed from methodological approaches with different backgrounds: the mission-oriented and the archetypal scenarios. Their conjunction could support the formulation of ambitious yet pragmatic policies, giving a plurality of actors the opportunity to act and establish fruitful and lasting partnerships.Originality/valueThe paper reconstructs one of the first urban foresight projects carried out in a major Italian city by two prestigious universities and exposes a methodological innovation resulting from reflection on the strengths and weaknesses of the project, which opens the door to the development of a new scenario technique.

Leveraging Generative AI for Data-Driven Insights and Visualization in Python

In this work, we introduce a novel solution that integrates Python, Streamlit, OpenAI, and embeddings to extract insights and generate visualizations from various data sources. Our approach empowers users to interact with data in plain text, enabling a more intuitive and efficient data analysis process. By leveraging Generative AI, our system not only simplifies data exploration but also addresses the challenges of identifying and generating charts, making data visualization more accessible. The solution accommodates diverse data sources, including PostgreSQL and CSV files, while ensuring a secure and user-friendly experience. This paper aims to demonstrate the effectiveness of combining unsupervised and supervised learning algorithms in real-time text analysis, providing a smart, quick, and scalable method for data-driven decision-making.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Insurance Based on Waqf and Blockchain Technology: A Strong Social Impact & Efficiency

We surveyed to measure the satisfaction of policyholders in Morocco, and the results clearly show that the majority of customers do not appreciate the current services. They suffer from the ambiguity of contracts, and delays in reimbursement and do not feel the real impact of insurance in society. To solve this problem, we propose an innovative insurance based on blockchain and waqf. We suggest in this paper, to use smart contracts to create an efficient and automatic process in the collection of premiums and reimbursement of policyholders. The goal of this paper is to build insurance that reflects the true meaning of solidarity through Waqf while integrating transparency and speed through Fintech. This insurance model is supposed to be resilient in times of crisis, have a strong social impact, and be attractive to customers. Many advantages of the proposed model are discussed in the paper. In addition, the suggested insurance model will be represented through simulations on the NetLogo platform. We carry out the analysis in normal times and evaluate the behavior of policyholders in choosing a specific type of insurance, depending on some decision-making tools. We also analyze the impact of insurance during a time of crisis, as a particular example, the crisis experienced during the coronavirus pandemic. The simulations aim to evaluate the model in different situations and prove its efficiency.

Examining information systems use to facilitate the workplace accommodation process.

The workplace accommodation process is often affected by ineffective and inefficient communications and information exchanges among disabled employees and other stakeholders. Information systems (IS) can play a key role in facilitating a more effective and efficient accommodation process since IS has been shown to facilitate business processes and effect positive organizational changes. Since there is little to no research that exists on IS use to facilitate the workplace accommodation process, this paper, as a critical first step, examines how IS have been used in the accommodation process. Thirty-six interviews were conducted with disabled employees from various organizations. Open, axial, and selective coding were part of the analysis. Fuzzy set qualitative comparative analysis was used to identify different levels of IS use based on participants' descriptions. An IS used in the workplace accommodation process consists of electronic request form, accommodation checklist, special budget, specific role, ancillary service, formal policy and procedure. There are different levels of IS use in the current accommodation process. The high-level IS use often results in a better accommodation performance than the low-level IS use, including high efficiency, high effectiveness, and low emotional tolls. Nevertheless, the high-level IS use often uses a specific, inflexible template as well as disregards human elements in the accommodation process. This work provides implications that future IS design should raise awareness of disability and accommodation, account for individual differences, involve multiple stakeholder inputs, as well as address the fundamental social issues in the accommodation process.

Exosome Isolation Using Chitosan Oligosaccharide Lactate-1-Pyrenecarboxylic Acid-Based Self-Assembled Magnetic Nanoclusters.

Exosomes are small extracellular vesicles that play a crucial role in intercellular communication and offer significant potential for a wide range of biomedical applications. However, conventional methods for exosome isolation have limitations in terms of purity, scalability, and preservation of exosome structural integrity. To address these challenges, an exosome isolation platform using chitosan oligosaccharide lactate conjugated 1-pyrenecarboxylic acid (COL-Py) based self-assembled magnetic nanoclusters (CMNCs), is presented. CMNCs are characterized to optimize their size, stability, and interaction dynamics with exosomes. The efficiency of CMNCs in isolating exosomes is systematically evaluated using various analytical methods to demonstrate their ability to capture exosomes based on amphiphilic lipid bilayers. CMNC-based exosome isolation consistently yields exosomes with structural integrity and purity similar to those obtained using traditional methods. The reusability of CMNCs over multiple exosome isolation cycles underscores their scalability and offers an efficient solution for biomedical applications. These results are supported by western blot analysis, which demonstrated the superiority of CMNC-based isolation in terms of purity compared to conventional methods. By providing a scalable and efficient exosome isolation process that preserves both structural integrity and purity, CMNCs can constitute a new platform that can contribute to the field of exosome studies.

Dauer juvenile recovery transcriptome of two contrasting EMS mutants of the entomopathogenic nematode Heterorhabditis bacteriophora.

The entomopathogenic nematode Heterorhabditis bacteriophora, symbiotically associated with enterobacteria of the genus Photorhabdus, is a biological control agent against many insect pests. Dauer Juveniles (DJ) of this nematode are produced in industrial-scale bioreactors up to 100 m3 in liquid culture processes lasting approximately 11days. A high DJ yield (> 200,000 DJ·mL-1) determines the success of the process. To start the mass production, a DJ inoculum proceeding from a previous monoxenic culture is added to pre-cultured (24h) Photorhabdus bacteria. Within minutes after contact with the bacteria, DJ are expected to perceive signals that trigger their further development (DJ recovery) to reproductive hermaphrodites. A rapid, synchronized, and high DJ recovery is a key factor for an efficient culture process. In case of low percentage of DJ recovery, the final DJ yield is drastically reduced, and the amount of non-desired stages (males and non-fertilized females) hinders the DJ harvest. In a preliminary work, a huge DJ recovery phenotypic variability in H. bacteriophora ethyl methanesulphonate (EMS) mutants was determined. In the present study, two EMS-mutant lines (M31 and M88) with high and low recovery phenotypes were analyzed concerning their differences in gene expression during the first hours of contact with Photorhabdus supernatant containing food signals triggering recovery. A snapshot (RNA-seq analysis) of their transcriptome was captured at 0.5, 1, 3 and 6h after exposure. Transcripts (3060) with significant regulation changes were identified in the two lines. To analyze the RNA-seq data over time, we (1) divided the expression profiles into clusters of similar regulation, (2) identified over and under-represented gene ontology categories for each cluster, (3) identified Caenorhabditis elegans homologous genes with recovery-related function, and (4) combined the information with available single nucleotide polymorphism (SNP) data. We observed that the expression dynamics of the contrasting mutants (M31 and M88) differ the most within the first 3h after Photorhabdus supernatant exposure, and during this time, genes related to changes in the DJ cuticle and molting are more active in the high-recovery line (M31). Comparing the gene expression of DJ exposed to the insect food signal in the haemolymph, genes related to host immunosuppressive factors were not found in DJ upon bacterial supernatant exposure. No link between the position of SNPs associated with high recovery and changes in gene expression was determined for genes with high differential expression. Concerning specific transcripts, nine H. bacteriophora gene models with differential expression are provided as candidate genes for further studies.

Comparative Study of Friction Stir, Stud and Seam Welding of Aluminium Alloys Using Different Grades

Friction stud welding is an innovative and efficient welding process used to create strong and reliable bonds between studs or fasteners and workpieces, particularly in applications where rapid and secure fastening is critical. This welding technique involves generating friction and heat between the stud and workpiece to forge a strong joint. The process consists of several key steps, including preparation, rotation, weld formation, and cooling. Friction stud welding offers numerous advantages, such as speed, consistency, minimal distortion, and the elimination of the need for additional filler materials. It is widely employed in various industries, including construction, automotive, and aerospace, for applications ranging from structural steel connections to automotive component assembly. However, successful friction stud welding necessitates careful selection of materials, welding parameters, and skilled operators to ensure durable and dependable welded joints. This abstract provides a concise overview of the friction stud welding process and its essential features.

Interrogating site dependent kinetics over SiO2-supported Pt nanoparticles

A detailed knowledge of reaction kinetics is key to the development of new more efficient heterogeneous catalytic processes. However, the ability to resolve site dependent kinetics has been largely limited to surface science experiments on model systems. Herein, we can bypass the pressure, materials, and temperature gaps, resolving and quantifying two distinct pathways for CO oxidation over SiO2-supported 2 nm Pt nanoparticles using transient pressure pulse experiments. We find that the pathway distribution directly correlates with the distribution of well-coordinated (e.g., terrace) and under-coordinated (e.g., edge, vertex) CO adsorption sites on the 2 nm Pt nanoparticles as measured by in situ DRIFTS. We conclude that well-coordinated sites follow classic Langmuir-Hinshelwood kinetics, but under-coordinated sites follow non-standard kinetics with CO oxidation being barrierless but conversely also slow. This fundamental method of kinetic site deconvolution is broadly applicable to other catalytic systems, affording bridging of the complexity gap in heterogeneous catalysis.

Assessing the Impact of Preprocessing and Conversion Technologies on the Sustainable Aviation Fuel Supply from Forest Residues in the Southeast U.S.A.

Forest residues have been suggested as potential feedstock for sustainable aviation fuel (SAF) production through different pathways. However, the bulky density and scatter distribution require efficient size-reduction processes to enhance the feedstock supply. Thus, this study analyzes the impact of alternative preprocessing and conversion technologies on the SAF supply chains from logging residues (the predominant share of forest residues) using a two-stage mixed-integer linear programming model. The model determines the optimal location of biomass preprocessing depots, conversion facilities, and airports receiving SAF by minimizing the net present value of the total supply chain cost over 10 years. Using high-resolution spatial data in the Southeast U.S.A. as a case study, the results show that the technology and scale of the facilities heavily influenced the SAF’s breakeven cost and maximum supply quantity. The most economically efficient system that adopts a rotary shear milling system and pyrolysis conversion process could generate up to 650 million gallons of SAF from logging residues in the region, with nearly 62% of the SAF being produced from hardwood residues. Also, the most efficient system has an estimated breakeven cost of US$5.51 per gallon of SAF.

PREPARATION AND JUSTIFICATION OF NANOFIBRES-LOADED MAFENIDE USING ELECTROSPINNING TECHNIQUE TO CONTROL RELEASE

Objective: The primary objective was to fabricate a novel drug delivery system capable of providing a controlled and prolonged release of antibiotics. Methods: The experimental design was formulated using Design-Expert® software (version 13), enabling systematic and efficient fabrication process optimization. The study involved the preparation of various nanofiber formulations with different ratios of the three polymers to assess their impact on drug release behavior. Mafenide, a widely used antibiotic, was chosen as the model drug for this investigation. The electrospinning process allowed for producing uniform and fine nanofibers with a high surface area, ensuring a large drug-loading capacity. The synthesized nanofibers were characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) to evaluate their morphology, chemical interactions, and thermal properties. The drug release kinetics of the antibiotic-loaded nanofibers were studied under different physiological conditions to assess their sustained release behavior. Results: The final nanofiber formula was successfully prepared using the electrospinning technique. The Fourier Transform Infrared Spectroscopy (FTIR) analysis was achieved to confirm the possibility of chemical interaction and bond formation between mafenide and the polymers. Present. The SEM picture of the optimized nanofiber formula showed the homogeneity and excellent entanglement of the electrospun nanofibers at a resolution of 5 µm. PVA/chitosan/HPMC and mafenide pure drug have been successfully fabricated with sufficient strength to resist swelling after absorbing wound exudate. The polymer network becomes more compact when chitosan and Hydroxypropyl Methyl Cellulose (HPMC) are combined with polyvinyl alcohol (PVA), enabling regulated swelling during solvent ingress. The polymer composite's three-dimensional network influenced how quickly the medication was released from the matrix. Sample 2's polymer network traps the medication, gradually releasing after controlled swelling, resulting in a sustained release profile compared to blank sample according to the cumulative release (%) study of mafenide loaded nanofiber and mafenide drug blank sample. Conclusion: This research successfully demonstrated the fabrication of sustained-release antibiotic nanofibers using electrospinning and three biocompatible polymers. The systematic optimization approach using Design-Expert® software proved effective in tailoring the drug release behavior of nanofibers. The developed drug delivery system holds great promise for pharmaceutical applications, particularly in improving antibiotic therapies and patient care. 

Multi-criteria structural optimization of a hood inner structure using machine learning technique

This article presents an efficient multi-criteria structural optimization process based on machine learning for shape and size optimization of the hood inner. For this purpose, a low dimensional parameterization based on two-dimensional mapping and dimensionality reduction is used to decrease the number of design variables. There are many design parameters to be considered for designing this structure. In this study, the effect of three important criteria consisting of head injury, eigenfrequency, and static load were analyzed. A full vehicle finite element model is prepared for a newly developed product because head injury analysis requires a full vehicle model due to contact with the inner components of the engine compartment. Then, an experimental study is performed based on the real test scenario for model verification. A new rating method for analyzing the head injury criteria evaluated in different points is proposed and different machine learning techniques are compared in the approximation of the objective function. Finally, the problem is solved as both single and multi-objective optimization problems. Results present considerable improvement in accuracy and efficiency in the identified structure while the method has a low computational cost and the implementation is not difficult.

Numerical simulations of a hydrogen-air pre-detonator for detonation engines

In recent years, heat release associated with the detonation phenomenon is considered a very efficient process. Pressure gain combustors are being developed with detonation waves as the driving force. In this scenario, study of detonation waves has become very important. Deflagration-to-Detonation transition (DDT) is one of the ways used to initiate detonation in the combustors. An attempt has been made in this paper to study the DDT in a tube with obstacles using OPENFOAM. A 2-dimensional rectangular section of a tube containing obstacles filled with stoichiometric hydrogen-air mixtures at different initial conditions have been studied. The effect of changes in the length of the tube, number of obstacles, blockage ratios and initial pressure of the reactants on the detonation wave properties have been studied using numerical simulation based on ddtfoam software. The motivation for this study is to design a pre-detonator for a rotating detonation engine (RDE). In order to make it compatible with the RDE and the test facility, it has been proposed to carry out simulations using shorter lengths of the DDT tube. The number of obstacles has been selected appropriately to obtain detonation at shorter lengths of the tube of the order of 1.5 m, 1.0 m & 0.5 m. As the objective has been to obtain design data for the prototype, the finer details of DDT are not being discussed in this paper.

A Perspective on Solar-Driven Electrochemical Routes for Sustainable Methanol Production

The transition towards sustainable and renewable energy sources is imperative in mitigating the environmental impacts of escalating global energy consumption. Methanol, with its versatile applications and potential as a clean energy carrier, a precursor chemical, and a valuable commodity, emerges as a promising solution within the realm of renewable energy technologies. This work explores the integration of electrochemistry with solar power to drive efficient methanol production processes, focusing on electrochemical reduction (ECR) of CO2 and methane oxidation reaction (MOR) as pathways for methanol synthesis. Through detailed analysis and calculations, we evaluate the thermodynamic limits and realistic solar-to-fuel (STF) efficiencies of ECR and MOR. Our investigation encompasses the characterization of multijunction light absorbers, determination of thermoneutral potentials, and assessment of STF efficiencies under varying conditions. We identify the challenges and opportunities inherent in both ECR and MOR pathways, shedding light on catalyst stability, reaction kinetics, and system optimization, thereby providing insights into the prospects and challenges of solar-driven methanol synthesis, offering a pathway towards a cleaner and more sustainable energy future.

Investigation of Drilling Holes in CFRP for Aircraft Using cBN Electroplated Ball End Mill Using Helical Interpolation Motion

Investigation of Drilling Holes in CFRP for Aircraft Using cBN Electroplated Ball End Mill Using Helical Interpolation Motion

A green and efficient lignite-fired power generation process based on superheated-steam-dried open pulverizing system

Currently, lignite-fired boilers often feature low thermal efficiency, difficult coal pulverizing and poor combustion stability owing to lignite having high water content. A novel lignite-fired power generation process is proposed in this paper with using a superheated-steam-drying open-pulverizing-system boiler (SSD-OPSB) and a deep utilization of waste heat from the steam for coal drying. Through using an open pulverizing system, the problems of low furnace temperature and low boiler efficiency are resolved. Meanwhile, the operation safety of the coal pulverizing system is ensured by using superheated steam as the drying agent. This paper described the working principle and engineering feasibility of the SSD-OPSB process, and established analysis model for its thermo-economic performance. Energy savings and environmental benefits of applying the process were calculated and discussed based on an in-service 600 MW supercritical power unit firing lignite with water content of 39.5% at its turbine heat acceptance condition. The result shows that for the power unit, applying the SSD-OPSB process can decrease coal consumption by 6.86% under the same power supply, meanwhile recovering water by 132.2 t/h and reducing CO2 emission by 27.78 t/h. It indicates that the SSD-OPSB process would significantly benefit lignite-fired power plants regarding energy-saving and environmental protection.

Dry fractionation of Australian mungbean for sustainable production of value‐added protein concentrate ingredients

AbstractBackground and ObjectivesDry fractionation, combining milling and air classification technology, offers an economical and sustainable pathway for the processing of value‐added protein ingredients from Australia's major pulse crops. Leveraging a wider range of pulses will help meet the growing demand for plant‐based protein ingredients. This study demonstrates the efficient processing of protein concentrates from Australian mungbean, which were characterized for key nutritional properties, phytochemical constituents, protein quality, and protein secondary structure.FindingsMilling and air classification of mungbean flour resulted in protein concentrates (fine fraction) with a protein content of 62.2 g/100 g (db), reflecting a two‐fold increase in protein content compared to the original flour (p < .05). Ash, fat, dietary fiber, and free phenolic and antioxidant compounds, also co‐concentrated with protein in the fine fraction (p < .05), imparting enhanced nutritional properties with the potential to deliver health benefits. The protein quality of dry fractionated mungbean protein concentrate was compared to a commercial source of mungbean protein isolate (88 g/100 g, [db]). Amino acid scores highlighted deficiencies in sulfur‐containing amino acids, methionine, and cysteine, as well as tryptophan and threonine. In vitro protein digestibility‐corrected amino acid scores for the mungbean protein concentrate and protein isolate were 63.7% and 59.5%, respectively. SDS‐PAGE protein profiles showed no major differences in protein composition, indicating that no specific types of proteins are lost during the dry fractionation process. Fourier transform infrared analysis of protein secondary structure showed that β‐sheets were the dominant structural component, with relative percentages of 36% and 49%, for the mungbean protein concentrate and isolate, respectively.ConclusionsThere is significant potential for dry fractionation to deliver value‐added opportunities for Australian mungbean, through the production of protein concentrate ingredients with enhanced nutritional properties for use in new and existing foods.Significance and NoveltyNutritional properties, protein quality, and protein secondary structure were compared for protein concentrates and protein isolates processed from Australian‐grown mungbean. Key findings from this study will inform pulse protein processors and pulse‐based food manufacturers.

Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor

Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process.

Biorefinery design with combined deacetylation and microwave pretreatment for enhanced production of polyhydroxyalkanoates and efficient carbon utilization from lignocellulose

Lignocellulosic biomass has emerged as a sustainable and economically viable feedstock for polyhydroxyalkanoate (PHA) production. However, it has seldom been used as part of an integrated process for effective carbon utilization. An innovative combined deacetylation and microwave pretreatment strategy (DEA-MW) and a wild-type Cupriavidus necator strain with the potential to utilize a lignocellulose-based carbon source and PHA biosynthesis were selected to set up the module needed to enable a new sustainable platform for biomass pretreatment and waste carbon valorization. DEA-MW pretreatment was introduced to mitigate the concentration of inhibitory acetic acid and phenolic compounds in the biomass hydrolysate. This pretreatment enhanced deacetylation and delignification, resulting in 95.50 % deacetylation and 45.30 % delignification. When DEA-MW pretreated rice husk biomass was coupled with microbial fermentation, C. necator promoted the fermentability of the rice husk hydrolysate. With a 3 g/L of levulinic acid from hydrolysis, as the sole carbon source, we achieved a concentration of PHA 440 mg/L with a content of 65 %. We also successfully recovered 72.22 % of lignin using a combined DEA-MW pretreatment. A detailed structural analysis showed that β-O-4, β–β, and β-5 linkages had been preserved in the recovered lignin. This study has shown that DEA-MW pretreatment plays a role in promoting biomass conversion and enhancing PHA production and opens up new possibilities for efficient and cost-effective lignocellulose-based biorefinery processes.

Influence of electric double layer rigidity on CO adsorption and electroreduction rate

Understanding the structure of the electric double layer (EDL) is critical for designing efficient electrocatalytic processes. However, the interplay between reactant adsorbates and the concentrated ionic species within the EDL remains an aspect that has yet to be fully explored. In the present study, we employ electrochemical CO reduction on Cu as a model reaction to reveal the significant impact of EDL structure on CO adsorption. By altering the sequence of applying negative potential and elevating CO pressure, we discern two distinct EDL structures with varying cation density and CO coverage. Our findings demonstrate that the EDL comprising densely packed cations substantially hinders CO adsorption on the Cu as opposed to the EDL containing less compact cations. These two different EDL structures remained stable over the course of our experiments, despite their identical initial and final conditions, suggesting an insurmountable kinetic barrier present in between. Moreover, we show that the size and identity of cations play decisive roles in determining the properties of the EDL in CO electroreduction on Cu. This study presents a refined adaptation of the classical Gouy-Chapman-Stern model and highlights its catalytic importance, which bridges the mechanistic gap between the EDL structure and cathodic reactions.