Understanding the molecular-scale mechanisms of atmospheric new particle formation (NPF) is critical for accurately modeling aerosol-climate interactions. Maleic acid (MA), a dicarboxylic acid ubiquitous in atmospheric particulate matter, may participate in initial cluster formation. This computational study evaluates the atmospheric relevance of MA–water (W) binary clusters, MA(W)n (n = 1–8), using density functional theory (DFT). Global minimum geometries, obtained at the ωB97X-D/6-311++G(3df,3pd) level, were used to calculate binding free energies. Thermodynamic population analysis indicates a sharp decrease in cluster concentration with increasing cluster size and relative humidity (RH); only the smallest clusters (n = 1–3) maintain atmospherically relevant concentrations at moderate RH. Kinetically, evaporation rates dominate over collision rates, significantly hindering persistent cluster growth. Although the calculated cluster radiative forcing efficiencies (REs) increase with hydration (0.21–0.84 Wm−2ppbv−1), these values are substantially offset by concomitant Rayleigh scattering activity. Collectively, these results suggest that binary MA-W clusters are unlikely to be stable nucleation precursors, with the MA(W)2 cluster being particularly emphasized as a kinetic bottleneck.

Succinic acid (SuA) is one of the most abundant dicarboxylic acids in atmospheric particulate matter and has been implicated in new particle formation (NPF). In this study, the structural, thermodynamic, concentration, kinetic, and radiative properties of binary SuA–water (SuA–Wₙ) and SuA–ammonia (SuA–Aₙ) clusters, as well as ternary SuA–A–Wn and SuA–2A–Wₙ clusters (n = 1–5), were investigated using the ωB97X–D/6–311++G(d,p) level of theory. Hydration and ammonia incorporation stabilize the clusters through extensive hydrogen bonding, with no evidence of proton transfer. Ammonia enhances cluster stability more effectively than water alone, as mixed ammonia–containing clusters exhibit the most favorable thermodynamic properties. Smaller hydrates (n = 1–2) dominate under all relative humidity (RH) conditions, although increasing RH shifts the population toward higher hydration states. Despite their greater thermodynamic stability, ammonia–containing clusters occur at significantly lower atmospheric concentrations than hydrated SuA clusters. Kinetic analysis indicates that cluster growth is accompanied by increasing evaporation tendencies, limiting atmospheric persistence. Radiative forcing efficiencies (REs) are positive across all clusters and increase with cluster size, reaching their highest values for mixed ammonia–water clusters. Rayleigh scattering intensities also increase significantly with hydration, partially offsetting the positive REs and moderating the overall warming potential of these transient clusters. These findings imply that such clusters are unlikely to persist in atmospheric populations under equilibrium conditions.

The relevance of aminomethylphosphonic acid (AMPA) and sulfuric acid (SA) in ternary nucleation with n = 0-4 water (W) and n = 0-3 ammonia (A) molecules is assessed. AMPA is a main metabolite of glyphosate, a major herbicide and insecticide constituent. Clusters AMPA(SA) (W)n = 1_ 4 and AMPA(SA)(A)n= 1_3 are generated using the ABCluster algorithm, optimized at omega B97X-D/6-31++G(2df,2pd). RDG analysis reveals H-bonds, van der Waals, and steric interactions. Regarding thermochemistry, cluster formation is exothermic: adding the first water releases up to 159.9 kJ/mol (73.2 kJ/mol free energy); adding first ammonia releases 195.4 kJ/ mol (107.7 kJ/mol free energy). AMPA(SA)(W)2 and AMPA(SA)(A)1 show the highest atmospheric concentrations (1.69 & times; 107 and 3.18 & times; 109 molecules/cm3, respectively). Concentrations are consistent and decreases with cluster size but increases with relative humidity (RH) and [SA]/ [A]. At 100% RH, all clusters are realizable (>= 1 molecule/cm3); at 20% RH, only n = 0-2 clusters are feasible. Evaporation rates are negligible for AMPA and even lower for SA compared to W and A, predicting long cluster lifetimes. In addition, the trihydrate and mono-ammoniate clusters show the highest relative abundance owing to their lowest single W/A evaporation rates. They are 2-5 orders of magnitude lower than the other clusters. Radiative forcing efficiency (RE) increases with cluster size, indicating climate warming, partially offset by Rayleigh scattering. Overall, a net warming effect is suggested based on RE and exothermic formation.

Charge transport and recombination in organic photovoltaic (OPV) devices are strongly governed by the nanoscale morphology and the energetic disorder, yet their coupled impact remains insufficiently quantified. Here, we employ a kinetic Monte Carlo framework with explicitly resolved donor-acceptor morphologies generated via an Ising-Kawasaki phase-separation model to investigate the morphology dependent charge dynamics in bulk heterojunction OPVs. The systematic variation of domain size, interfacial mixing, and energetic disorder reveals clear quantitative trends in carrier mobility, lifetime, and recombination kinetics. The simulations show that excessively fine morphologies (<10 nm domains) enhance recombination through increased interfacial encounter rates, while overly coarse morphologies reduce the carrier extraction due to the disrupted percolation pathways. The optimal charge transport emerges for the intermediate domain sizes (approximate to 15-25 nm) with continuous percolation networks. The energetic disorder is found to modulate the recombination indirectly by increasing the carrier localization and the spatial separation, leading to delayed recombination dynamics rather than uniform suppression. The transient carrier decay exhibits a non-monotonic behaviour arising from the morphology induced trapping and detrapping within the percolating networks. These results establish a direct physical connection between the morphology descriptors, the energetic disorder, and the macroscopic device-relevant observables. The findings provide design guidelines for morphology optimization in OPVs and highlight the relevance of controlled disorder in emerging non-fullerene and tandem OPV architectures.

Accurate wind speed estimation is required for integrating wind energy into Cameroon’s hydropower-dependent national grid. However, Cameroon’s diverse climatic zones present significant challenges for modelling. This study develops and evaluates a Hybrid Improved Genetic Algorithm-Support Vector Machine (IGA-SVM) framework for wind speed prediction across five climatic regions, with subsequent conversion to wind power density (WPD). Using 40 years of NASA’ Prediction of Worldwide Energy Resources (POWER) meteorological data (temperature, relative humidity, atmospheric pressure, rainfall, wind direction, snowfall, wind speed, and snow depth), the Hybrid IGA-SVM eliminates manual hyperparameter tuning by optimizing the SVM’s box constraint (C), kernel scale (γ), and epsilon-insensitivity (ε), independently for each zone. The model achieves R 2 values from 0.7956 to 0.8634 across all terrains. The Adamawa Plateau shows the highest stability and predictability, while the Sudano-Sahelian zone shows the greatest wind power density despite seasonal volatility. However, none of the zones reached the National Renewable Energy Laboratory (NREL) Class 3 thresholds (300–400 W/m 2 ); the highest wind power density (WPD) (81 W/m 2 in the Western Highlands) falls into Class 2, indicating suitability for small to medium scale or hybrid applications. The proposed model can serve as a preliminary screening tool for regional energy planning in Cameroon. All recommendations require on-site validation and economic analysis (e.g., levelized cost of energy (LCOE)) before investment decisions. Importantly, all zone-specific recommendations in this paper are based exclusively on technical metrics (R 2 , Root Mean Square Error (RMSE), wind speed, and wind power density); no economic analysis (levelized cost of energy, net present value, or payback period) were performed, and thus these recommendations should be interpreted as pre-feasibility guidance for prioritizing zones for further study, and not as final investment advice.

Eskom, South Africa’s main utility company, faces significant challenges in meeting the high energy demand of a growing population and increasing industrialization, largely due to aging coal power generation plants. Recent investments in renewable energy resources to accelerate the transition away from fossil fuels also encounter significant challenges, particularly due to the impacts of local weather variability on renewable energy output. To help improve Eskom’s grid management under weather uncertainty, we developed machine learning and Deep learning models to predict the short-term impacts of weather variability on Eskom’s renewable-to-grid integration at an hourly timescale, using data from ERA5 and Eskom. A comparative analysis between the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Artificial Neural Network (ANN) shows the superiority of LSTM in predicting renewable energy generation under weather uncertainty, as they can capture autocorrelation and temporal patterns more effectively. We also investigate the impact of seasonality on Eskom’s renewable generation capacity. Seasonal analysis reveals a complementary relationship between wind and solar: when wind power output is low, solar output is high, and vice versa, which improves grid stability when both sources are combined. Future projections using CMIP6 under two emission scenarios (SSP2-4.5 and SSP5-8.5) reveal that, under moderate emission scenarios, we expect a strong increase in both solar radiation and wind speed compared to high emission scenarios, suggesting renewable energy resources are equally impacted by anthropogenic climate change. This work is very important as it can help Eskom to better manage its grid, avoid unforeseen power failures and load shedding, and support South Africa’s transition to a low-carbon energy future.

This work explores the impact of cyano-substitutions on the energy storage and UV-vis spectra of a long half-life photosensitive hydrazone (HDZ) molecular system. The relevance of this study lies in the investigation of properties such as energy storage density, solar spectrum matching, and minimal spectral overlap in the context of enhancing solar energy storage technologies. The results indicate that cyano-substituted systems 4-HDZ-CN, 4-HDZ-(CN)2, and 5-HDZ-(CN)2, all featuring substitution at the phenyl group, significantly enhance energy storage density and UV-vis spectra compared to the parent HDZ system, especially in toluene. The cyano-substitutions were found to lower the highest occupied molecular orbital energy levels while raising the lowest unoccupied molecular orbital energy levels, allowing for efficient electron transfer and making these systems suitable candidates for photovoltaic applications. These findings are relevant for advancing solar energy storage technologies and photovoltaic applications. Computational modeling was performed using density functional theory (DFT) at the M062-X/6-31++G(2d,2p) and MN15/6-31++G(2d,2p) levels of theory to determine energy storage density. Time-dependent DFT (TD-DFT) was employed using CAM-B3LYP/6-31++G(2d,2p) to assess the UV-vis spectra, analyze solar spectrum matching, and evaluate the spectral overlap of the molecular structures and their photoisomers, and study their optoelectronic properties. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

This paper investigates the influence of different parameters on the open circuit voltage of an organic solar cell (OSC) and how the open circuit voltage impacts the cell's power conversion efficiency. These parameters include temperature, light intensity, recombination, charge carrier density, charge carrier mobility ratio, and the reverse saturation current. Organic solar cells' power conversion efficiency is still far from ideal and is currently about 20 %. In the approach, mathematical expressions governing these parameters are established and simulations are then performed in which all other parameters are held at their optimal values and one parameter of interest is varied within a predetermined range. It is shown that the open circuit voltage (Voc) can theoretically reach a value of about 2.34 V if the following parameters are maintained optimal: light intensity, charge-carrier density (1 × 1018cm-3), charge carrier mobility ratio (10) and cell temperature (320 K). It is shown that the open circuit voltage (Voc) is negatively impacted by recombination (up to 30 Ω). Lastly, the power conversion efficiency is predicted to be 20 % at 0.63 V and can reach a theoretical value of 37 % at a Voc of 1.0 V, at a power intensity input of 6.578 w/m2, and a fill factor of 0.89 (max for silicon).

Aminomethylphosphonic acid (AMPA) is the main metabolite of glyphosate and phosphonate, the major constituents of herbicides and insecticides used nowadays in modern agriculture and treatment of environmental refuge and sewage. Through these activities, AMPA is released into the atmosphere which can result in water molecule adsorption around AMPA. The DFT method through the APF-D/6–31++G(d,p) was used throughout the work to cluster one to ten water molecules around AMPA and to find their concentrations in atmosphere along with climate forcing. It comes to light that, the binding energies of the complexes AMPA(H2O)n = 1–10 increase upon addition of H2O. The binding energy (ΔE) per H2O is approximatively -55.7 kJ/mol for n = 1 – 5 and -52.7 kJ/mol for n = 6 – 10. Likewise, the Gibbs free energy per H2O averages within the same ranges at -19.0 and -13.5 kJ/mol, respectively. Thus, AMPA easily forms clusters with water molecules in an exothermic reaction. This happens with high cluster concentrations and high evaporation rate constant. The concentrations, [AMPA(H2O)n], show that AMPA forms more complexes with water at higher relative humidity (saturated air) than lower relative humidity (moderate or dry air). However, the significant drop in the concentrations at n > 5, shows that the stability of the complexes reduces with cluster size. The evaporation rates of a single water evaporation pathway of AMPA(H2O)n are large enough thereby showing that binary clusters AMPA – water easily evaporate in the atmosphere. The presence of clusters, AMPA(H2O)n, in the atmosphere can contribute greatly to the atmospheric puzzles of global warming and climate change. This is supported by the estimates of radiative forcing efficiencies of AMPA(H2O)n.

To implement the European Union (EU)-Africa Green Energy Initiative in Cameroon to boost the renewable energy sector, we model the performance of a 500 W monocrystalline solar panel in major cities of Cameroon located in different climatic zones to select the best location for the installation of a solar farm. We also evaluate the contribution of seasonal and weather variability to the amount and stability of power generated by the panel using the artificial neural network (ANN). The ANN model was used to train and test the ERA5 hourly data for Bamenda. The model was then used to estimate Photovoltaic (PV) output in Douala, Yaounde, Ngaoundere, Garoua, and Maroua with a mean absolute error of 4.109 x 10(-5), 4.699 x 10(-5), 3.563 x 10(-5), 3.106 x 10(-5), and 3.083 x 10(-5) kW, respectively. The results show that the ANN can capture the influence of weather variability on the generated output power. Cloud cover and rainfall are found to negatively affect the amount and stability of generated power in the lower latitude cities of Douala and Yaounde compared to the northern cities, with these effects being stronger in the rainy season than in the dry season. Garoua followed by Maroua are proving to be the best locations for installing a solar park in terms of the amount and stability of electricity generated throughout the year. The Cameroonian government, its EU partners, and other stakeholders involved in the development of solar energy in the country will be able to use the results of this study for better decision-making.

This paper studies the interplay between charge carrier mobility and the related recombination processes exhibited within a bulk heterojunction-disordered hopping organic solar cell, using drift-diffusion simulations. The investigation focuses on the recombination order, the current-voltage properties and the charge carrier mobility’s active involvement in the recombination processes within an organic solar cell. The outcome of the investigation based on the drift diffusion simulation highlights the fact that the recombination characteristics are altered by charge carrier mobility. There exists a normalised mobility, which averages the progression of slow to fast charge carriers transforming the electrons and holes mobilities into an optimal mobility, which significantly increases the efficiency for a variety of bulk heterojunction structure types by significantly lowering the extent of recombination.

This paper presents the load-dependent power loss in a wind turbine gearbox under real-time operating wind speed for three different oil formulations. The gear power loss was determined using mathematical models from the values of gear loss factors and specific film thickness experimentally determined by other researchers. The bearing power loss was determined using the new SKF calibrated model. Wind data from Bafoussam, a town in Cameroon was used to validate the model. A back propagation neural network with different numbers of hidden neurons was designed for power loss modeling and prediction. The achieved results reveal that the load-dependent power loss in a wind turbine gearbox is greatly influenced by wind speed and oil type. Finally, it is shown that the predictive performance of the neural network is also influenced by the number of neurons in the hidden layer.

This chapter explores the different ways in which solar radiation (SR) can be quantified for use in photovoltaic applications. Some solar radiation models that incorporate different combinations of parameters are presented. The parameters mostly used include the clearness index (Kt), the sunshine fraction (SF), cloud cover (CC) and air mass (m). Some of the models are linear while others are nonlinear. These models will be developed for the estimation of the direct (Hb) and diffuse (Hd) components of global solar radiation (H) on both the horizontal and tilted surfaces. Models to determine the optimal tilt and azimuthal angles for solar photovoltaic (PV) collectors in terms of geographical parameters are equally presented. The applicable, statistical evaluation models that ascertain the validity of the SR mathematical models are also highlighted.

This paper treats the nonlinear dynamics of an enzymatic-substrate modeled by the fractional multi-limit cycles Van der Pol oscillator with fractional time-delay feedback device subjected to Lévy noise perturbation and periodic excitation. The fractional electronic circuit has been used to model the system and the oscillations are described by a nonlinear fractional differential equation and show a new bifurcation parameter. The robustness of the stochastic resonance is examined by the use of standard measures within a continuous and a two-state description of the system. Firstly, the electronic circuit with the fractional-order operator and fractional delay feedback is used as a prototype of a fractional self-sustained system. Secondly, based on the minimum mean square error principle, the fractional derivative term is found to be equivalent to the linear combination of the damping force and restoring force, and the original system is further simplified to an equivalent integer order system. The stochastic bifurcation of a bistable Van der Pol system with fractional-order and time delay without and under Lévy noise excitation is studied where we show the considerable effect of these parameters on birhythmic region, escape time and energy barriers. Additionally, we study the multi-effects of fractional order and fractional time-delay feedback in the self-sustained system driven by Lévy noise. The effects of fractional-order parameter, time delay feedback parameter on the autocorrelation function, power spectral density and signal-to-noise-ratio used in this investigation are shown to be maximized for an appropriate choice of the Lévy noise intensity and for a convenient choice of fractional-order and time delay feedback parameters. For a choice of a control parameter in the birhythmic region, by varying the other parameters of the system like fractional and time delay parameters, it appears that, for a fixed value of skewness Lévy noise parameter, the initial selection of the attractor seems to have a large effect on the resonance and coherence.

One of attracting concepts has been the use of Turbosail principle to produce lift from aspirated cylinders with flap in various engineering applications. The Emerging of the Turbosail principle in wind turbine technology is promising to develop and to design innovative devices. The objective of this project paper is to develop an efficient numerical code, for the prediction of the aerodynamic characteristics of Turbosail type wind turbine with very thick aspirated profiles. A vortex model has been treated based on the lifting line theory. The results predicted by the code developed, have been compared and validated by some numerical and experimental data.

For the future installation of a wind farm in Cameroon, the wind energy potentials of three of Cameroon's coastal cities (Kribi, Douala and Limbe) are assessed using NASA average monthly wind data for 31 years (1983-2013) and compared through Weibull statistics. The Weibull parameters are estimated by the method of maximum likelihood, the mean power densities, the maximum energy carrying wind speeds and the most probable wind speeds are also calculated and compared over these three cities. Finally, the cumulative wind speed distributions over the wet and dry seasons are also analyzed. The results show that the shape and scale parameters for Kribi, Douala and Limbe are 2.9 and 2.8, 3.9 and 1.8 and 3.08 and 2.58, respectively. The mean power densities through Weibull analysis for Kribi, Douala and Limbe are 33.7 W/m2, 8.0 W/m2 and 25.42 W/m2, respectively. Kribi's most probable wind speed and maximum energy carrying wind speed was found to be 2.42 m/s and 3.35 m/s, 2.27 m/s and 3.03 m/s for Limbe and 1.67 m/s and 2.0 m/s for Douala, respectively. Analysis of the wind speed and hence power distribution over the wet and dry seasons shows that in the wet season, August is the windiest month for Douala and Limbe while September is the windiest month for Kribi while in the dry season, March is the windiest month for Douala and Limbe while February is the windiest month for Kribi. In terms of mean power density, most probable wind speed and wind speed carrying maximum energy, Kribi shows to be the best site for the installation of a wind farm. Generally, the wind speeds at all three locations seem quite low, average wind speeds of all the three studied locations fall below 4.0m/s which is far below the cut-in wind speed of many modern wind turbines. However we recommend the use of low cut-in speed wind turbines like the Savonius for stand alone low energy needs.

With the soaring of fuel prices and the increase of environmental issues, research in the field of wind power has known a significant growth around the world. The new concept of the Turbosail has been introduced, but did not reach the practical use stage. Despite its practical interest, little work has been conducted on the Turbosail in the recent years.The aim of this paper is to develop an accurate numerical code, based on the singularities method, for the Turbosail analysis and applications. The effects of thickness, suction and flap deflections on the aerodynamics performances of Turbosail profile are studied. The simulation results show that the flap plays an important role in increasing the lift. On the other hand, at high deflection angles, it is generally ineffective for drag reduction. It is found that the suction decreases significantly the drag and causes a little increase in lift. The main conclusion is that, the incorporation of the flap and the suction effect doubly enhance the Turbosail efficiency by increasing its lift and reducing its drag respectively. These results are very important and provide the impetus for developing; the simple design equations and applications for the Turbosail. (C) 2016 Elsevier Ltd. All rights reserved.

Modelling and prediction of wind characteristics are essential design inputs in the development of wind energy systems. This paper exploits the characteristics of the Rayleigh probability density function to analyse the wind potential of all the regions of the republic of Cameroon. In the procedure, the probability density (PD) curves of some representative towns of the ten regions of Cameroon are presented. These curves highlight the fraction of time for which some wind velocity V prevails at the sites and the most frequent wind speed expected at these sites which coincide with the peak of the PD curves. We then proceed to calculate the power density of the sites, as well as the energy available for wind turbine extraction. We recommend the Savonius rotor for the regions of low wind speed based on its low cut-in speed of 1m/s.

This paper treats the vortex shedding phenomenon of a savonius wind turbine, whose knowledge is primordial in correctly calculating the airloads on the blades. The specific aim being to numerically predict the disposition and geometry of the vortical structures in the wake of the savonius rotor whose existence has been visualised by a number of experimentalists. In the numerical approach, the blade is represented by discrete bound vortices while the wake is generated in a time stepping calculation as an emission of free vortices. The calculations are enhanced by the Newmann boundary condition coupled to the Kutta–Joukowsky condition and the Kelvin's theorem for the conservation of circulation. The convection of the vortices in the wake is accomplished through a predictor corrector integration scheme. A code has been developed which predicts the wake structure to be in good agreement with the experimental visualizations: For low tip speed ratios, the wake consists of a series of three discrete vortical structures while at higher tip speed ratios, the characteristic structure is the presences of a central vortex.