Energy Cost Research Articles

Fluctuation-induced extensive molecular transport via modulation of interaction strength.

In our previous work, we studied the thermodynamics of two cases of intercompartmental transport through a carbon nanotube: one involving water molecules and the other involving nonpolar molecules. Free energy calculations indicate that transporting water molecules from one compartment to another via a narrow channel is impossible, whereas for nonpolar molecules, only approximately half can be transported. Therefore, the interaction strength between transported molecules significantly affects molecular transport. In this study, we examined the effect of interaction strength on molecular transport both kinetically and thermodynamically via simple models and molecular simulation methods. The results revealed that, depending on the interaction strength, the transport behavior can be categorized into three regimes: water-like, nonpolar-like, and transition regimes. Interestingly, the molecular fluctuations in the transition regime are so large that a significant number of molecules are transported between the compartments in an oscillatory manner, exceeding the transport of half of the molecules in the nonpolar-like regime. Thus, to induce molecular transport driven by large fluctuations, the interaction strength should remain within a moderate range. Moreover, potential of mean force (PMF) analysis supports this large fluctuating behavior, as the PMF profile exhibits a flat region that allows significant variation with no free energy cost. We elucidate the role of interaction strength in molecular transport, as well as the deep connection between molecular fluctuations and molecular transport.

Inferring the energy cost of resistance to parasitic infection and its link to a trade-off

BackgroundIn infected hosts, immune responses trigger a systemic energy reallocation away from energy storage and growth, to fuel a costly defense program. The exact energy costs of immune defense are however unknown in general. Life history theory predicts that such costs underpin trade-offs between host disease resistance and other fitness related traits, yet this has been seldom assessed. Here we investigate immune energy cost induced by infection, and their potential link to a trade-off between host resistance and fat storage that we previously exposed in sheep divergently selected for resistance to a pathogenic helminth.ResultsTo this purpose, we developed a mathematical model of host-parasite interaction featuring individual changes in energy allocation over the course of infection. The model was fitted to data from an experimental infectious challenge in sheep from genetically resistant and susceptible lines to infer the magnitude of immune energy costs. A relatively small and transient immune energy cost in early infection best explained within-individual changes in growth, energy storage and parasite burden. Among individuals, predicted responses assuming this positive energy cost conformed to the observed trade-off between resistance and storage, whereas a cost-free scenario incorrectly predicted no trade-off.ConclusionsOur mechanistic model fitting to experimental data provides novel insights into the link between energy costs and reallocation due to induced resistance within-individual, and trade-offs among individuals of selected lines. These will be useful to better understand the exact role of energy allocation in the evolution of host defenses, and for predicting the emergence of trade-offs in genetic selection.

Efficient deep neural network training via decreasing precision with layer capacity

Low-precision training has emerged as a practical approach, saving the cost of time, memory, and energy during deep neural networks (DNNs) training. Typically, the use of lower precision introduces quantization errors that need to be minimized to maintain model performance, often neglecting to consider the potential benefits of reducing training precision. This paper rethinks low-precision training, highlighting the potential benefits of lowering precision: (1) low precision can serve as a form of regularization in DNN training by constraining excessive variance in the model; (2) layer-wise low precision can be seen as an alternative dimension of sparsity, orthogonal to pruning, contributing to improved generalization in DNNs. Based on these analyses, we propose a simple yet powerful technique–DPC (Decreasing Precision with layer Capacity), which directly assigns different bit-widths to model layers, without the need for an exhaustive analysis of the training process or any delicate low-precision criteria. Thorough extensive experiments on five datasets and fourteen models across various applications consistently demonstrate the effectiveness of the proposed DPC technique in saving computational cost (−16.21%–−44.37%) while achieving comparable or even superior accuracy (up to +0.68%, +0.21% on average). Furthermore, we offer feature embedding visualizations and conduct further analysis with experiments to investigate the underlying mechanisms behind DPC’s effectiveness, enhancing our understanding of low-precision training. Our source code will be released upon paper acceptance.

A thermodynamically consistent approach to the energy costs of quantum measurements

A thermodynamically consistent approach to the energy costs of quantum measurements

Optimal Sizing and Techno-economic Analysis of Combined Solar Wind Power System, Fuel Cell and Tidal Turbines Using Meta-heuristic Algorithms: A Case Study of Lavan Island

Combined renewable energy sources (RESs) are emerging as a competitive alternative to conventional energy production facilities due to their sustainability and zero-emission characteristics. However, determining the optimal system size is complicated by two major challenges: the cost of energy (COE) and the intermittent nature of RESs. This study introduces a novel mathematical approach to optimize the sizing of photovoltaic (PV), wind, hydrogen, battery, and fuel cell systems with electrolyzers, specifically tailored for the remote area of Lavan Island. The proposed method aims to deliver electricity without reliance on the traditional electricity distribution grid, while offering a scalable solution applicable to other geographical regions. The primary objective is to achieve cost-effective electricity generation while ensuring a reliable energy supply through the evaluation of system reliability indices. A fuzzy logic system is employed to minimize the costs of a hybrid system incorporating hydroelectric, wind, solar, and battery technologies, while simultaneously calculating two key reliability metrics: the Loss of Power Supply Probability (LPSP) and the Dump Energy Probability (DEP). To optimize the objective function, this study applies three advanced algorithms: the Shuffled Frog Leaping Algorithm (SFLA), the Grasshopper Optimization Algorithm (GOA), and the Honey Badger Algorithm (HBA). These algorithms are used to determine the global optimum, with comparative analyses conducted to highlight the performance of the proposed approach. The results are evaluated based on statistical metrics, including consistency, execution time, convergence speed, and the minimization of the objective function. The findings demonstrate the superiority and the reliability of the proposed method over alternative approaches, paving the way for cost-efficient and sustainable energy solutions in isolated regions.

Bioenergetic trade-offs can reveal the path to superior microbial CO2 fixation pathways.

A comprehensive optimization of known prokaryotic autotrophic carbon dioxide (CO2) fixation pathways is presented that evaluates all their possible variants under different environmental conditions. This was achieved through a computational methodology recently developed that considers the trade-offs between energy efficiency (yield) and growth rate, allowing us to evaluate candidate metabolic modifications in silico for microbial conversions. The results revealed the superior configurations in terms of both yield (efficiency) and rate (driving force). The pathways from anaerobic organisms appear to fix carbon at lower net ATP cost than those found in aerobic organisms, and the reverse TCA cycle pathway shows the lowest overall energy cost and maximum adaptability across a broad range of CO2 and electron donor (H2) concentrations. The reverse tricarboxylic acid cycle and Wood-Ljungdahl pathways appear highly efficient under a broad range of conditions, while the 3-hydroxypropionate 4-hydroxybutyrate cycle and the 3-hydroxypropionate bicycle appear capable of generating large thermodynamic driving forces at only moderate ATP yield losses.IMPORTANCEBiotechnology can lead to cost-effective processes for capturing carbon dioxide using the natural or genetically engineered metabolic capabilities of microorganisms. However, introducing desirable genetic modifications into microbial strains without compromising their fitness (growth yield and rate) during industrial-scale cultivation remains a challenge. The approach and results presented can guide optimal pathway configurations for enhanced prokaryotic carbon fixation through metabolic engineering. By aligning strain modifications with these theoretically revealed near-optimal pathway configurations, we can optimally engineer strains of good fitness under open culture industrial-scale conditions.

Towards Sustainable Magnetic Resonance Neuro Imaging: Pathways for Energy Optimization and Cost Reduction Strategies

We evaluated the energy consumption of a 3T MRI using a central monitoring system, focusing on hospital energy costs during peak winter months from 2021 to 2023. We analyzed consumption during non-productive phases like end-of-day standby and assessed their impact. For active use, we compared standard and AI-enhanced protocols on phantoms, scheduling high-demand protocols during off-peak hours to benefit from lower energy prices. Standard protocols consumed 3.4 to 15 kWh, while optimized protocols used 2.3 to 10.6 kWh, reducing consumption by 32% on average. Savings per scan ranged from EUR 0.03 to EUR 3.7. The electrical consumption of a brain MRI protocol is equivalent to that of 3–4 knee protocols or 2–3 lumbar spine protocols. Using AI-optimized protocols and management, 41 protocols can be completed in 12 h, up from 30, reducing daily costs by EUR 2.38 to EUR 29.18. Annually, AI-optimized protocols could save 7900 to 8800 kWh per MRI unit, totaling 10,500 to 11,600 MWh across France’s MRI fleet, equivalent to the yearly consumption of about 4700 to 5300 people. Optimizing MRI resource use can expand patient access while significantly reducing the associated energy footprint. These findings support the implementation of more sustainable practices in medical imaging without compromising care quality.

Improvement of crop production in controlled environment agriculture through breeding

Controlled environment agriculture (CEA) represents one of the fastest-growing sectors of horticulture. Production in controlled environments ranges from highly controlled indoor environments with 100% artificial lighting (vertical farms or plant factories) to high-tech greenhouses with or without supplemental lighting, to simpler greenhouses and high tunnels. Although food production occurs in the soil inside high tunnels, most CEA operations use various hydroponic systems to meet crop irrigation and fertility needs. The expansion of CEA offers promise as a tool for increasing food production in and near urban systems as these systems do not rely on arable agricultural land. In addition, CEA offers resilience to climate instability by growing inside protective structures. Products harvested from CEA systems tend to be of high quality, both internal and external, and are sought after by consumers. Currently, CEA producers rely on cultivars bred for production in open-field agriculture. Because of high energy and other production costs in CEA, only a limited number of food crops have proven themselves to be profitable to produce. One factor contributing to this situation may be a lack of optimized cultivars. Indoor growing operations offer opportunities for breeding cultivars that are ideal for these systems. To facilitate breeding these specialized cultivars, a wide range of tools are available for plant breeders to help speed this process and increase its efficiency. This review aims to cover breeding opportunities and needs for a wide range of horticultural crops either already being produced in CEA systems or with potential for CEA production. It also reviews many of the tools available to breeders including genomics-informed breeding, marker-assisted selection, precision breeding, high-throughput phenotyping, and potential sources of germplasm suitable for CEA breeding. The availability of published genomes and trait-linked molecular markers should enable rapid progress in the breeding of CEA-specific food crops that will help drive the growth of this industry.

Resources-Blessed but Energy-Poor: A Critical Review of the Paradox of Energy Delivery in Nigeria

Abstract: Energy is essential for the growth and improved livelihood of any nation. Nigeria, though blessed with abundance of oil and gas, and has good land mass and natural resources for renewable energy, struggles with energy delivery. Many industries have shut down due to high energy cost or unavailability of the energy required to support their operations. Through critical review of existing studies on the Nigerian energy delivery, this study identified that the Nigerian energy challenges stem from infrastructural issues, environmental degradation, inconsistent government policies, poor regulatory framework and limited investments in renewable energy. However, the challenges can be mitigated if there is renewed policy thrust by the Nigerian government, energy incentives, stakeholders’ involvement in decision-making in the sector and diversification of public power supply into renewable energy. As much as Nigeria still struggles with energy delivery from crude oil and gas, it is obvious that the future of energy delivery in Nigeria lies in resort to renewable energy sources.

Simulation-Based Studies on FAGeI3-Based Lead (Pb2+)-Free Perovskite Solar Cells

In the recent reports, it is clear that lead-free perovskite materials with low band gaps are desirable candidates for photovoltaic cells. In this regard, it was observed that germanium (Ge) is a less toxic lead-free metal that is significant for the preparation of Ge-based perovskite materials. Ge-based perovskite materials, for example, methyl ammonium germanium iodide (MAGeI3), cesium germanium iodide (CsGeI3), and/or formamidinium germanium iodide (FAGeI3) may be the suitable absorber materials and alternatives towards the fabrication of lead-free photovoltaic cells. In the past few years, few attempts were made to develop FAGeI3-based perovskite solar cells, but their photovoltaic performance is still under limitations. This is indicating that some significant and effective strategies should be designed and developed for the construction of Ge-based perovskite solar cells. It is believed that optimization of layer thickness, device structure, and selection of a suitable electron transport layer (ETL) may improve the photovoltaic performance of FAGeI3-based perovskite solar cells. Solar cell capacitance simulation, i.e., SCAPS is one of the promising software programs that can provide significant theoretical findings for the development of FAGeI3-based perovskite solar cells. The simulation studies via SCAPS may benefit researchers to save their energy and high cost for the optimization process in the laboratories. In this research article, SCAPS was adopted as a simulation tool for the theoretical investigations of FAGeI3-based perovskite solar cells. The simulation studies exhibited the excellent efficiency of 15.62% via SCAPS. This study proposed the optimized device structure of FTO/TiO2/FAGeI3/PTAA/Au with enhanced photovoltaic performance.

Constructed wetland - microbial fuel cell (CW-MFC) mediated bio-electrodegradation of azo dyes from textile wastewater.

Azo dyes constitute 60-70% of commercially used dyes and are complex, carcinogenic, and mutagenic pollutants that negatively impact soil composition, water bodies, flora, and fauna. Conventional azo dye degradation techniques have drawbacks such as high production and maintenance costs, use of hazardous chemicals, membrane clogging, and sludge generation. Constructed Wetland-Microbial Fuel Cells (CW-MFCs) offer a promising sustainable approach for the bio-electrodegradation of azo dyes from textile wastewater. CW-MFCs harness the phytodegradation capabilities of wetland plants like Azolla, water hyacinth, and Ipomoea, along with microalgae such as Nostoc, Oscillatoria, Chlorella, and Anabaena, to break down azo dyes into aromatic amines. These intermediates are then reduced to CO2 and H2O by microalgae in the fuel cells, while simultaneously generating electricity. CW-MFCs offer advantages including low cost, sustainability, and use of renewable energy. The valorization of the resulting algal and plant biomass further enhances the sustainability of this approach, as it can be used for biofuel production, nutraceuticals, pharmaceuticals, and bio-composting. Implementing CW-MFCs as a tertiary treatment step in textile industries aligns with the circular economy concept and contributes to achieving several Sustainable Development Goals (SDGs).

Multi-Objective Dynamic System Model for the Optimal Sizing and Real-World Simulation of Grid-Connected Hybrid Photovoltaic-Hydrogen (PV-H2) Energy Systems

Hybrid renewable-hydrogen energy systems offer a promising solution for meeting the globe’s energy transition and carbon neutrality goals. This paper presents a new multi-objective dynamic system model for the optimal sizing and simulation of hybrid PV-H2 energy systems within grid-connected buildings. The model integrates a Particle Swarm Optimisation (PSO) algorithm that enables minimising both the levelised cost of energy (LCOE) and the building carbon footprint with a dynamic model that considers the real-world behaviour of the system components. Previous studies have often overlooked the electrochemical dynamics of electrolysers and fuel cells under transient conditions from intermittent renewables and varying loads, leading to the oversizing of components. The proposed model improves sizing accuracy, avoiding unnecessary costs and space. The multi-objective model is compared to a single-objective PSO-based model that minimises the LCOE solely to assess its effectiveness. Both models were applied to a case study within Robert Gordon University in Aberdeen, UK. Results showed that minimising only the LCOE leads to a system with a 1000 kW PV, 932 kW electrolyser, 22.7 kg H2 storage tank, and 242 kW fuel cell, with an LCOE of 0.366 £/kWh and 40% grid dependency. The multi-objective model, which minimises both the LCOE and the building carbon footprint, results in a system with a 3187.8 kW PV, 1000 kW electrolyser, 106.1 kg H2 storage tank, and 250 kW fuel cell, reducing grid dependency to 33.33% with an LCOE of 0.5188 £/kWh.

Boston Consulting Group Matrix-Based Equilibrium Optimizer for Numerical Optimization and Dynamic Economic Dispatch

Numerous optimization problems exist in the design and operation of power systems, critical for efficient energy use, cost minimization, and system stability. With increasing energy demand and diversifying energy structures, these problems grow increasingly complex. Metaheuristic algorithms have been highlighted for their flexibility and effectiveness in addressing such complex problems. To further explore the theoretical support of metaheuristic algorithms for optimization problems in power systems, this paper proposes a novel algorithm, the Boston Consulting Group Matrix-based Equilibrium Optimizer (BCGEO), which integrates the Equilibrium Optimizer (EO) with the classic economic decision-making model, the Boston Consulting Group Matrix. This matrix is utilized to construct a model for evaluating the potential of individuals, aiding in the rational allocation of computational resources, thereby achieving a better balance between exploration and exploitation. In comparative experiments across various dimensions on CEC2017, the BCGEO demonstrated superior search performance over its peers. Furthermore, in dynamic economic dispatch, the BCGEO has shown strong optimization capabilities and potential in power system optimization problems. Additionally, the experimental results in the spacecraft trajectory optimization problem suggest its potential for broader application across various fields.

A Detailed Review of Organic Rankine Cycles Driven by Combined Heat Sources

The Organic Rankine Cycle (ORC) is an effective method for transforming low- and medium-grade heat into electricity that has recently gained significant attention. Several review studies in the literature are focused on working fluids, system architecture, and the individual utilization of renewable and alternative heat sources in ORCs, like solar irradiation, geothermal, biomass, and waste heat energy. However, no studies have yet investigated ORC systems driven by two of the aforementioned sources combined. This work aims to review and explore multiple aspects of hybrid ORC systems. Such systems are categorized based on source combinations and configurations, and the results regarding their thermodynamic, thermo-economic, and environmental performance are discussed. The source arrangements follow the following three main configurations: series, parallel, and heat upgrade. Most of the examined systems include solar energy as one of the sources and only four cases involve combinations of the other three sources. The reported results show that hybrid ORCs generally perform better thermodynamically compared to their respective single-source systems, exhibiting an enhancement in power production that reaches 44%. An average levelized cost of energy (LCOE) of 0.165 USD/kWh was reported for solar–geothermal plants, 0.153 USD/kWh for solar–biomass plants, and 0.100 USD/kWh for solar–waste plants. Solar–biomass plants also reported the lowest reported LCOE value of 0.098 USD/kWh. The payback periods ranged from 2.88 to 10.5 years. Further research is proposed on multiple source combinations, the in-depth analysis of the three main configurations, the integration of polygeneration systems, the incorporation of zeotropic mixture working media and experimental research on ORCs with combined sources.

Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices

Traditional limb kinematic analysis relies on manual goniometer measurements. With computer vision advancements, integrating RGB cameras can minimize manual labor. Although deep learning-based cameras aim to offer the same ease as manual goniometers, previous approaches have prioritized accuracy over efficiency and cost on PC-based devices. Nevertheless, healthcare providers require a high-performance, low-cost, camera-based tool for assessing upper and lower limb range of motion (ROM). To address this, we propose a lightweight, fast, deep learning model to estimate a human pose and utilize predicted joints for limb ROM measurement. Furthermore, the proposed model is optimized for deployment on resource-constrained edge devices, balancing accuracy and the benefits of edge computing like cost-effectiveness and localized data processing. Our model uses a compact neural network architecture with 8-bit quantized parameters for enhanced memory efficiency and reduced latency. Evaluated on various upper and lower limb tasks, it runs 4.1 times faster and is 15.5 times smaller than a state-of-the-art model, achieving satisfactory ROM measurement accuracy and agreement with a goniometer. We also conduct an experiment on a Raspberry Pi, illustrating that the method can maintain accuracy while reducing equipment and energy costs. This result indicates the potential for deployment on other edge devices and provides the flexibility to adapt to various hardware environments, depending on diverse needs and resources.

Thermoeconomic Analyses of Heat Pumps

Recently, the rapid development of technology and the increase in energy costs and needs have made reducing the costs and efficient use of energy and exergy important. The thermoeconomic analysis method, which is the best cost analysis method of energy and exergy, gives the ways to reduce costs and increase efficiency that can be applied a facility. It has been found that the most suitable thermoeconomic solution for winter heating and summer cooling of a facility is a vertical underground source heat pump and the most high-performance working fluid that should be used is R32. If the heating is done with a ground source heat pump instead of electrical energy, 4.25 kW of heating energy will be obtained for each kW of electrical energy given for heating, since the costs are COPIP 4.25. Here the earnings increase by around 325%. In other words, heating costs decrease by around 77.5 percent, that is, from one hundred liras to 22.5 liras.

СОВЕРШЕНСТВОВАНИЕ ТЕХНОЛОГИИ ПРОИЗВОДСТВА ТЕКСТУРИРОВАННОЙ МУКИ

The purpose of the study is to reduce energy costs for the production of textured flour by optimizing the technology of its production. Tasks: to develop a constructive and technological scheme for the production of textured flour and evaluate its technical and economic efficiency. The object of research is a technological line for the production of textured flour from wheat grain. Research was carried out at the Engineering Center of the FSBEI HE Krasnoyarsk State Agrarian University. Flour production was carried out according to two technologies, differing in that after cleaning, the grain was either extruded or pre-moistened and softened using a developed and patented grain processing device, and then extruded. The obtained extrudates were ground to obtain a textured flour. Its analysis showed a change in nutritional value. There was an increase in the content of protein, starch and fat by 4.7 %, 1.4 and 29.4 %, respectively, a decrease in the content of sugars – by 13.8 % and fiber – by 22.5 % when using preliminary grain conditionning. Based on the equipment used in production, taking into account all stages of processing of raw materials, an analysis of energy costs for the production of textured flour was carried out. The assessment of energy flows was used to justify the efficiency and feasibility of its production. To assess the energy efficiency of production, the energy income indicator E (MJ/kg) was used, showing the difference between the energy contained in the resulting product and the total energy costs invested in the production of textured flour at the stages of grain cleaning, moisturizing and tempering, extrusion and grinding. The cost of producing textured flour in the technology without pre-moistening and tempering of the grain amounted to 3.72 MJ/kg, with pre-moistening and tempering – 3.73 MJ/kg, at the same time, the energy income in the proposed technology increased and amounted to 8.81 MJ/kg. kg, compared with the original – 8.34 MJ/kg.

Sustainable Concrete Pavements with Blended Cements

Transportation infrastructure management has traditionally focused on the safety and reliability without deliberately incorporating sustainability considerations. Recently, national, state and municipal governing bodies, the engineering community and society as a whole, are realizing the enormous investment of materials, energy, capital, and social costs affiliated with infrastructure system design, construction and maintenance. Approaches to help achieve higher infrastructure sustainability and resiliency include design and construction techniques to provide longer-lasting pavements, use of recycled materials in construction, and increasing the use of supplemental cementitious materials (SCMs) and portland limestone cements in concrete mixture designs. Concrete materials manufactured with blended cements intrinsically reduce greenhouse gas emissions by reducing portland cement usage. In addition, blended cement concretes containing SCMs may substantially increase the pavement service life providing the most cost-effective method to reducing the economic, environmental and societal impacts (triple bottom line) of surface transportation.

Advancements and Challenges in Direct Air Capture Technologies: Energy Intensity, Novel Methods, Economics, and Location Strategies

Direct air capture (DAC) technology is increasingly recognized as a key tool in the pursuit of climate neutrality, enabling the removal of carbon dioxide directly from the atmosphere. Despite its potential, DAC remains in the early stages of development, with most installations limited to pilot or demonstration units. The main barriers to its widespread implementation include high energy demands and significant capture costs. This literature review addresses the most critical research directions related to the development of this technology, focusing on its challenges and prospects for deployment. Particular attention is given to studies aimed at developing new, cost-effective, and efficient sorbents that could significantly reduce the energy intensity and costs of the process. Alternative technologies, such as electrochemical and membrane-based processes, show promise but require further research to overcome limitations, such as sensitivity to oxygen presence or insufficient membrane selectivity. The economic feasibility of DAC remains uncertain, with current estimates subject to significant uncertainty. Governmental and regulatory support will be crucial for the technology’s success. Furthermore, the location of DAC installations should consider factors such as energy availability, options for carbon dioxide storage or utilization, and climatic conditions, which significantly affect process efficiency. This review highlights the necessity for continued research to overcome existing barriers and fully harness the potential of DAC technology.

Hinged Carboxylate in the Artificial Distal Pocket of an Iron Porphyrin Enhances CO2 Electroreduction at Low Overpotential.

To efficiently capture, activate, and transform small molecules, metalloenzymes have evolved to integrate a well-organized pocket around the active metal center. Within this cavity, second coordination sphere functionalities are precisely positioned to optimize the rate, selectivity, and energy cost of catalytic reactions. Inspired by this strategy, an artificial distal pocket defined by a preorganized 3D strap is introduced on an iron-porphyrin catalyst (sc-Fe) for the CO2-to-CO electrocatalytic reduction. Combined electrochemical, kinetic, and computational studies demonstrate that the adequate positioning of a carboxylate/carboxylic group acting in synergy with a trapped water molecule within this distal pocket remarkably enhances the reaction turnover frequency (TOF) by four orders of magnitude compared to the perfluorinated iron-tetraphenylporphyrin catalyst (F20Fe) operating at a similar low overpotential. A proton-coupled electron transfer (PCET) is found to be the key process responsible for the unexpected protonation of the coordinating carboxylate, which, upon CO2 insertion, shifts from the first to the second coordination sphere to play a possible secondary role as a proton relay.