Dense Layer Research Articles

Spurious numerical mixing under strong tidal forcing: a case study in the south-east Asian seas using the Symphonie model (v3.1.2)

Abstract. The role of mixing between layers of different densities is key to how the ocean works and interacts with other components of the Earth's system. Correctly accounting for its effect in numerical simulations is therefore of utmost importance. However, numerical models are still plagued with spurious sources of mixing, originating mostly from the vertical advection schemes in the case of fixed-coordinate models. As the number of phenomena explicitly resolved by models increases, so does the amplitude of resolved vertical motions and the amount of spurious numerical mixing, and regional models are no exception to this. This paper provides a clear illustration of this phenomenon in the context of simulating the south-east Asian (SEA) seas along with a simple way to reduce its impact. This region is known for its particularly strong internal tides and the fundamental role they play in the dynamic of the region. Using the Symphonie ocean model, simulations including and excluding tides and using a pseudo-third-order upwind advection scheme on the vertical are compared to several reference datasets, and the impact on water masses is assessed. The high diffusivity of this advection scheme is demonstrated along with the importance of accounting for tidal mixing for a correct representation of water masses. Simultaneously, we present an improvement in this advection scheme to make it more suitable for use in the vertical. Simulations with the new formulation are added for comparison. We conclude that the use of a higher-order numerical diffusion operator greatly improves the overall performance of the model.

Optimizing poultry audio signal classification with deep learning and burn layer fusion

This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which injects controlled random noise during training to reinforce the model's resilience to input signal variations. The proposed architecture is streamlined, with convolutional blocks, densely connected layers, dropout, and an additional Burn Layer to fortify robustness. The model demonstrates efficiency by reducing trainable parameters to 191,235, compared to traditional architectures with over 1.7 million parameters. The proposed model utilizes a Burn Layer with burn intensity as a parameter and an Adamax optimizer to optimize and address the overfitting problem. Thorough evaluation using six standard classification metrics showcases the model's superior performance, achieving exceptional sensitivity (96.77%), specificity (100.00%), precision (100.00%), negative predictive value (NPV) (95.00%), accuracy (98.55%), F1 score (98.36%), and Matthew’s correlation coefficient (MCC) (95.88%). This research contributes valuable insights into the fields of audio signal processing, animal health monitoring, and robust deep-learning classification systems. The proposed model presents a systematic approach for developing and evaluating a deep learning-based poultry audio classification system. It processes raw audio data and labels to generate digital representations, utilizes a Burn Layer for training variability, and constructs a CNN model with convolutional blocks, pooling, and dense layers. The model is optimized using the Adamax algorithm and trained with data augmentation and early-stopping techniques. Rigorous assessment on a test dataset using standard metrics demonstrates the model's robustness and efficiency, with the potential to significantly advance animal health monitoring and disease detection through audio signal analysis.

Marginal stability analyses for thermochemical convection and its implications for the dynamics of continental lithosphere and core-mantle boundary regions

Summary Significant compositional differences may exist in the lithospheric mantle and above the core-mantle boundary (CMB) relative to the ambient mantle. The intrinsic density differences may affect the development of thermal boundary layer (TBL) instabilities associated with lithospheric delamination and formation of thermochemical plumes. In this study, we explored the instability of two-layer thermochemical fluid using two different techniques: marginal stability analysis with a propagator-matrix method and finite element modeling. We investigated both the instabilities in lithospheric mantle (i.e., lithospheric instability) and the mantle above the CMB (i.e., plume-forming instability) using a background temperature Tbg(z) with the TBL. For lithospheric instability, we found that two-layer fluid with free-slip boundary conditions mainly undergoes the same three different convective modes (i.e., two oscillatory convection modes and one layered convection regime) as that with no-slip boundary condition reported in Jaupart et al., (2007). However, with free-slip boundary conditions, the transitions between these convection modes occur at larger values of buoyancy number B. Free-slip boundary conditions lead to smaller critical Rayleigh number Rac, but larger convective wavelength and oscillation frequency ωc, compared with those with no-slip boundary conditions. Our numerical modeling results demonstrate that Rac and ωc predicted from the classical marginal stability analyses using Tbg(z) with TBL temperature may have significant errors when the oscillatory period is comparable with or larger than the timescale of lithospheric thermal diffusion that causes Tbg(z) to vary with time significantly. In this case, using a more gently sloped background temperature profile ignoring the TBL temperature, the stability analysis predicts more accurate stability conditions, thus presenting an effective remedy to the stability analysis. For plume-forming instability, because of the reduced viscosity in the hot and compositionally dense bottom layer, the transition to the layered convection occurs at significantly smaller B values, and in the oscillatory convection regime, Rac is larger but ωc is smaller, compared with those for lithospheric instability. Finally, our study provides a successful benchmark of numerical models of thermochemical convection by comparing Rac and ωc from numerical models with those from the marginal stability analysis.

Fabrication, Microstructure and Plasma Resistance Behavior of Y–Al–Si–O (YAS) Glass-Ceramics Coated on Alumina Ceramics

This study investigates the fabrication, microstructural characteristics and plasma resistance of Y–Al–Si–O (YAS) glass-ceramics coated on alumina ceramics. YAS frits were initially prepared using a melt-quenching method, then homogenously milled and coated onto alumina ceramics. The melt-coating process was conducted at 1650 °C for 1 h. The composition and microstructure of the glass frits and coatings were thoroughly characterized using X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. These analyses revealed a dense microstructure with a polycrystalline structure predominantly composed of Y3Al5O12 (YAG) phase and a minor phase of Y2Si2O7. The YAS coatings on alumina revealed a dense layer with strong adhesion to the substrate. Subsequently, the coatings underwent C4F6/Ar/O2 plasma treatment for 1 h. Plasma exposure tests demonstrated that the YAS-coated alumina exhibited significantly better etching resistance compared to uncoated alumina, with minimal surface damage observed on the YAS coating, confirming its protective properties against plasma. The superior plasma resistance of YAS coatings is attributed to the predominance of its YAG phase. This research offers a more stable and cost-efficient solution for protecting ceramics in demanding plasma environments.

Tri-anion solvation structure electrolyte improves the electrochemical performance of Li||LiNi0.8Co0.1Mn0.1O2 batteries.

Li||LiNi0.8Co0.1Mn0.1O2 batteries,which consist of lithiummetal anode (LMA) matchedwith NCM811 cathode, have an energy density more than twice that of lithium ion battery (LIB).However, the unstable electrode/electrolyte interface still hinders its practical application.Ether electrolytes show promise in improving the stability of LMA and NCM811 cathodes.However, a robust and stableelectrode/electrolyte interface in Li||NCM811 batteries cannot be easily and efficiently achieved with most of the ether electrolytes reported in present studies. Herein, we present a straightforwardand efficient tri-anion synergistic strategy to overcome this bottleneck. The addition of ClO4- and NO3- anions to LiFSI-based ether electrolytes forms a unique solvation structure with tri-anion (FSI-/ClO4-/NO3-) participation(LB511).This structure not only enhances the electrochemical window of the ether electrolytes but also achieves a stable Li||NCM811batteries interface.The interaction between electrode and electrolyte is suppressed andan inorganic-rich (LiF/Li3N/LiCl) SEI/CEI layer is formed. Meanwhile, the coordination structure in the LB511 electrolyte increases the overpotential for Li deposition, resulting in a uniform and dense layer of deposition.Therefore,the Li||Cu cells using the LB511 electrolytehave an average CE of 99.6%.TheLi||NCM811 batteries was cycled stably for 250 cycles with a capacity retention of 81% in the LB511 electrolyte (N/P = 2.5, 0.5 C).

Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

Efficiency improvement of ultrathin CIGS solar cells

In this paper we present an optimization of rear-passivation parameters (cell pitch, opening width, and interface trap density) in u-CIGS solar cell using TCAD tools. The proposed investigation exhibits a significantly enhanced in understanding the beneficial effect of the rear passivation in ultrathin cell on conversion efficiency and reliability compared to existing solutions in the literature. Firstly, the device was calibrated according to the fabricated model taking in to account all material properties and defects at interfaces and within the bulk of the cells. Additional simulations demonstrate notable enhancements in cell performance due to changes in absorber thickness and doping concentration. However, traps were identified at rear u-CIGS/Al2O3 interface as a key factor degrading conversion efficiency by promoting carrier recombination. Mitigating this mechanism is essential for improving device performance. Incorporating a negative fixed charge density in the rear passivation layer offers a promising solution, providing effective field-effect passivation to minimize minority carrier recombination at the rear surface. The proposed u-CIGS solar cell device achieves an impressive efficiency of 15 % using a cell pitch of 1.5 µm and opening width of 300 nm with a fixed bandgap.

Comparative of OCT and OCTA parameters in patients with early chronic angle-closure glaucoma and early pituitary adenoma

Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have the potential application in evaluating pathological structural change of the optic nerve. We aimed to evaluate the value of the OCT and OCTA parameters of the optic disk and macular in differentiating early chronic primary angle-closure glaucoma (CPACG) and early pituitary adenoma (PA) in case of mild visual field defects (the mean defect (MD) > 6 dB). The results showed that regarding OCTA parameters, CPACG patients had lower retinal blood flow density of most layers of the optic disk and macular than PA patients. Regarding OCT parameters, CPACG patients had thinner circumpapillary retinal nerve fiber layer (CP-RNFL) in all quadrants and average CP-RNFL, ganglion cell layer (GCL) and macular ganglion cell complex (GCC) in each quadrant of macular inner and outer rings, and inner plexus layer (IPL) of macular inner ring, superior-outer ring and temporal-outer ring than PA patients. The Z test indicated that OCTA parameters and OCT parameters had similar value in the diagnosis of disease. In conclusion, in the case of similar visual field damage, early CPACG patients have smaller blood flow density and thinner optic disk and macular than early PA. OCTA has similar performance to OCT in diagnosing CPACG and PA.

Effects and optimizations of image charge technology on the imaging performance of capacitive division image readout MCP detectors

A finite element model is proposed to investigate the impact of image charge technology on the imaging performance of capacitive division image readout (C-DIR) devices. Through detailed simulation analysis, we explore the charge density of the induction layer and each electrode layer in the multilayer capacitive anode. The position nonlinearity is calculated for various resistances per square of the induction layer and substrate thicknesses. The simulation results indicate a strong linear correlation between the position nonlinearity and substrate thickness, and an exponential decay relationship with the logarithmic resistance per square of the induction layer. The C-DIR detector is experimentally tested for imaging nonlinearity and counting rate, with the resistance per square of the induction layer ranging from 0.005 to 1000 MΩ, and the substrate thickness ranging from 1 to 5 mm. The experimental results validate the simulation conclusions and reveal the impact of the “charge accumulation effect” and “shielding effect” on the imaging performance of the detector, as well as the “imaging compression” phenomenon. Optimized image charge readout technology enables the C-DIR detector to achieve an imaging nonlinearity of 1.53% in the experiment.

The retinal blood flow density is related to the pathological severity of diabetic kidney disease.

This study aimed to investigate the correlation between fundus blood flow parameters and the severity of pathological biopsy in patients with diabetic kidney disease (DKD). Data of patients with type 2 diabetes mellitus who completed renal pathology biopsies and optical coherence tomography angiography (OCTA) examinations, including renal function, 24-h urine protein quantification, and macular flow imaging, were collected. DKD pathology biopsies were graded as stages 1-4, and differences and correlations of the parameters were compared between groups. The grading was transformed into early (stage 1) and late (stages 2-4), and regression analyses were conducted to develop a model, draw a nomogram, and test efficacy. This study included 157 eyes from 157 individuals in total. Urinary microalbumin and to urinary creatinine ratio (mALB/NCR) increased with pathological grading, whereas while glomerular filtration rate was decreased (p < 0.01). Corresponding retinal blood flow in superficial, deep, and full paracentral rings was decreased, which correlated with pathological grading (p < 0.01), with the highest blood flow density in the whole layer (r2 = -0.707). Meaningfully, in the early DKD model (area under the curve =0.929 (0.889-0.970), p < 0.01), whole-layer blood flow density, mALB/NCR, and diabetes duration were statistically significant. The decrease in macular retinal blood flow density detected by OCTA is closely associated with the increase in pathological grading of DKD and can be used as a noninvasive parameter for monitoring early changes in DKD.

The effect of different potency glucocorticosteroids treatments on tear inflammatory factors and corneal optical density after femtosecond-assisted laser in situ keratomileusis.

To assess the impact of glucocorticosteroids with varying potencies on inflammatory mediators in tears and corneal optical density after femtosecond-assisted laser in situ keratomileusis (FS-LASIK). In a prospective study, 110 patients (220 eyes) who underwent FS-LASIK were divided into 2 groups: 55 patients (110 eyes) received dexamethasone, and another 55 patients (110 eyes) received fluorometholone. Visual acuity, intraocular pressure, and corneal optical density were measured before, 1 week, and 1 month after surgery. Tear fluid samples were also collected to assess expression levels of TNF-α, IL-1α, IL-6, and TGF-β1. One week after the procedure, the dexamethasone group exhibited elevated intraocular pressure (IOP) levels (P > .05) and a decreased expression of TNF-α in tears (P < .001) compared to the fluorometholone group. Within the 0 to 2 mm range from the corneal apex, the anterior corneal layer's optical density in the fluorometholone group surpassed that of the dexamethasone group (P < .05). At 1 month post-surgery, the IOP in the fluorometholone group was higher than that in the dexamethasone group (P < .05). In both the 0 to 2 mm and 2 to 6 mm intervals from the corneal apex, the optical density of the anterior corneal layer was significantly higher in the fluorometholone group compared to the dexamethasone group (P < .05). There was no statistically significant difference in visual acuity between the 2 groups at any postoperative time point. Short-term use of potent corticosteroids after FS-LASIK can swiftly address ocular surface inflammation, enhance corneal wound healing, reduce corneal edema, and accelerate the restoration of corneal transparency, in contrast to prolonged use of milder corticosteroids post-surgery.

Wet carbonation of recycled cement paste powder using a CO2-loaded monoethanolamine solvent as an internal CO2 source

To address the challenge posed by the carbonation product layer formed on recycled cement paste powder (RCPP) on the efficiency and degree of carbonation, CO2-loaded monoethanolamine (MEA) “microcapsules” were used. The CO2 within MEA was released by heating after the formation of the dense carbonation products layer on RCPP to provide an additional internal CO2 source to penetrate into the uncarbonated layer. Results showed that using CO2 dissolved in 15 %, 30 %, and 45 % MEA aqueous solution increased the carbonation degree of RCPP by 10.8 %, 11.2 %, and 13.8 %, respectively, compared to RCPP wet carbonated in water. In addition to calcite, vaterite and aragonite were observed in the CO2-loaded MEA carbonated RCPP. The long-term compressive strength of cement pastes with 30 % CO2-loaded MEA carbonated RCPP increased, and the porosity decreased compared to cement paste with RCPP wet carbonated in water. However, MEA negatively affected the early compressive strength of the cement paste.

A Data-Driven Multi-Step Flood Inundation Forecast System

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

One-step fabrication of hetero-structured polyethersulfone hollow fiber membranes through surface segregation for pervaporation desalination

Hollow fiber membranes (HFMs) are an ideal configuration for industrial applications of pervaporation desalination (PV) membranes. Currently, the efficient fabrication of PV HFMs is restricted by multi-step post-treatment processes on their outer surfaces to form the selective layer. Herein, we utilized the surface segregation strategy to fabricate a hetero-structured HFM in one step, simultaneously forming the hydrophilic dense outer layer and the porous support layer. During hollow fiber spinning process, the amphiphilic copolymer polyvinylpyrrolidone-polyvinyl acetate (PVP-PVAc) in polyether sulfone (PES) dope solution migrated towards water contacting interface in the coagulation bath and formed the hydrophilic layer by extending hydrophilic segment outwards. This fast in-situ hydrophilization well matched the spinning efficiency. The optimal PVP-PVAc content and phase inversion temperature were investigated and the spinning parameters on the structure and performance of HFMs were studied. The HFMs prepared realized a flux of 26.70 kg m−2 h−1 and the salt rejection of 99.98 %, along with adaptability to solutions with different salinities and excellent anti-organic fouling property. This study may provide a facile method to fabricate HFMs with desirable PV desalination performance and scaling-up potential.

Intercalation modified nano zirconium phosphate inhibitor for inhibiting coal spontaneous combustion

The synthesis and characterization of a modified zirconium phosphate (MZrP) nano-inhibitor for spontaneous coal combustion inhibition were explored through isooctylamine intercalation. Using oxidation experiment, FTIR and SEM, the variation rules of the characteristic parameters of the different coal samples were investigated. It was observed that MZrP exhibited enhanced dispersion within the coal matrix post-intercalation modification. As the MZrP concentration increased, there was a notable decrease in oxygen consumption rate, gas production concentration, and active group content in the coal, alongside a significant increase in apparent activation energy and inhibition efficacy. This enhanced inhibition is attributed to two primary mechanisms. Firstly, the hydrophilic nature of MZrP allows for its uniform distribution on the coal surface and within internal pores, creating a dense carbonized layer. This layer effectively retains moisture and isolates oxygen, enhancing physical inhibition. Secondly, MZrP inhibitor is thermally decomposed into phosphoric acid and its phosphoric acid derivatives, which can effectively capture the H- and -OH in the coal, and strengthens the inactivation of its reactive free radicals. The optimal inhibition was observed in coal samples treated with 6 wt% MZrP, exhibiting an average inhibition rate of 62.2 %, coupled with the lowest rates of gas production and oxygen consumption.

Heterogeneous porous hypoxia-mimicking scaffolds propel urethral reconstruction by promoting angiogenesis and regulating inflammation

The nasty urine microenvironment (UME) impedes neourethral regeneration by inhibiting angiogenesis and inducing an excessive inflammatory response. Cellular adaptation to hypoxia improves regeneration in numerous tissues. In this study, heterogeneous porous hypoxia-mimicking scaffolds were fabricated for urethral reconstruction via promoting angiogenesis and modulating the inflammatory response based on sustained release of dimethyloxalylglycine (DMOG) to promote HIF-1α stabilization. Such scaffolds exhibit a two-layered structure: a dense layer composed of electrospun poly (l-lactic acid) (PLLA) nanofibrous mats and a loose layer composed of a porous gelatin matrix incorporated with DMOG-loaded mesoporous silica nanoparticles (DMSNs) and coated with poly(glycerol sebacate) (PGS). The modification of PGS could significantly increase rupture elongation, making the composite scaffolds more suitable for urethral tissue regeneration. Additionally, sustained release of DMOG from the scaffold facilitates proliferation, migration, tube formation, and angiogenetic gene expression in human umbilical vein endothelial cells (HUVECs), as well as stimulates M2 macrophage polarization and its regulation of HUVECs migration and smooth muscle cell (SMCs) contractile phenotype. These effects were downstream of the stabilization of HIF-1α in HUVECs and macrophages under hypoxia-mimicking conditions. Furthermore, the scaffold achieved better urethral reconstruction in a rabbit urethral stricture model, including an unobstructed urethra with a larger urethral diameter, increased regeneration of urothelial cells, SMCs, and neovascularization. Our results indicate that heterogeneous porous hypoxia-mimicking scaffolds could promote urethral reconstruction via facilitating angiogenesis and modulating inflammatory response.

Numerical exploration of MHD bioconvective Williamson-Maxwell nanoliquid flow due to an exponentially elongated porous sheet

This research delves into the intriguing realm of non-Newtonian fluids in conjunction with microorganisms, presenting a mathematical model tailored to analyze heat and mass transfer within Williamson-Maxwell nanofluids hosting gyrotactic microbes. The study investigates how these fluids behave under the influence of multiple factors such as magnetic fields, thermal radiation, chemical reactions, and dissipation effects. Employing a set of similarity invariants, the governing equations are transformed into ordinary differential equations, which are then solved using a fourth-order R-K scheme. The findings, presented graphically, offer insights into various flow parameters and are complemented by pertinent physical explanations. The influence of magnetic flux ([Formula: see text]), Buoyancy ratio ([Formula: see text]), Peclet number ([Formula: see text]), and Schmidt number ([Formula: see text]) on various physical parameters are shown graphically. Notably, the research reveals that while increasing the external magnetic field impedes fluid motion, it enhances thermal and density layers. Additionally, a higher bioconvective Schmidt number is shown to reduce microbial density. These observations hold significant implications for applications involving nanofluids and microorganisms across biomedical, pharmaceutical, biofuels, and other sectors. Overall, this study contributes valuable knowledge to the understanding and potential utilization of complex fluid systems in diverse industrial contexts.

Spatial variation of soil organic carbon density in the black soil region of Northeast China under the influence of erosion and deposition

Soil erosion of the Songnen typical Black Soil Region (BSR) in Northeast China where contains higher soil organic carbon is serious, resulting in soil organic carbon spatial redistribution. To study the spatial redistribution pattern of cropland SOC caused by erosion, 115 cropland sample points were selected in the Songnen typical BSR, and soil samples were collected along the entire soil profile. The soil organic carbon density (SOCD) of the soil layer and profile were calculated, and the carbon storage (CS) was calculated for the erosion and deposition areas. The average soil organic carbon density (SOCDav) of the 115 cropland soil profiles (1 m) ranged from 1.5 to 4.5 kg/m2, and the total soil organic carbon density (SOCDt) of the soil profile (1 m) ranged from 10 to 20 kg/m2. The SOCD of each soil layer (20 cm) decreased vertically in a logarithmical manner from the top, and was divided into four groups with thickness through systematic clustering, which were 30–60 cm, 60–100 cm, less than 300 cm and greater than 350 cm. The SOCDt in the area north of the Songhua River and the area west of Nen River (The Greater Khingan Mountains area) ranged from 2.5 to 59.3 kg/m2, with an average is 14.1 kg/m2, and the first sample point group (30–60 cm) was mainly located in this area which had an average slope of 3.2° and an average black soil thickness of 24.6 cm. The SOCDt in the area north of the Songhua River and the area east of the Nen River ranged from 11 to 90 kg/m2, with an average of 28.6 kg/m2, the third and the fourth sample point group (greater than 100 cm) were both mainly located in this area which had an average slope of 2° and an average black soil thickness of 61 cm. The SOCDt in the area south of the Songhua River ranged from 1.6 to 28.7 kg/m2, with an average of 13.8 kg/m2, and the second sample point group (60–100 cm) was mainly located in this area which had an average slope of 2.4° and an average black soil thickness of 58.7 cm. The results showed that soil erosion occurred on steeper slopes, resulting in thinner soil layers, whereas soil deposition occurred on lower grade slopes, resulting in thicker soil layers. Kriging interpolation in ArcGIS showed that the Cs in the erosion and deposition areas of the Songnen typical BSR was 459.7 and 1234.7 Tg, respectively, and the total Cs was 1.6944 Tg (1 Tg = 1012 g).

Numerical investigation and design optimization for enhanced efficiency of Cs2AgBiBr6 perovskite solar cell

Nowadays, environmental concerns are becoming increasingly important. As a result of the imperative to protect society, lead-free perovskites are becoming increasingly important. The lead-free compound cesium silver bismuth bromide (Cs2AgBiBr6) was numerically investigated as a potential candidate for double perovskite solar cells application. In the proposed architecture, we combine FTO/ETL-SnO2/Cs2AgBiBr6/HTL-CuI/Au, all optimized for performance. To optimize the solar cell’s performance, the device configuration was modelled using SCAPS-1D. An evaluation of the impact of various factors, including absorber thickness, defect density, electron affinity, electron and hole transport layer optimization, band diagram analysis, working temperature and quantum efficiency, was conducted. The proposed device configuration exhibited excellent photovoltaic performance, achieving a PCE of 8.15%, a Voc of 1.041 V, a Jsc of 11.79 mA cm−2, an FF of 66.41%, and a quantum efficiency of 99.19% within the visible range. This research highlights the potential of Cs2AgBiBr6 as a promising perovskite material for use in lead-free photovoltaic technology.

Integration of SCAPS-1D and density functional theory for the performance evaluation of RbGeI3-based perovskite solar cell

The instability of organic-inorganic perovskites in the presence of heat, light, or moisture coupled with the presence of carcinogenic lead (Pb) motivated researchers to look for alternatives in the form of Pb-free all-inorganic perovskite materials as potential absorbers for designing stable and efficient solar cells. In this study, SCAPS-1D is utilized to study the photovoltaic performance of all-inorganic Pb-free RbGeI3-based planar n-i-p perovskite solar cell (PSC). The optoelectronic characteristics of RbGeI3 are obtained using WIEN2K within the density functional theory framework. Twenty-five different combinations of RbGeI3-based device architectures with different ETLs and HTLs are explored out of which the best PSC architecture is chosen for further analysis. We show that the device structure FTO/TiO2/RbGeI3/PTAA/Au exhibited a remarkable power conversion efficiency (PCE) of 24.03 %, a high fill factor (FF) of 79.85 %, an open circuit voltage (Voc) of 0.88 V, and a short circuit current density (Jsc) of 33.83 mA/cm2. Enhanced optimized performance characteristic is obtained through the variation of the defect density, bandgap, and thickness of the RbGeI3 layer. This paper proposes a new way of studying the photovoltaic characteristics of lead-free perovskite absorbers.