Bio-inspired piezoelectric materials have emerged as promising environmentally sustainable alternatives for energy harvesting applications due to their non-toxic, biodegradable, and biocompatible properties. However, achieving high power density and efficient energy conversion in bio-piezoelectric sensors remains a significant challenge. This study investigates the potential of naturally abundant eggshell membrane (ESM), a bio-waste derived material, as a novel piezoelectric material for energy harvesting. The ESM possesses inherent fibrous and porous characteristics that facilitate mechanical-to-electrical energy conversion without requiring complex chemical processing. A bio-piezoelectric sensor (BPS) device was fabricated from ESM and systematically characterized for its electrical performance. Under controlled mechanical stimulation, the fabricated device demonstrated a peak output voltage of 1.56 V, indicating its viability as a cost-effective and sustainable sensing material. These findings demonstrate the potential of repurposing bio-waste materials in the development of environmentally friendly piezoelectric sensors and energy harvesting devices, offering a pathway toward circular economy principles and green electronics.

Germanium-based perovskite solar cells have emerged as promising lead-free alternatives to conventional perovskite photovoltaic technologies due to reduced toxicity and relative earth abundance. This study presents a comprehensive computational investigation of four germanium-based perovskite absorbers MAGeI₃, FAGeI₃, CsGeI₃, and RbGeI₃ using SCAPS-1D numerical simulations to evaluate their potential for high-efficiency solar cells. Although germanium possesses favorable properties including lower spin-orbit coupling and better charge-carrier transport characteristics than lead-based perovskites, the strong tendency of Ge²⁺ to oxidize to Ge⁴⁺ results in elevated defect densities exceeding 10¹⁵ cm⁻³, significantly limiting device performance through enhanced trap-assisted recombination. A planar n-i-p device architecture comprising FTO/TiO₂/Ge-based perovskite/Spiro-OMeTAD/Au was simulated under identical conditions to enable systematic comparison. Key parameters including absorber thickness, bulk defect density, and interface defect density were systematically optimized to determine their influence on photovoltaic characteristics. Results demonstrate that an optimal absorber thickness of approximately 400 nm provides the best balance between light absorption and recombination losses across all four materials. Analysis of current density-voltage characteristics, external quantum efficiency spectra, and generation-recombination profiles reveals that RbGeI₃ exhibits superior performance with the highest power conversion efficiency, open-circuit voltage, and fill factor, while FAGeI₃ displays lower efficiency due to increased recombination losses. These findings highlight the critical importance of defect control in developing efficient Germanium-based perovskite solar cells and provide valuable insights for future experimental optimization strategies toward environmentally benign, high-performance photovoltaic devices.

High-quality organic single crystals of hippuric acid were successfully grown from methanol solvent using the slow evaporation solution growth technique. Single crystal and powder X-ray diffraction analyses confirmed the orthorhombic crystal system with the non-centrosymmetric space group P212121, and the lattice parameters were found to be in good agreement with reported values. Optical transmittance studies revealed that the crystal is highly transparent across the entire visible region (350–1100 nm) with a lower cut-off wavelength of 230 nm, and the optical band gap was determined to be 3.74 eV using Tauc’s plot, suggesting a high laser damage threshold. Dielectric constant and dielectric loss measurements as a function of frequency confirmed the normal polarization behavior of the material with low loss at higher frequencies, indicative of good optical quality. DC conductivity measurements established the ohmic nature of the crystal, and an activation energy of 0.99 eV was calculated using the Arrhenius relation. Negative photoconductivity was observed and attributed to the Stockmann mechanism. Photoluminescence studies revealed a prominent green emission at 520 nm upon excitation at 230 nm, confirming the material’s potential in optoelectronic applications. Powder second harmonic generation (SHG) measurements using the Kurtz–Perry technique yielded an output of 18 mV, approximately 1.12 times that of the standard reference material potassium dihydrogen phosphate (KDP), establishing the NLO activity of the crystal. The first-order hyperpolarizability (β0) computed using density functional theory (DFT) at the B3LYP/6-31G level was found to be 24.85 times greater than that of urea, further confirming the strong nonlinear optical response of hippuric acid crystals.

Linear stability analysis of Supercritical Water-CooledReactors (SCWR) identifies whether a given operating pointis stable or unstable, but it cannot distinguish betweentwo fundamentally different types of instability onset:the supercritical Hopf bifurcation, in which oscillationsgrow smoothly and predictably from rest, and the subcriticalHopf bifurcation, in which the system jumps discontinuouslyto large-amplitude oscillations without warning and cannotreturn to the stable state simply by reversing the perturbation.The second type is the more dangerous of the two from a reactorsafety standpoint, yet no real-time computational methodhas previously been proposed to distinguish them. This paperaddresses that gap by presenting a Physics-Informed NeuralNetwork (PINN) framework for classifying the Hopf bifurcationtype at any SCWR operating point, using the sign of theFirst Lyapunov Coefficient $l_1$ --- the fundamental quantityfrom nonlinear bifurcation theory --- as the classificationcriterion. The PINN is trained on a dataset of 8,000 operatingpoints generated analytically from the Lumped Parameter Model(LPM) of Shankar et al. \cite{shankar2017,shankar2018},with five physics constraints encoding the monotonicrelationships between $l_1$ and the LPM parameters$N_{TPC}$, $N_{SPC}$, $\tau_f$, $K_i$, $K_o$, andthe Peclet number $Pe$, established through the generalisedHopf analysis of Shankar et al.\cite{shankar2018}.Ten PINN architectures are evaluated against two baselinemultilayer perceptrons. The best model, Hopf PINN Light,achieves an AUC-ROC of 0.9987 compared to 0.9961 for thebest baseline (improvement $+0.26\%$, $p = 0.005811$,statistically significant). The trained classifier evaluatesany operating point in under 0.1 seconds --- approximatelyfour orders of magnitude faster than full nonlinear analysis--- making real-time Hopf type monitoring feasible withinthe reactor control system. The approach is validated againstthe five benchmark cases reported in the nonlinear stabilityanalysis of Shankar et al. \cite{shankar2018}, withcorrect type prediction in all five cases.

Ensuring the thermohydraulic stability of Supercritical Water-Cooled Reactors (SCWR) under seismic excitation is a critical safety requirement for Generation-IV nuclear deployment. Earthquake ground motion systematically reduces the Marginal Stability Boundary (MSB) the threshold separating stable from unstable coolant thereby enlarging the operating envelope in which density wave oscillations can develop. This paper presents a Physics-Informed Neural Network (PINN) framework that extends the validated Lumped Parameter Model (LPM) of Shankar et al. [1, 2] to include seismic forcing characterised by the Kanai Tajimi power spectral density function [2]. A nine-dimensional input vector combining the LPM dimensionless stability parameters NTPC, NSPC, τf, Ki, and Ko with four Kanai Tajimi seismic descriptors ωg, ζg, S0, and amax enables the network to classify any operating point as seismically stable or unstable with a single forward pass. Seven physics constraints, derived directly from the published LPM parametric analysis and Kanai Tajimi theory, are embedded in the training loss to enforce monotonic physical relationships that pure data-driven models would otherwise violate. The best model, Seismic PINN Light, achieves an AUC-ROC of 0.9994 compared to 0.9982 for the best baseline (improvement +0.12%, p = 0.000138, highly signi cant). The trained classier reduces the computational cost of a seismic stability evaluation by four to ve orders of magnitude relative to Monte Carlo simulation, opening a practical pathway for real-time seismic safety monitoring in Gen-IV reactor control systems.

This study presents a gate-to-gate life cycle assessment (LCA) comparing the environmental impact of three torrefied municipal solid waste (MSW) energy sources (S1) solar photovoltaic (PV), (S2) grid electricity from India and (S3) biomass combustion. Experiments performed in a laboratory setting produced a yield of transitory MSW torrefaction of 28–32% at an input fuel energy of 2 kWh/kg at 200–300 °C for 30–60 min. The ReCiPe 2016 midpoints [Global Warming Potential (GWP); Human Toxicity Potential (HTP); Acidification Potential (AP); Particulate Matter Formation Potential (PMFP)] showed PV produced the least number of emissions (GWP = 0.0426 kg CO2 equivalent; HTP = 0.00988 kg 1,4-DB equivalent), while grid power produced the greatest number of emissions (GWP = 2.2 kg CO2 equivalent). Biomass produced intermediate results (GWP = 1.546 kg CO2 equivalent). All sources had an average positive net energy ratio of approximately 2.78. The results indicate that integrating renewable energy sources significantly increases the environmental and social benefits of the torrefaction process. Additionally, the study provides a MS Excel-Based LCA framework to use when data are limited.

Accurate prediction of the Marginal Stability Boundary (MSB) in Supercritical Water-Cooled Reactors (SCWR) is essential for safe reactor operation. The Lumped Parameter Model (LPM) developed previously by the author has demonstrated reliable MSB prediction within ±7% of experimental data, but requires computationally intensive transient simulations for each operating point. This work presents a Physics-Informed Neural Network (PINN) framework that extends the LPM into a fast, data-driven MSB classifier. The two-zone LPM, characterised by the Transcritical Phase-Change Number (NTPC) and Pseudo-Subcooling Number (NSPC), is used to generate a physics-grounded training dataset of 8,000 operating points across the full stability parameter space. Four physics constraints derived directly from the LPM parametric analysis — encoding the destabilising effect of fuel time constant τf, the stabilising role of inlet orifice coefficient Ki, the NTPC powerinstability relationship, and the NSPC inlet subcooling reserve — are embedded into the PINN loss function. Ten PINN architectures are evaluated against two baseline multilayer perceptrons without physics constraints. The best PINN achieves an AUC-ROC of 0.9995 compared to 0.9990 for the baseline (improvement +0.05%, p = 0.000018, highly significant). The proposed framework provides instantaneous MSB classification — replacing multi-hour transient simulations — while maintaining physical consistency with the validated LPM. This approach offers a practical tool for real-time SCWR safety monitoring and complements the authors’ prior work on scaling methodology and parametric stability analysis.

Abstract Supercritical water-cooled reactors (SCWRs) are the generation-IV nuclear reactors coming up as a promising technology that has a low carbon footprint and is capable of providing outstanding thermal efficiency. Safety being paramount for SCWRs, precise mapping of the wall heat flux profile in the reactor core is essential to identify localized hot spots, manage thermal stress on fuel rods, and maintain their structural integrity as a critical reactor safety parameter. However, due to rapid thermophysical property shifts in water's pseudo-supercritical zone and limited data availability of the reactor core it becomes challenging to model thermal hydraulics in the pseudocritical region of water. Again, inverse physics-informed neural networks are known for their capability to reconstruct unknown time-dependent profiles when governing physics of any system is known to an extent. Available physics-informed neural network (PINN) studies have utilized the dense grid of temporal data, which may not be always available in reality. So, to address these gaps in this study, we have established a very limited data guided multistage inverse PINN architecture integrated with SCWR thermal-hydraulic model thermal-hydraulic solver undertaking supercritical water (THRUST) that simultaneously recovers transient bulk fluid temperature and transient wall heat flux distributions under the assumption of constant mass flux and a prescribed range of linear pressure drop. A benchmark validated recovery through an analytical temperature field and simulated heat flux profile demonstrated reliable reconstruction of smooth, realistic, and physically consistent heat flux distributions. Also, extensive parametric and ablation studies establish the robustness of the architecture, aiding to the first-time PINN integration on the thermal hydraulics of SCWR like system where data sparsity is a real constraint.

Germanium-based perovskite solar cells have emerged as promising lead-free alternatives to conventional perovskite photovoltaic technologies due to reduced toxicity and relative earth abundance. This study presents a comprehensive computational investigation of four germanium-based perovskite absorbers MAGeI3, FAGeI3, CsGeI3, and RbGeI3 using SCAPS-1D numerical simulations to evaluate their potential for high-efficiency solar cells. Although germanium possesses favorable properties including lower spin–orbit coupling and better charge-carrier transport characteristics than lead-based perovskites, the strong tendency of Ge2+ to oxidize to Ge4+ results in elevated defect densities exceeding 1015 cm−3, significantly limiting device performance through enhanced trap-assisted recombination. A planar n-i-p device architecture comprising FTO/TiO2/Ge-based perovskite/Spiro-OMeTAD/Au was simulated under identical conditions to enable systematic comparison. Key parameters including absorber thickness, bulk defect density, and interface defect density were systematically optimized to determine their influence on photovoltaic characteristics. Results demonstrate that an optimal absorber thickness of approximately 400 nm provides the best balance between light absorption and recombination losses across all four materials. Analysis of current density–voltage characteristics, external quantum efficiency spectra, and generation-recombination profiles reveals that RbGeI3 exhibits superior performance with the highest power conversion efficiency, open-circuit voltage, and fill factor, while FAGeI3 displays lower efficiency due to increased recombination losses. These findings highlight the critical importance of defect control in developing efficient germanium-based perovskite solar cells and provide valuable insights for future experimental optimization strategies toward environmentally benign, high-performance photovoltaic devices.

Supercritical water is highly useful and prevalent in nuclear reactors and advanced thermal systems for its high cooling capacity. Water when used at supercritical states can provide the exemptions from two phase flow instability but comes with the unique challenge of modeling the thermophysical property variations around the pseudo critical point. That's why modeling of flow instability of supercritical water in different physical systems is of prime interest to the researchers throughout the decades. To avoid the computational complexity associated with iteratively solving the complex and multiparameter equation of states repeatedly and with an aim to develop symbolic formulations for relating the basic thermophysical properties, i.e. density, specific heat, thermal conductivity, viscosity of super critical water Random Forest ML model is employed on a varied data set extracted from Coolprop, a thermophysical repository in python. As a result, machine learning model learns the inherent patterns even in the pseudocritical property variation zone of water from the highly reliable Coolprop datasets and successfully able to deliver the prediction with highly reasonable accuracy. Besides this using pysr, a symbolic correlation package it is efficiently discovered two set of correlations for each of thermal conductivity and viscosity, where each of the correlation is based on only temperature and pressure. Giving input of temperature and pressure only can now predict four of the said properties.

This chapter explores the innovative use of waste-derived carbon nanomaterials in solar cells, addressing both environmental and energy challenges. It discusses the potential of these materials to improve solar cell efficiency and reduce costs while utilizing waste sources like biomass, plastics, and industrial by-products. The synthesis methods for these nanomaterials, including pyrolysis and hydrothermal carbonization, are examined. The chapter details how waste-derived carbon nanomaterials can enhance various solar cell components, such as electron and hole transport layers, in perovskite, organic, and dye-sensitized cells. Specific examples are presented, like graphene quantum dots from coal and carbon nanotubes from plastic waste, along with their performance improvements. The chapter also addresses challenges in using these materials, including purity and scalability issues. Environmental and economic impacts are evaluated, highlighting the potential for waste reduction and creating a circular economy in the solar energy sector. Future trends, such as integration with quantum dots and applications in flexible solar cells, are explored. The chapter concludes by emphasizing the transformative potential of waste-derived carbon nanomaterials in advancing sustainable solar technology, calling for continued research and development in this promising field.

Sunlight driven photoelectrochemical (PEC) water splitting has garnered excessive attention as an eco-friendly technique to produce renewable hydrogen. Designing TiO2 based composites have evolved as an efficient strategy to extend the photoactivity of TiO2, inhibit their charge recombination and increase their stability. Owing to its well-matched energy bands, graphitic carbon nitride (g-C3N4) has emerged as an effective counterpart of TiO2. The present work reports the synthesis of gC(3)N(4)/TiO2 composites from a three-step process where bulk g-C3N4 was electrodeposited on separated TiO2 nanotubes from a constantly rotating organic suspension at a voltage of about similar to 90 V for desired time. To explore the role of crystalline TiO2 nanotubes on the composite formation, g-C3N4 was similarly electrodeposited on amorphous separated nanotubes etched from Ti Foil to obtain g-C3N4/amorphous TiO2 nanotube which was then annealed at 450 degrees C for 3 h. UV-Vis, FTIR, Raman and PL spectra was recorded for virgin TiO2 nanotube and g-C3N4/ TiO2 nanotube composites formed under varying deposition conditions to investigate their optoelectronic properties comparatively. The photoresponse of the samples was evaluated from photoelectrochemical measurements. All the composites demonstrated good photoactivity and enhanced photoelectrochemical response going upto an order increase than bare TiO2. The microstructural study revealed the effect of crystalline TiO2 nanotubes on the composite formation. TiO2 nanotube arrays provide a direct pathway for electron transfer and the ease of access of its inner and outer surfaces aid in light scattering more efficiently. Incorporating bulk g-C3N4 is appealing as a simple process that decreases the complexity and promotes increased light harvesting.

Recent research has shown that two-dimensional transition metal oxides (TMOs) are potential materials for improving the stability and efficiency of solar cells. The brief overview of TMOs’ potential for use in solar cell applications in this abstract emphasizes their special qualities, synthesis techniques, and most recent developments. Due to their various chemical compositions and electrical structures, transition metal oxides have been thoroughly researched for their potential in renewable energy technologies, particularly in photovoltaics. Due to their fascinating electrical characteristics and simplicity of integration into solar cell topologies, two-dimensional TMOs like vanadium pentoxide (V2O5), tungsten diselenide (WSe2), TiO2 (titanium dioxide), zinc oxide (ZnO), tin dioxide (SnO2) has drawn a lot of attention. TMOs’ distinctive electrical band structures make effective charge separation and light absorption possible, both of which are essential for the performance of solar cells. TMOs have variable bandgaps that enable them to absorb various sun-spectrum wavelengths. TMOs are a good choice for single-junction and tandem solar cells because of their superior charge transfer capabilities and tunability. TMOs’ adaptability in terms of synthesis techniques is one of their main benefits. High-quality TMO thin films have been created using various methods, including chemical vapor deposition (CVD), liquid-phase exfoliation, and atomic layer deposition (ALD). These techniques enable the optimization of TMO features for particular solar cell applications by providing fine control over thickness, shape, and composition. The development of TMO-based solar cells has been impressive recently. Particularly, tandem solar cells profit from the superior optical qualities of TMOs. Researchers have significantly increased power conversion efficiency by mixing TMOs with other semiconducting substances like silicon or perovskites. TMOs have been included in many solar cell topologies, such as thin-film, organic, and quantum dot solar cells, proving their versatility. The long-term survival of solar cell technologies depends on TMOs’ exceptional stability and durability in adverse environmental conditions. They are desirable options for outdoor applications and have longer operational lifetimes because of their resistance to corrosion and photo-induced degradation.

E-rickshaws, which are highly popular in developing countries like India, are categorized as a primary source of income for many commercial drivers/operators. Though conventional e-rickshaws may seem environmentally friendly as they are charged through the national electricity grid, a significant portion of the electricity from the grid is contributed by thermal coal-based powerplants that cause substantial carbon emissions. Additionally, the current study focuses on the North-Eastern region of India, where charging e-rickshaws are a problem primarily due to the erratic supply of electricity from the grid. To tackle this, some operators have integrated their e-rickshaws with solar PV modules to increase operation time and reduce charging costs. Most e-rickshaw operators are hesitant to adapt to this technology. Thus, to address this problem, this paper presents a perspective through simulation and mathematical modelling of performance, payback, and carbon reduction from installing the solar PV retrofit. Results indicate that the average duration of operation extension per day in the North-East region from the installation of this retrofit is about 1 h and 18 min. Payback can be achieved within nine months, and net CO2 emission reduction from each unit annually is about 0.45 tonnes.

The effect of seismic waves on the SCWR is investigated in this work. Only vertical motion has been considered in the current study to reduce complexity. A sinusoidal curve representing the vertical seismic acceleration has been used for the analysis. The axial power distribution, the sine wave's effects, and the degree of instability in the system's initial state, controlled by the ratio of power-to-flow at its core, will be investigated. The preliminary results are described in this paper using simpler models and substantial computed outcomes. The main goal of this research is to provide quantitative data that can be used to identify the critical effects of seismic waves on the SCWR system utilizing LPM. To achieve the goal, the short thermal-hydraulic in-house code is employed, which has been adjusted to account for external acceleration and gravity.

The persistent quest to address the ever-increasing global energy crisis accompanied by environmental issues has opened the flood gates for semiconductor photocatalysis. The past few decades have witnessed extensive applications of carbon-based nanomaterials (CNMs), especially carbon nanotubes, carbon quantum dots, graphene, and graphitic carbon nitride, as energy materials owing to their distinctive properties and the renewable, safe, and economic applicability of these materials. Their ability to inhibit charge recombination, provide a hydrophobic environment, and act as a sensitizer further enables them to enhance their overall photocatalytic efficiency. Inspired by this ongoing research, the present chapter discusses the viability of photocatalysis in energy generation and environmental remediation. Subsequently, utilization of CNMs and their composites in photocatalytic water splitting for hydrogen generation, photocatalytic carbon dioxide reduction, and photocatalytic dye degradation, together with the challenges encountered while using these CNMs for energy production have been likewise reviewed. Some common synthesis techniques along with recent developments in preparation approaches of CNMs highlighting their photocatalytic performances attributes are also explored. To conclude, this chapter aims at providing a suitable direction enabling advancements in the ongoing research work, which will not only enrich the future applications but will also contribute to confronting the remaining limitations appearing in this field.

Semiconductor heterostructures such as TiO2/Sb2S3 and ZnO/Sb2S3 are promising for photoelectrochemical applications. In this work, we demonstrate the photoelectrochemical performance with enhanced current densities of Sb2S3 sensitized nanorods of TiO2 (TNR) and ZnO (ZNR) photoanodes. ZNR and TNR are synthesized by the hydrothermal method and annealed in the air (AA) and hydrogen (HA) ambient. Stoichiometric Sb2S3 are coated on ZNR and TNR by a facile one-step method using thermolysis of Sb-MPA precursor in air ambient. Structural, optical and microstructural studies are carried out to confirm the formation of phase pure Sb2S3 on ZnO and TiO2 oxide nanorods in the TiO2/Sb2S3 and ZnO/Sb2S3 heterojunction photoanodes. Photoelectrochemical studies show enhanced performance from the Sb2S3 sensitized photoanodes in comparison to the bare TNR or ZNR photoanodes. TNR-AA/Sb2S3(MPA) and TNR-HA/Sb2S3(MPA) heterostructures exhibit current densities of 1.79 mA/cm2 and 1.58 mA/cm2, respectively, which is six times higher than the uncoated photoanodes (0.38 mA/cm2 for TNR-AA and 0.20 mA/cm2 for TNR-HA). In the case of ZNR, we see a 10 to 15 fold increase in current density (-3.3 to 4 mA/cm2) upon sensitizing with Sb2S3 on photoanodes. Further hydrogen annealed ZNR sensitized with Sb2S3 (ZNR-HA/Sb2S3(MPA)) shows a slightly higher current density (3.9 mA/ cm2) than the air annealed ZNR-AA/Sb2S3(MPA) (3.32 mA/cm2). Hydrogen annealing is beneficial for ZNR, whereas air-annealing is favored for TNR. Also, stability studies and photocurrent measurements are discussed for these photoanodes.

Single crystalline ZnO and TiO2 nanorods are grown on fluorine-doped tin oxide (FTO) substrates by hydrothermal method. The nanorods are annealed under air and reducing conditions to alter the oxygen nonstoichiometry and hence the mid-bandgap defect states. Such an annealing process is shown to impart significant change on the photoelectrochemical (PEC) performance of the photoelectrodes. Large photocurrent densities (J) of 0.78 mA cm−2 are obtained for air annealed (AA) TiO2 nanorods (TNR) compared to hydrogen annealed (HA) TNR (J = 0.36 mA cm−2). ZnO nanorods (ZNR), on the contrary, shows photocurrent density of 0.76 mA cm−2 and 0.36 mA cm−2 for ZNR-HA and ZNR-AA photoanodes, respectively. The contrasting difference in the PEC performance is attributed to the synergetic effect of interfacial impedance with electrolytes and the oxygen nonstoichiometry. Further, to overcome the limitation of light absorption by these materials owing to their wide bandgap, TiO2 and ZnO nanorods are coated with Sb2S3 by chemical bath deposition to form heterostructured TNR-AA/Sb2S3(CBD) and ZNR-HA/Sb2S3(CBD) thin films. Such heterostructures exhibit enhanced photocurrent values of ∼1.39 mA cm−2 and 3.36 mA cm−2 (at 1.6 V versus Ag/AgCl), respectively. The PEC performances of the nanorods are analyzed in terms of the annealing conditions and subsequent introduction of defect states in the bandgap. The present study shows the importance of oxygen defect control at the interface between the oxide and chalcogenide, and its role in the betterment of PEC performance in TiO2/Sb2S3 and ZnO/Sb2S3 heterostructure photoanodes.