Energy advances最新文献

A key challenge in thin-film photovoltaics is achieving selective carrier collection that minimizes recombination losses while maintaining efficient charge extraction. This study presents a theoretical analysis of how reducing junction contact area can enhance the open-circuit voltage (V _OC) and the power conversion efficiency (PCE) in thin-film solar cells. Using a zinc-phosphide (Zn₃P₂) -based heterojunction as a model, we simulate the effect of geometrically minimizing contact via silicon-dioxide (SiO₂) layers with patterned holes. The smaller the contact area, the lower the reverse saturation current, which results in a significant increase in the V _OC up to 100 mV. However, the reduced contact area also increases the series resistance, thereby limiting the gain in PCE. This approach is especially effective with non-absorbing highly-doped transport layers, such as titanium-dioxide (TiO₂) (PCE gain up to 1.45%). This work underscores the importance of balancing reduced recombination with parasitic resistance and current crowding for optimal performance.

Proton exchange membrane fuel cells (PEMFCs) offer high efficiency, rapid refueling, and zero-carbon operation, but their commercialization is constrained by the cost of platinum-based oxygen reduction catalysts. Transition metal-nitrogen-carbon materials, particularly Fe-N-C, are promising platinum-free alternatives, although their activity and durability still require improvement. Ammonia is often introduced during synthesis to enhance nitrogen incorporation, but safer nitrogen sources are desirable to simplify processing and reduce associated hazards. Here, we investigate the use of urea, melamine, cyanoguanidine, and nicarbazin as nitrogen precursors during the thermal activation of ZIF-8-derived Fe-N-C catalysts, aiming to promote nitrogen incorporation and active-site formation without the use of ammonia. Structural analysis reveals that the use of urea, melamine, and cyanoguanidine during heat-treatment largely preserve the ZIF-8 morphology, while nicarbazin leads to the formation carbonaceous flakes. The high surface area of ZIF-8 (∼1600 m² g^-1) is partially retained after pyrolysis (∼1200 m² g^-1). X-ray photoelectron spectroscopy reveals enhanced nitrogen content and increased Fe-N _x species, most notably in urea-activated samples. Electrochemical testing in an acidic electrolyte confirms higher onset potentials and mass activities for urea- and melamine-activated catalysts compared to the control, with consistent trends observed in rotating disk electrode and single-cell PEMFC measurements. Despite their lower intrinsic activity compared to Pt/C, Fe-N-C catalysts exhibit enhanced ORR kinetics when secondary nitrogen precursors are used during synthesis. Despite elevated peroxide yields predicted from rotating ring disk electrode measurements, ion chromatography indicates a modest increase in ionomer degradation compared to Pt/C during fuel cell tests. Overall, nitrogen-rich molecular precursors enhance Fe-N-C activity while providing a safer and scalable pathway for nitrogen doping, advancing the development of cost-effective non-platinum catalysts for fuel cells.

Hydrogen is currently produced predominantly through fossil fuel reforming, which accounts for approximately 3% of annual global CO₂ emissions. To reduce the carbon intensity of hydrogen production, several low-carbon alternatives have been proposed, including biogas reforming and electrified steam methane reforming (e-SMR). Biogas benefits from its biogenic origin, leading to near net-zero carbon emissions, while e-SMR replaces the natural gas combustion used for reactor heating in conventional SMR with electrical heating. This modeling study performs a dynamic techno-economic assessment of these processes in comparison with state-of-the-art steam methane reforming (SMR) and auto-thermal reforming (ATR), evaluating the impact of implementing carbon capture and permanent storage (CCS). The analysis incorporates time-resolved and seasonal variations of real electricity prices in French, Swiss and German scenarios, employed as reference cases for low and high electricity grid footprints. Large-scale SMR and ATR plants exhibit the highest process efficiency (79-81%), which remains stable when CCS is implemented (77-81%). Lower efficiencies are observed for biogas reforming (56-67% with base case and 65-69% with CCS) and e-SMR (59% with base case and 71% with CCS) due to their smaller scale and the presence of CO₂ in the feed. CCS significantly reduces carbon footprints: from 8.6-8.7 to 1.2-3.4 kg_CO2 kg_H2 ^-1 for SMR and ATR and from 0.2-1.0 to -10 to -4 kg_CO2 kg_H2 ^-1 for biogas reforming. e-SMR emissions (from 6-18 to 0.3-10 kg_CO2 kg_H2 ^-1 with CCS) depend strongly on the electricity mix. The possible presence of carbon credits makes the application of CCS economically beneficial for SMR and ATR (H₂ cost ranging from 1.6 to 1.3 € per kg_H2 ) and for biogas reforming (from 3.7 to 3.5 € per kg_H2 ). e-SMR competitiveness is highly electricity-price-dependent and benefits from CCS regardless of carbon credits, performing best in France (3.7 to 2.6 € per kg_H2 with CCS) and worst in Switzerland (4.2 to 3.1 € per kg_H2 with CCS). Intermittent operation to exploit low-cost electricity may further reduce e-SMR costs by 0.1-0.4 € per kg_H2 .

Sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs) are potential alternatives to lithium-ion batteries. However, knowledge about the solid electrolyte interphase (SEI) in SIBs and PIBs is still limited. Here, the formation and stability of SEI in SIBs and PIBs are compared to understand ageing related to SEI characteristics in electrolyte solutions based on 1 M KPF₆ or 1 M NaPF₆ in ethylene carbonate:diethyl carbonate (EC:DEC). Galvanostatic cycling coupled with pause testing was used to quantify the amount of charge consumed for electrolyte reduction for initial SEI formation and for SEI reformation required due to the dissolution of SEI. Proton nuclear magnetic resonance (¹H-NMR) spectroscopy was used to reveal changes in the composition of electrolyte solutions due to SEI formation and dissolution. ¹H-NMR findings were supported by X-ray photoelectron spectroscopy (XPS) analysis showing the evolution of SEI composition during a 50 h pause.

The pursuit of efficient photocatalytic systems for solar light-driven hydrogen evolution (HER) drives the search for novel semiconductor materials capable of forming advanced heterojunctions. Herein, we report the first synthesis of a non-stoichiometric, Cu-substituted freudenbergite (Cu-FDT) via a facile co-precipitation method. Comprehensive characterization (PXRD, XPS, RAMAN, HR-TEM/STEM-EDS) confirms the formation of a phase-pure freudenbergite structure with nanoplatelet morphology and mixed-valent Ti⁴⁺/Ti³⁺. Electronic characterization (UV-DRS, Mott–Schottky) reveals a bandgap (E_g) value of 2.95 eV enabling extended solar light harvesting and a band alignment perfectly suited for HER, coupled with a strong oxidation potential for the valence band (VB). A modified synthetic approach, involving the addition of water during Cu-FDT peptization, enabled the in situ fabrication of a Cu-FDT/TiO₂ heterojunction. The TiO₂ phase (anatase, mixed phase, rutile) was tuned by varying the calcination temperature. Photocatalytic performance toward HER was evaluated for all composites to elucidate the effect of excess surface Na⁺ on photocatalytic activity. The optimal catalyst, a 1 wt% Pt-loaded, desodiated Cu-FDT/anatase heterojunction (1 wt% Pt@Cu-FDT_A deNa), achieved a high hydrogen production rate of 7183 µmol g⁻¹ h⁻¹ under solar irradiation. To correlate electrochemical properties with HER performance, EIS, photocurrent density, and LSV measurements toward the HER were conducted on anatase TiO₂, Cu-FDT and their heterojunction, comparing Na⁺-rich and desodiated surfaces. Mott–Schottky analysis confirmed a direct Z-scheme charge transfer mechanism, enabling superior charge separation while preserving strong redox potentials. Furthermore, the high oxidative power of the heterojunction was further demonstrated by the near-complete mineralization of 5-hydroxymethyl furfural (5-HMF), with only minimal yields of partial oxidation products 5-hydroxymethyl-2-furancarboxylic acid (HMFCA) and 2,5-diformylfuran (DFF). These findings highlight the potential of this novel photocatalyst to simultaneously drive HER and challenging oxidation reactions, thus coupling renewable H₂ evolution with the potent oxidative power of photogenerated holes (h⁺) in the VB of Cu-FDT.

Despite many recent advances, overpotentials remain high for anion exchange membrane water electrolyzers (AEMWEs). Voltage breakdown analyses (VBA) can help decouple the origins of overpotentials and facilitate design decisions to improve cell performance, but studies investigating how to adapt and apply VBA to AEMWEs are lacking. Specifically, catalyst layer resistances and their contributions to overpotentials are not consistently quantified in water electrolysis and are rarely quantified for AEMWEs. This work presents a systematic methodology for VBA tailored to AEMWEs, including an approach to Tafel analysis in the absence of a reference electrode and under conditions where both the oxygen evolution reaction and hydrogen evolution reaction exhibit significant overpotentials. Catalyst layer resistance contributions are diagnosed via changes in the catalyst layer thickness, transport layer porosity, ionomer content, and electrolyte concentration. In this study, we explain discrepancies between inherent catalytic kinetics and device level performance and identify catalyst layer design strategies to reduce catalyst layer resistances.

The pursuit of efficient and stable lead-free perovskite solar cells (PSCs) is critical for sustainable photovoltaic technologies. In this work, we systematically investigated Sr₃PBr₃-based PSCs incorporating five different copper-based hole transport layers (HTLs)—Cu₂O, CuI, CuSbS₂, CuSCN, and Cu₂BaSnS₄ (CBTS)—using SCAPS-1D simulations. The device configuration FTO/SnS₂/Sr₃PBr₃/HTL/Au was optimized to evaluate the impact of HTL selection, absorber thickness, doping concentration, defect density, series resistance, and temperature on photovoltaic performance. The results demonstrate that the HTL choice strongly governs charge extraction, interfacial recombination, and stability. Among the candidates, CBTS exhibited the highest efficiency, achieving a power conversion efficiency (PCE) of 30.78% with an open-circuit voltage (V_OC) of 1.32 V, a short-circuit current density (J_SC) of 26.82 mA cm⁻², and a fill factor (FF) of 87.05%. Machine learning (ML) models trained on simulation datasets provided predictive accuracies above 99.6% and, through SHAP (SHapley Additive exPlanations) analysis, revealed that acceptor density and defect density are the most influential parameters controlling device performance. This combined simulation–ML framework establishes CBTS as a highly promising non-toxic HTL and provides actionable insights for the design of stable, high-efficiency lead-free PSCs.

The hydrogen evolution reaction is a key reaction in the field of sustainable energy, and platinum is recognised as a state-of-the-art catalyst for this reaction. The cost and scarcity of platinum drive us to look for low-cost and effective alternatives. Among the various metal nitrides, tungsten nitride has been less explored as an HER electrocatalyst despite its stability under extreme pH conditions. WN–carbon composites were synthesised using urea as a nitrogen precursor, and the synthesised WN/NC catalyst exhibits a lower HER overpotential of 200 mV in an acidic medium and 230 mV in an alkaline medium. The mass activity was estimated as 24.2 A g⁻¹ in 0.5 M H₂SO₄, which is much higher than the mass activity in 1 M KOH electrolyte (13.5 A g⁻¹). A homemade water electrolysis system demonstrates that the WN–carbon composite-coated carbon electrode exhibits hydrogen evolution up to 770 mL min⁻¹ g_cat⁻¹ at a constant current density of 100 mA cm⁻². The results demonstrate that the WN with an appropriate carbon support may be a promising alternative to platinum-based electrocatalysts for the hydrogen evolution reaction.

Photovoltaic (PV) systems are susceptible to different types of faults, such as electrical, physical, and environmental issues, which can significantly impact power generation and system reliability. Physical faults, such as cracks, delamination, shading, dirt accumulation, and temperature fluctuations, can reduce module efficiency by altering irradiance levels. To address these challenges, accurate and timely fault detection is essential for ensuring optimal PV system performance and longevity. In this work, we propose a novel machine learning (ML) approach for fault detection using unlabeled electroluminescence (EL) images of PV panels. First, we label the dataset through k-means clustering, applied to features extracted using transfer learning (TL) from a pre-trained VGG-16 model's convolutional and pooling layers. k-Means clustering categorizes the images into three classes based on Silhouette scores, with all healthy panels grouped together. We employ Principal component analysis (PCA) to reduce dimensionality, revealing that 64 principal components account for 95% of the variance in the data. Finally, we train and evaluate classical ML models, including random forest (RF) for binary classification and logistic regression (LR) for three-class classification, achieving accuracies of 97.54% and 89.44%, respectively. We empirically demonstrate that data augmentation further improves the performance of the three-class classification, with RF emerging as the best classifier at 91.5% accuracy. Additionally, we note that the convolutional neural network (CNN) model, which is comparatively lightweight and computationally efficient, saw an increase in accuracy from 98% to 99.5% with data augmentation for binary classification, while the semi-supervised learning approach for the three-class problem achieved an average accuracy of 92.25%. By combining TL, k-means clustering, and data augmentation, our proposed approach enhances fault detection accuracy, reduces reliance on manual labeling, and improves PV system reliability. The proposed method advances automated fault detection techniques and supports the broader adoption of renewable energy systems.

Perovskite-structured bismuth ferrite (BiFeO₃, BFO) possesses considerable promise as a pseudocapacitive material due to its enhanced theoretical capacitance. Nevertheless, its use is constrained by low electrical conductance and limited ion diffusion rates. To address these challenges, a ternary nanomaterial was constructed by integrating bismuth ferrite (BFO) with molybdenum disulfide (MoS₂) and multiwalled carbon nanotubes, resulting in a BiFeO₃/MoS₂@MWCNT hybrid electrode architecture specifically engineered for asymmetric supercapacitor devices. The inclusion of MoS₂ introduces numerous reactive sites for faradaic processes, while MWCNTs enhance the overall conductive and architectural properties of the hybrid matrix. Electrochemical testing revealed that the composite electrode achieves a specific capacitance of 1765 F g⁻¹ at 1 A g⁻¹ while exhibiting consistent performance across multiple scan rates. Assembled into a full ASC device using AC as the anode, the system delivers an impressive specific energy of 65.7 Wh kg⁻¹ at the rate of 802.7 W kg⁻¹. Moreover, a retention of 96.7% was observed after 10 k cycles. The superior electrochemical behaviour is owing to the combined effect of BiFeO₃, MoS₂, and MWCNTs, facilitating efficient charge transfer and stable ion transport pathways. This investigation reveals a promising technique for designing advanced composite electrodes for high-efficiency energy storage applications.