Potential Energy Research Articles

Quantum Algorithms for the Variational Optimization of Correlated Electronic States with Stochastic Reconfiguration and the Linear Method.

Solving the electronic Schrodinger equation for strongly correlated ground states is a long-standing challenge. We present quantum algorithms for the variational optimization of wave functions correlated by products of unitary operators, such as Local Unitary Cluster Jastrow (LUCJ) ansatzes, using stochastic reconfiguration (SR) and the linear method (LM). While an implementation on classical computing hardware would require exponentially growing compute cost, the cost (number of circuits and shots) of our quantum algorithms is polynomial in system size. We find that classical simulations of optimization with the linear method consistently find lower energy solutions than with the L-BFGS-B optimizer across the dissociation curves of the notoriously difficult N2 and C2 dimers; LUCJ predictions of the ground-state energies deviate from exact diagonalization by 1 kcal/mol or less at all points on the potential energy curve. While we do characterize the effect of shot noise on the LM optimization, these noiseless results highlight the critical but often overlooked role that optimization techniques must play in attacking the electronic structure problem (on both classical and quantum hardware), for which even mean-field optimization is formally NP hard. We also discuss the challenge of obtaining smooth curves in these strongly correlated regimes, and propose a number of quantum-friendly solutions ranging from symmetry-projected ansatz forms to a symmetry-constrained optimization algorithm.

Piezoelectric Interaction Analysis and Load‐Deflection Prediction for Nonlinear Bending and Snap‐Through of Post‐Buckled FG Piezoelectric Beams

Revealing the interaction effect in the nonlinear bending of post‐buckled functionally graded (FG) piezoelectric beams promotes the intelligent development of structural design. The nonlinear bending behavior of post‐buckled piezoelectric beams is studied. Based on the displacement field constructed by the refined beam model, the post‐buckling configuration is considered. The nonlinear geometric relationship and the constitutive relationship in the multi‐physical field are modeled. In the framework of the principle of minimum potential energy, the nonlinear static models of post‐buckled FG piezoelectric beams are established. The post‐buckling configuration is obtained by the extended two‐step perturbation method. To obtain the approximate solution for the static problems of the post‐buckled FG piezoelectric beam, the perturbation scheme of electric potential and generalized displacement is proposed. The detailed parametric analysis indicates the interaction mechanism in the nonlinear bending of the post‐buckled FG piezoelectric beam. The snap‐through process and its sensitive interval are further captured. The back propagation neural network (BPNN) technique developed for the nonlinear bending problem of post‐buckled FG piezoelectric beams achieves accurate prediction.

Rydberg-State Double-Well Potentials of Van der Waals Molecules

Recent progress in studies of Rydberg double-well electronic energy states of MeNg (Me = 12-group atom, Ng = noble gas atom) van der Waals (vdW) molecules is presented and analysed. The presentation covers approaches in experimental studies as well as ab initio-calculations of potential energy curves (PECs). The analysis is shown in a broader context of Rydberg states of hetero- and homo-diatomic molecules with PECs possessing complex ‘exotic’ structure. Laser induced fluorescence (LIF) excitation spectra and dispersed emission spectra employed in the spectroscopical characterization of Rydberg states are presented on the background of the diverse spectroscopic methods for their investigations such as laser vaporization–optical resonance (LV-OR), pump-and-probe methods, and polarization labelling spectroscopy. Important and current state-of-the-art applications of Rydberg states with irregular potentials in photoassociation (PA), vibrational and rotational cooling, molecular clocks, frequency standards, and molecular wave-packet interferometry are highlighted.

Complete Active Space Iterative Coupled Cluster Theory.

In this work, we investigate the possibility of improving multireference-driven coupled cluster (CC) approaches with an algorithm that iteratively combines complete active space (CAS) calculations with tailored CC and externally corrected CC. This is accomplished by establishing a feedback loop between the CC and CAS parts of a calculation through a similarity transformation of the Hamiltonian with those CC amplitudes that are not encompassed by the active space. We denote this approach as the complete active space iterative coupled cluster (CASiCC) ansatz. We investigate its efficiency and accuracy in the singles and doubles approximation by studying the prototypical molecules H4, H8, H2O, and N2. Our results demonstrate that CASiCC systematically improves on the single-reference CCSD and the externally corrected CCSD methods across entire potential energy curves while retaining modest computational costs. However, the tailored coupled cluster method shows superior performance in the strong correlation regime, suggesting that its accuracy is based on error compensation. We find that the iterative versions of externally corrected and tailored coupled cluster methods converge to the same results.

Renewable energy: A lever for a sustainable future in Africa

Drawing on documented evidence and an analysis of ongoing projects, this paper elucidates the benefits, challenges, and socio-economic impacts of renewable energy on rural communities in Africa. It further argues that, despite limitations in infrastructure and financing mechanisms, the growing influx of investments in digital technologies presents a pivotal opportunity for sustainable development across the continent. Moreover, the analysis compares how countries such as Senegal and Ethiopia have capitalized on their renewable energy potential through differing regulatory frameworks and financing models.

Harvesting the tuning mechanism of strategic polychrome and white light emission in NapH dye based on excited state intermolecular proton transfer

Organic white light emitting (WLE) materials have sparked a research boom due to their promising applications in display devices, artificial lighting, and molecular sensors. However, the complex luminescence mechanism of full-spectra emitting materials has limited the development of white light materials with high stability and reproducibility. In this study, building on the full-spectra luminescent naphthimide dye (NapH) introduced by Xu et al., (Chin. Chem. Lett. 35(2024), 108348), we report the solvent-sensitive excited state proton transfer (ESPT) properties and white light emission mechanism in NapH. Our theoretical research results show that the ESPT process of NapH molecule is hindered in the low-polar solvent tetrahydrofuran (THF), so only the blue fluorescence from the Enol* is observed in the experiment. In contrast, in polar solvents such as water (H2O) and methanol (MeOH), the formation of strong intermolecular hydrogen bonds successfully initiates the ESPT process, leading to the dual fluorescence observed in experiments, with blue emission from the Enol* and red emission from the Nap anion. In essence, different solvent environments can adjust several emission colors. Furthermore, we theoretically verify that the optimal white light emission can be achieved when the mixed solvent ratio of dimethyl sulfoxide (DMSO) and dioxane (DIOX) is 5:5, which aligns well with experimental results. More importantly, we elucidate the specific WLE mechanism from multiple perspectives, including potential energy curves and electron spectra. Our work not only displays the interaction models of NapH molecule in various solvents, but also reveals the underlying luminescence mechanism, thus providing a valuable theoretical reference for the development and advancement of new white light materials.

Module‐Based Supercapacitors: Potential Energy Storage Solutions for Large‐Scale Photovoltaic Systems

Intermittency is an inherent characteristic of photovoltaic (PV) power generation and results in high ramp rates of the generated power. This article explores the feasibility of integrating supercapacitors at the PV module level, aiming to reduce the power fluctuations of PV systems and control the power ramp rate into the power grid. First, an equivalent circuit model of a single‐phase grid‐connected PV system based on module‐based supercapacitors is proposed, and a power ramp rate control scheme is established. Then, experimental setups for a single‐phase grid‐connected PV system based on module‐based supercapacitors are implemented, and the computational model is verified through experiments. Finally, using the verified computational model and the proposed control scheme, the module‐based supercapacitor sizes for different PV system sizes (PV module, rooftop, small system, large system) that meet specific ramp rate requirements under different ramp rate limits (5, 10, 15% min−1) are compared. Case studies show that large‐scale PV systems with geographical smoothing effects help to reduce the size of module‐based supercapacitors per normalized power of installed PV, providing the possibility for the application of modular supercapacitors as potential energy storage solutions to improve power ramp rate performance in large‐scale PV systems.

Supervised Learning-Based Prediction of Lightning Probability in the Warm Season

The accurate prediction of lightning is crucial for forecasters to respond effectively to its related hazards. The rapid development and confined spatial extent of convective storms, in which lightning frequently occurs, pose considerable challenges for accurately predicting their locations using numerical weather prediction (NWP) models. Lightning occurrence is often prognosed using thermodynamic parameters, convective available potential energy (CAPE), the severe weather threat index (SWEAT), the lifted index (LI), etc. A high-resolution NWP model provides a prediction of these thermodynamic parameters at high spatiotemporal resolution with high accuracy for a few hours. However, a complicated algorithm is required to handle all the useful high-resolution variables from the NWP model. The recently emerging machine learning technique can solve this issue by properly handling these “big data” without any model distributional assumption. In this study, we developed a random forest algorithm for nowcasting and very short-range forecasting (useful for ~6 h), named LightningRF. LightningRF was trained by using lightning occurrence as a response variable and characteristic parameters from the NWP as predictors. It was also applied to analysis and forecast fields, showing a high probability of lightning within the observed lightning regions. This highlights the potential of helping forecasters improve their lightning forecasting skills using real-time probabilistic forecasts from a trained model.

Harnessing the Energy Potential and Value-Added Products from the Treatment of Sugarcane Vinasse: Maximizing Methane Production Through Co-Digestion with Sugarcane Molasses and Enhanced Organic Loading.

This study assessed the impact of organic loading rate (OLR) on methane (CH4) production in the anaerobic co-digestion (AcoD) of sugarcane vinasse and molasses (SVM) (1:1 ratio) within a thermophilic fluidized bed reactor (AFBR). The OLR ranged from 5 to 27.5kg COD.m-3.d-1, with a fixed hydraulic retention time (HRT) of 24h. Organic matter removal varied from 56 to 84%, peaking at an OLR of 5kg COD.m-3.d-1. Maximum CH4 yield (MY) (272.6 mL CH4.g-1CODrem) occurred at an OLR of 7.5kg COD.m-3.d-1, while the highest CH4 production rate (MPR) (4.0L CH4.L-1.d-1) and energy potential (E.P.) (250.5 kJ.d-1) were observed at an OLR of 20kg COD.m-3.d-1. The AFBR exhibited stability across all OLR. At 22.5kg COD.m-3.d-1, a decrease in MY indicated methanogenesis imbalance and inhibitory organic compound accumulation. OLR influenced microbial populations, with Firmicutes and Thermotogota constituting 43.9% at 7.5kg COD.m-3.d-1, and Firmicutes dominating (52.7%) at 27.5kg COD.m-3.d-1. Methanosarcina (38.9%) and hydrogenotrophic Methanothermobacter (37.6%) were the prevalent archaea at 7.5kg COD.m-3.d-1 and 27.5kg COD.m-3.d-1, respectively. Therefore, this study demonstrates that the organic loading rate significantly influences the efficiency of methane production and the stability of microbial communities during the anaerobic co-digestion of sugarcane vinasse and molasses, indicating that optimized conditions can maximize energy yield and maintain methanogenic balance.

Modeling and forecasting biogas production from anaerobic digestion process for sustainable resource energy recovery

Anaerobic digestion (AD) is one of the most extensively accepted processes for organic waste cleanup, and production of both bioenergy and organic fertilizer. Numerous mathematical models have been conceived for modeling the anaerobic process.In this study, a new modified dynamic mathematical model for the simulation of the biochemical and physicochemical processes involved in the AD process for biogas production was proposed. The model was validated, and a sensitivity analysis based on the OAT approach (one-at-a-time) was carried out as a screening technique to identify the most sensitive parameters. The model was developed by updating the bio-chemical framework and including more details concerning the physico-chemical process. The fraction XP was incorporated into the model as a particulate inert product arising from biomass decay (inoculum). New components were included to distinguish between the substrate and inoculum, and a surface-based kinetics was used to model the substrate disintegration. Additionally, the sulfate reduction process and hydrogen sulfide production have been included. The model was validated using data extracted from the literature. The model's ability to generate accurate predictions was testified using statistical metrics. The model exhibited excellent performance in forecasting the parameters related to the biogas process, with measurements falling within a reasonable error margin. The relative absolute error (rAE) and root mean square error (RMSE) were both less than 5 %, indicating a high ability of the current model in comparison with the literature. Additionally, the scatter index (SI) was below 10 %, and the Nash-Sutcliffe efficiency (NES) approached one, which affirms the model's accuracy and reliability. Finally, the model was applied to investigate the performances of the AD of food waste (FW). The findings of this study support the robustness of the developed model and its applicability as a virtual platform to evaluate the efficiency of the AD treatment and to forecast biogas production and its quality, CO2 emission, and energy potential across various organic solid waste types.

Assessment of Energy Recovery Potential in Urban Underground Utility Tunnels: A Case Study

Underground spaces contain abundant geothermal energy, which can be recovered for building ventilation, reducing energy consumption. However, current research lacks a comprehensive quantitative assessment of its energy recovery. This research evaluates the energy recovery potential of the Xingfu Forest Belt Urban Underground Utility Tunnels. Field experiments revealed a 7 °C temperature difference in winter and a 2.5 °C reduction during the summer-to-autumn transition. A computational fluid dynamics (CFD) model was developed to assess the impact of design and operational factors such as air exchange rates on outlet temperatures and heat exchange efficiency. The results indicate that at an air change rate of 0.5 h−1, the tunnel outlet temperature dropped by 10.5 °C. A 200 m tunnel transferred 8.7 × 1010 J of heat over 30 days, and a 6 m × 6 m cross-sectional area achieved 1.1 × 1011 J of total heat transfer. Increasing the air exchange rate and cross-sectional area reduces the inlet–outlet temperature difference while enhancing heat transfer capacity. However, the optimal buried depth should not exceed 8 m due to cost and safety considerations. This study demonstrates the potential of shallow geothermal energy as an eco-friendly and efficient solution for enhancing building ventilation systems.

Energy Efficiency and Sustainability in Food Retail Buildings: Introducing a Novel Assessment Framework

One of the primary global objectives is to decrease building energy consumption to promote energy efficiency and environmental sustainability. The large-scale food retail trade sector accounts for over 15% of total primary energy consumption in Europe, posing a significant challenge to the transition towards green energy. This study proposes a simple method for energy efficiency, environmental sustainability, and cost-saving assessment and improvement in large-scale food retail trade buildings. It aims to analyze the energy and environmental performance of building–plant systems, establishing an interactive network to assess intervention potential for the energy transition. The investigation focuses on the proper selection and analysis of the benefits of retrofit solution implementation, emphasizing potential energy savings in current and future climate change scenarios. Dynamic simulation with the Building Energy Model (BEM) was used to evaluate the impacts of building–plant system retrofit solutions, such as high thermal insulation, photovoltaic (PV) panels, Light Emitting Diode (LED) installation, waste heat recovery, and improvement in refrigeration units. The results show a reduction in annual energy consumption for the PV panel installation by up to 29% and lighting systems with high-quality LED to 60%. Additionally, CO2 emissions can be decreased by up to 41% by combining these two strategies.

Bioinspired surface modification of mussel shells and their application as a biogenic filler in polypropylene composites

This study explores the potential of mussel shells (MS) as biogenic fillers in polymer composites. The chemical composition and crystal structures of MS were characterised. To improve MS filler dispersion and adhesion within a polypropylene (PP) matrix, three surface modification methods were evaluated: polydopamine (PDA) coating, maleic anhydride-grafted polypropylene (MAPP) modification, and PDA/MAPP co-modification. The PDA coating, inspired by the adhesive properties of mussel foot proteins, successfully functionalized the MS surface, as confirmed by X-ray photoelectron spectroscopy (XPS). Thermodynamic analysis, based on contact angle measurements, revealed that MAPP and PDA/MAPP modifications reduced surface energies and potential energy differences. These changes enhanced filler dispersion and interfacial bonding by increasing hydrophobicity and reducing agglomeration in the PP matrix. Consequently, PP composites with 20% PDA/MAPP-modified MS fillers exhibited a 2.9% increase in tensile strength and a 7.5% increase in flexural strength compared to neat PP. Scanning electron microscopy (SEM) also showed reduced filler-matrix debonding and fewer voids. The proposed mechanism attributes these macroscopic property enhancements to the ability of the PDA coating to facilitate chemical and hydrogen bonding between MS fillers and MAPP.

Analytical potential energy functions for CO in its ground and excited electronic states.

Accurate functions to analytically represent the potential energy interactions of CO diatomic system in , , and electronic states are proposed. The new functions depend upon only four parameters directly obtained from experimental data, without any fitting procedure. These functions have been developed from the modified generalized potential proposed by Araújo and Ballester. The function for the electronic state represents a significant improvement to the previously proposed model. To quantify the accuracy of the potential energy functions, the Lippincont test is used. The novel potential was also compared with the classical Morse potential and with the recently proposed Improved Generalized Pöschl-Teller potential. Furthermore, the main spectroscopic constants and vibrational energy levels are calculated and compared for all potentials. The present results agree excellently with the experiment Rydberg-Klein-Rees (RKR) potentials. The rovibrational energy levels of the proposed diatomic potentials were asserted by solving radial the Schrödinger equation of the nuclear motion with the aid of the LEVEL program.

Energy dissipation and power flow analysis based on acoustic black hole laminated beams

To enhance the understanding of the dynamic performance of composite beams, assessing their energy accumulation and dissipation capabilities is crucial. The purpose of this paper is to establish a dynamic analysis model of acoustic black hole (ABH) laminated beams based on the Timoshenko beam theory and isogeometric method and to analyze structural intensity and power flow. The vibration governing equation for the ABH laminated beam is derived by calculating its potential and kinetic energy. The convergence of the results is confirmed through multiple mesh refinements, while the model's accuracy and applicability are substantiated through comparisons with the literature, traditional finite element simulations, and experimental data. This investigation into the power flow and structural intensity under varying parameter conditions elucidates the energy transfer mechanisms within ABH laminated beams, offering valuable insights for the deployment of ABH technologies in practical engineering applications.

A new pilot wave reinterpretation for quantum AI systems

A new nonlinear Schrödinger equation is derived, which describes a cluster of bosons interacting via the strongest force. Its numerical solution points out that the cluster is stable only if the number of particles is exactly 131. The relevant kinetic and potential energies of the particles are also calculated and interrelated via the virial theorem. The developed concept is also applied to electrostatic forces.

Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies.

Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principles accuracy - even with the aid of machine learning potentials - is a challenge that requires sub-kJ mol-1 accuracy in the potential energy surface and finite-temperature sampling. We present an accurate and data-efficient protocol for training machine learning interatomic potentials by fine-tuning the foundational MACE-MP-0 model and showcase its capabilities on sublimation enthalpies and physical properties of ice polymorphs. Our approach requires only a few tens of training structures to achieve sub-kJ mol-1 accuracy in the sublimation enthalpies and sub-1% error in densities at finite temperature and pressure. Exploiting this data efficiency, we perform preliminary NPT simulations of hexagonal ice at the random phase approximation level and demonstrate a good agreement with experiments. Our results show promise for finite-temperature modelling of molecular crystals with the accuracy of correlated electronic structure theory methods.

Spray combustion characteristics of boron slurry fuel in high particle loading conditions

Enhancing the energy density of hydrocarbon fuels is indeed crucial, especially in the context of addressing energy needs. Incorporating energetic particles as additives into conventional liquid fuels garners increased attention due to its potential to enhance energy density. Due to its high energy potential, boron is the most promising additive in several energetic particles. However, the study focusing on the boron-loaded slurry fuels are limited. Therefore, the present work focuses on the effect of boron loading on the spray combustion of boron-loaded slurry fuel, especially at higher loadings (10%, 20% & 30% boron + Jet A-1 fuel) using a swirl-stabilized spray combustor. The research scrutinizes the feed and exhaust particles' surface morphology, chemical composition and active boron content through diverse particle characterization techniques. Furthermore, the study investigates the influence of boron loading on the combustion characteristics of slurry fuel using methods such as colour flame imaging, spectroscopy, and BO2* Chemiluminescence imaging. The impact of boron loading on the positive thermal contribution of the slurry was examined by analyzing temperature measurements at various locations within the combustor. The BO2* chemiluminescence imaging and spectroscopy results indicate that the intensity of BO2 emission increases with a rise in particle loading. The temperature results reveal an increase in temperature compared to pure Jet A-1 for all the boron-loaded cases. In particular, JB10 and JB20 achieved nearly 90% and 85% of the theoretical temperature increase. The diffractogram and thermogravimetric results of the burnt particles collected show that the particles were burnt thoroughly for the boron-loaded cases.

Higher Onshore Wind Energy Potentials Revealed by Kilometer‐Scale Atmospheric Modeling

Higher Onshore Wind Energy Potentials Revealed by Kilometer‐Scale Atmospheric Modeling

S@FeS2 Core-Shell Cathode Nanomaterial for Preventing Polysulfides Shuttling and Forming Solid Electrolyte Interphase in High-Rate Li-S Batteries.

Lithium-sulfur (Li-S) battery is a potential next-generation energy storage technology over lithium-ion batteries for high capacity, cost-effective, and environmentally friendly solutions. However, several issues including polysulfides shuttle, low conductivity and limited rate-capability have hampered its practical application. Herein, a new class of cathode active material with perfect core-shell structure is reported, in which sulfur is fully encapsulated by conductivity-enhancing FeS2 (named as S@FeS2), for high-rate application. Surface-stabilized S@FeS2 cathode exhibits a stable cycling performance under 2 - 20 times higher rates (1-2 C, charged in 30-60min) than standard rates (e.g., 0.1-0.5C, charged in 2-10h), without polysulfides shuttle event. Surface analysis results reveal the unprecedented formation of a stable solid electrolyte interphase (SEI) layer on S@FeS2 cathode, which is distinguished from other sulfur-based cathodes that are not able to form the SEI layer. The data suggest that the prevention of polysulfides shuttling is owing to the surface protection effect of FeS2 shell and the SEI layer formation overlying core-shell S@FeS2. This unique and potential material concept proposed in the present study will give insight into designing a prospective fast charging Li-S battery.