Critical Parameters Research Articles

Host Dependency of Boundary between Strong and Weak Crystal Field Strength of Cr3+ Luminescence.

Cr3+ doped near-infrared phosphors hold significant applications and generate considerable research interest. The critical parameter for assessing the strength of the crystal field for Cr3+ in the Tanabe-Sugano diagram is the boundary value of Dq/B, representing the ratio of crystal field splitting to the Racah parameter B. Nevertheless, there are conflicting values for this parameter, as reported in various studies, such as 2.1, 2.2, and 2.3 for C/B = 4.5-4.8. Moreover, some Cr3+ doped phosphors with wide-band emissions exhibit a Dq/B value that falls within the region of a contradictory strong field. In this study, we numerically determine the boundary value of Dq/B, which distinguishes between strong and weak fields. The results then demonstrate a dependence on the host material and are correlated with the values of Racah parameters B and C. This work resolves the inconsistency between the boundary values of Dq/B and the emission profile of Cr3+, providing researchers with a more profound comprehension of Cr3+ luminescence.

Heparin Specifically Inhibits CRISPR/Cas12 Activation, Enabling Ultrasensitive Heparin Detection and Gene Editing Regulation.

Heparin is a highly sulfated linear glycosaminoglycan that is used as an anticoagulant to prevent and treat thrombotic diseases. Herein, we find that heparin specifically inhibits the activation of the Cas12 protein through the competitive binding of heparin and crRNA to Cas12. Studies illustrate that heparin's high molecular weight and strong negative charge are critical parameters for its inhibitory effect. This unexpected finding was engineered for the detection of heparin, affording a low detection limit of 0.36 ng/mL for fluorometric quantification. We further developed a rapid lateral flow-based system named HepaStrip (heparin strip), allowing heparin monitoring in clinical samples within 20 min. Finally, in vivo investigations revealed that heparin can regulate gene editing with the clusters of the regularly spaced short palindromic repeat (CRISPR)/Cas12 system in Escherichia coli. Heparin blocks the formation of Cas12-crRNA ribonucleoprotein, allowing the application of CRISPR for rapid and field-deployable detection of heparin and unleashing the potential use of heparin in future anti-CRISPR applications.

Geomechanical analysis of lost circulation control in tight formations

Unconventional oil and gas reservoirs, especially that in tight formations, contribute great parts to the global energy. During drilling in tight formations, lost circulation was one of the major problems, which can cause large amount of non operation time and millions of losses. In order to migrate the problem, lost circulation materials (LCMs) were used to prevent reopening of the fracture by isolating the fracture tip while the calculation of stress intensity factor (SIF) and fracture width is the key to LCMs design. In this paper, a dual porosity medium flow model suitable for tight formation is established to calculate the pressure distribution in fracture, and the fracture width and fracture reopening pressure (FROP) is then calculated by using the semi-analytical fracture mechanics model. Sensitivity analysis of critical parameters, for example, fracture length, wellbore radius, LCMs permeability, viscosity, wellbore pressure, and two rock-mechanics-related properties are implemented. The fracture width is larger in the formation with large horizontal principal stress anisotropy, low Young’s modulus and Poisson’s ratio. The increase in fracture length, wellbore radius and wellbore pressure also contributes to fracture opening. Meanwhile, we compared the situation before and after fracture plugging and the results emphasize that the fracture reopening is less likely to occur under the conditions of high viscosity and low permeability LCMs. The method proposed in this study can be used to calculate fracture width and FROP, which has potential significant application for lost circulation control in tight formation.

Design Improvement Using Topology Optimization for the Structural Frame Design of a 40 Ft LNG ISO Container Tank

LNG ISO tank containers are a solution for bulk liquefied natural gas (LNG) delivery to the outer islands of Indonesia that are not connected to the gas pipeline network. The design of an ISO tank frame must consider two critical parameters, strength/rigidity and weight saving, which affect the operational performance of the distribution process. The current investigation aims to numerically optimize the design of the structural frame of a 40 ft LNG ISO tank for a mini LNG carrier operation using a topology optimization framework. Two design solutions are used in the topology optimization framework: reducing the strain energy and mass retained. Mass retained was selected as the objective function to be minimized, which was assumed to be 60–80%. The proposed frame design is tested using three operational loading scenarios, including racking, lifting, and stacking tests based on the ISO 1496 standard. The convergence mesh tests were initially evaluated to obtain the appropriate mesh density in the finite element analysis (FEA). The simulation findings show that the topology optimization method of the frame design resulted in an improved design, with an increase in the strength-to-weight saving ratio. A promising result from the optimization scenario demonstrates weight savings of about 18.4–37.3%, with experienced stress below the limit criteria. It is found that decreasing mass retained causes a significant stress increase in the structural frame and ISO corner castings, especially in the stacking load. The critical recommendation in the frame design of the LNG ISO tank can be improved by eliminating the saddle support and bottom frame and increasing the thickness of the vertical frame.

A quality-by-design strategic approach for the development of bedaquiline-pretomanid nanoparticles as inhalable dry powders for TB treatment

Tuberculosis (TB) is caused by Mycobacterium tuberculosis (M.tb) and is the second leading cause of death from an infectious disease globally. The disease mainly affects the lungs and forms granulomatous lesions that encapsulate the bacteria, making treating TB challenging. The current treatment includes oral administration of bedaquiline (BDQ) and pretomanid (PTD); however, patients suffer from severe systemic toxicities, low lung drug concentration, and non-adherence. In this study, we developed BDQ-PTD loaded nanoparticles as inhalable dry powders for pulmonary TB treatment using a Quality-by-Design (QbD) approach. The BDQ-PTD combination showed an additive/synergistic effect for M.tb inhibition in vitro, and the optimized drug ratio (1:4) was successfully loaded into polymeric nanoparticles (PLGA NPs). The QbD approach was implemented by identifying the quality target product profile (QTPPs), critical quality attributes (CQAs), and critical process parameters (CPPs) to develop efficient design space for dry powder preparation using spray drying. The three-factorial and three-level Box-Behnken Design was used to assess the effect of process parameters (CPPs) on product quality (CQAs). The Design of Experiments (DoE) analysis showed different regression models for product quality responses and helped optimize process parameters to meet QTPPs. The optimized dry powder showed excellent yield (72 ± 2 % w/w), high drug (BDQ-PTD) loading, low moisture content (<1% w/w), and spherical morphology. Further, aerosolization performance revealed the suitability of powder for deposition in the respiratory airways of the lungs (MMAD 2.4 µm and FPF > 75 %). In conclusion, the QbD approach helped optimize process parameters and develop dry powder with a suitable quality profile for inhalation delivery in TB patients.

Design and simulation of a nano biosensor based on amorphous indium gallium zinc oxide (a-IGZO) thin film transistor

In this study, a biosensor utilizing a dielectric-modulated amorphous indium gallium zinc oxide (a-IGZO) thin film transistor (TFT) is introduced. TFT biosensors have garnered significant attention due to their heightened sensitivity, scalable nature, low power consumption, rapid electrical detection capabilities, and cost-effective means of mass production. By embedding a nano-cavity within the gate insulator of the TFT, biomolecules can accumulate within. As each biomolecule possesses its own dielectric constant, it modulates the effective gate capacitance and, subsequently, changes the channel conductance. To assess the sensitivity of the biosensor, variation in saturation current after the absorption of biomolecules with respect to the drain current in the case of an air-filled cavity has been considered as a precise measure. The efficient operation of a biosensor is contingent upon the sensitivity being highly dependent on the dielectric constant of the biomolecules that are accumulated within the nano-cavity. Consequently, a comprehensive evaluation has been conducted to ascertain the impact of critical design parameters which have the potential to affect the sensitivity of the biosensor. Additionally, a statistical analysis based on coefficient of variation measure has been performed to evaluate the susceptibility of the biosensor’s sensitivity to variations in geometrical and physical design parameters. The utilization of label-free detection methodology in this device presents a notable advantage due to its compatibility with the fundamental CMOS processing technology and its cost-effective potential for macro production.

A transformer fault diagnosis method based on EHBA-DHKELM-Adaboost model

Aiming at the problem of inaccurate transformer fault diagnosis in dissolved gas analysis, this paper proposes a novel diagnostic method that integrates an enhanced honey badger algorithm (EHBA) with an ensemble learning-based deep hybrid kernel extreme learning machine (DHKELM). First, kernel principal component analysis (KPCA) was deployed for feature fusion of the gas data, thus extracting more effective features. The DHKELM, combining polynomial and RBF kernel functions, was used as a base learning to build a powerful classifier with Adaboost framework. The EHBA introduces information sharing and firefly perturbation strategies based on HBA. This EHBA was harnessed to optimize the DHKELM’s critical parameters, establishing the EHBA-DHKELM-Adaboost transformer fault diagnosis model. Finally, the features garnered by KPCA were fed into the model, simulating and validating various fault diagnosis models. The findings reveal that EHBA-DHKELM-Adaboost achieves 98.75% diagnostic accuracy in transformer faults, surpassing other models.

Active microbial population dynamics and life strategies drive the enhanced carbon use efficiency in high-organic matter soils.

Microbial carbon use efficiency (CUE) is a critical parameter that controls carbon storage in soil, but many uncertainties remain concerning adaptations of microbial communities to long-term fertilization that impact CUE. Based on H218O quantitative stable isotope probing coupled with metagenomic sequencing, we disentangled the roles of active microbial population dynamics and life strategies for CUE in soils after a long-term (35 years) mineral or organic fertilization. We found that the soils rich in organic matter supported high microbial CUE, indicating a more efficient microbial biomass formation and a greater carbon sequestration potential. Organic fertilizers supported active microbial communities characterized by high diversity and a relative increase in net growth rate, as well as an anabolic-biased carbon cycling, which likely explains the observed enhanced CUE. Overall, these results highlight the role of population dynamics and life strategies in understanding and predicting microbial CUE and sequestration in soil.IMPORTANCEMicrobial CUE is a major determinant of global soil organic carbon storage. Understanding the microbial processes underlying CUE can help to maintain soil sustainable productivity and mitigate climate change. Our findings indicated that active microbial communities, adapted to long-term organic fertilization, exhibited a relative increase in net growth rate and a preference for anabolic carbon cycling when compared to those subjected to chemical fertilization. These shifts in population dynamics and life strategies led the active microbes to allocate more carbon to biomass production rather than cellular respiration. Consequently, the more fertile soils may harbor a greater microbially mediated carbon sequestration potential. This finding is of great importance for manipulating microorganisms to increase soil C sequestration.

Evaluation of plant growth and spacing effects on bioengineered slopes subjected to rainfall

Shallow landslides can be mitigated through the hydro-mechanical reinforcement provided by vegetation. Several critical parameters, such as plant spacing and plant age, play a significant role in influencing bioengineered slope stability facilitated by vegetation. However, the coupling of these effects on the stability of vegetated slope has been ignored. The objective of this study is to investigate the hydro-mechanical impact of vegetation growth and spacing on the stability of bioengineered slopes based on the predictions of a calibrated numerical model against field measurements. The impact of vegetation is investigated, with specific attention given to different plant spacing and growth stages, utilizing Schefflera arboricola. In the context of rainfall, it was observed that younger vegetation demonstrated more effective matric suction retention and recovery up to 25 kPa compared to the aged vegetation. Vegetation was revealed to substantially enhance the factor of safety up to 0.3 compared to the bare slope. Plant growth and reducing plant spacing increased the impact of root systems on both hydraulic and mechanical stability, primarily attributable to the influence of root cohesion rather than transpiration rates. The results revealed that the mechanical contribution to the factor of safety enhancement was raised from one-third to two-thirds because of the vegetation-induced cohesion within the growing rooted zone.

Unraveling potential phase transition mechanism of NBT-based systems: From an interesting view to survey ferroelectric loops

As for assessing strain performances of Na0.5Bi0.5TiO3-based systems, the driving field level is a critical parameter. Currently, both current density and strain rate evaluation methods have been utilized to characterize the driving field. However, we find that some discrepancies indeed exist between these two evaluation methods, especially for the highly ergodic condition. By means of the typical ferroelectric theory, the initial analysis for discrepant current density and strain rate loops reveals that the relaxor-ferroelectric transition is likely to be a multi-step transition process. Dynamic dielectric loss/permittivity evolution further confirms the speculation and suggests that partial transition steps may be consisted of successive transition events. Ferroelectric and dielectric loops not only provide a fantastic view to unravel the multi-step relaxor-ferroelectric transition process, but also guides to regulate the strain appraisal system.

Effect of Cooling Rate on the Microstructure and Mechanical Property of Nickel-Based Superalloy MAR-M247.

Heat treatment is an important process for optimizing the microstructures of superalloys, and the cooling rate after solid solution treatment is one of the most critical parameters. In this work, we treated solid solution MAR-M247 alloys with water quenching, air cooling, and furnace cooling. Microstructure characterization, hardness, and room temperature tensile tests were conducted to investigate the effect of cooling rate on the microstructure and mechanical properties of MAR-M247 alloys. The results showed that the cooling rate after solid solution treatment mainly affected the precipitation behavior of the secondary γ' phase, but it had few effects on other microstructure characterizations, including grain size, γ/γ' eutectic, and MC carbide. The water-quenched sample had the highest cooling rate (400 °C/s) and hardness (400 HV) but suffered from premature fracture because of quenching cracks. A further decrease in cooling rate from 1.5 °C/s to 0.1 °C/s deteriorated hardness (384 HV to 364 HV) and yield strength (960 MPa to 771 MPa) but increased elongation (8.5% to 13.5%). Moreover, the deformation mechanism was transformed from dislocation shearing to Orowan bypassing. The decreased yield strength was mainly due to the weakened precipitation strengthening resulting from γ'-phase coarsening. The improved elongation was attributed to not only the higher work-hardening index caused by interface dislocation networks but also the more uniform deformation, which delayed necking.

Lead-free alternative cation (Ethylammonium) in organometallic perovskites for thermoelectric applications.

Hybrid halide perovskites are gaining prominence as a promising option in the advancement of photovoltaic devices. Ethylammonium-based hybrid halide perovskites have demonstrated impressive characteristics, such as a reduced band gap, enhanced stability, and non-toxic properties. In this study, we have explored the structural, electronic, optical, and thermoelectric characteristics of Ethylammonium tin chloride. We have found that Ethylammonium tin chloride (EASnCl3) is a direct wide band gap semiconductor. Additionally, we conducted calculations for various optical parameters, including the dielectric function, absorption coefficient, and refractive index, across a photon energy spectrum ranging from 0 to 7eV. The research highlights the exceptional qualities of EASnCl3, which exhibits a high absorption coefficient and an elevated Seeback coefficient, among other favorable attributes. These findings position it as a promising material for cost-effective photovoltaic device applications, addressing concerns related to environmental stability. Fundamental properties based on the full-potential linearized augmented plane wave (FP-LAPW) method, this computation was performed using the WIEN2k simulation code. We utilized the exchange-correlation potentials PBE-GGA and KTB-mBJ to compute the optimized structure, density of states, and band structure of the material. In order to calculate the thermoelectric properties of the material, the Boltztrap simulation tool has been used. There are several critical absorbance parameters, including the Seeback coefficient, figure of merit, power factor, electrical conductivity, and thermal conductivity, concerning their carrier concentration and chemical potential, that have been taken into consideration.

Shadow and greybody bounding of a regular scale-dependent black hole solution

In this manuscript, we explore the shadow and the greybody bounding characteristics of a regular black hole within 4-dimensional space–time, employing the context of gravity that is scale-dependent. Our focus lies in determining limitations on the parameter denoted as ϵ̃, which serves as a descriptor for the scale-dependent solution with respect to the classically observed shadow radius Rsh as indicated in the documented data from the Event Horizon Telescope (EHT). Our result indicates that there is a unique value for ϵ̃ occurring at the reported mean of Rsh and the uncertainties could be the result of the fluctuating value of the scale-dependent parameter. We found that ϵ̃>0 is positive for Sgr. A*, but ϵ̃<0 for M87*. Utilizing M87* as a reference model, we scrutinized the shadow radius and weak deflection angle within the specified constraints. Discrepancies were observed not only in the shadow but also in the deflection angle of photons, particularly when the photon’s impact parameter closely approached the critical impact parameter.

Integrating environmental monitoring and bird attack prevention in fish farming: a combined solution for improved pond management

Fish farming plays a pivotal role in meeting the ever-increasing global demand for fish and seafood. Yet, fish farmers face formidable challenges in maintaining ideal water conditions and safeguarding their stock from avian predators. This research introduces a comprehensive solution that harnesses IoT technology, real-time monitoring, and bird deterrent mechanisms to heighten the efficiency of fish farming. Through IoT sensors, critical water parameters like temperature, pH, turbidity, and more are continuously monitored, providing real-time data accessible via an intuitive web application and SMS alerts. Motion detection, using passive inferred (PIR) based sensors, activates a water-spraying mechanism to repel birds and safeguard fish, thus eliminating the necessity for expensive and potentially harmful net enclosures. Our experiments underscore the system’s precision, with a mere 0.40 °C average temperature difference compared to traditional measurement tools. This integrated approach not only enhances sustainability and productivity but also reduces manual labour, minimizes losses, and preserves the environment, rendering fish farming more efficient and economically viable.

Experimental analysis of convective drying of paper and board

Conventional multi-cylinder drying of paper and board involves a mixture of conductive drying from steam-heated dryer cylinders and convective drying by the flow of heated air over the surface of the paper web in the pockets. Pocket ventilation is a critical component in assisting heat and mass transfer during the drying process but is the primary contributor toward removing evaporated water from the web. Air temperature, velocity, and humidity are critical parameters involved in the convective drying process. This paper covers an experimental study involving the design and development of a small lab-scale setup for convective drying of various grades of paper and board, monitoring multiple parameters like paper temperature, moisture content, air humidity, temperature, and velocity measured in situ as the drying proceeds with continuous and accurate sampling capabilities for all parameters in the sample and the system. Instantaneous drying rates, heat, and mass transfer coefficients were also deduced for every time step till the paper completely dried. Furthermore, the coefficients obtained were also reported in the form of dimensionless correlations, and the results were compared against traditional correlations used in the modeling of paper drying. This data will be useful in process development, modeling, design, and the paper drying process simulation.

The use of machine learning techniques to investigate the properties of metakaolin-based geopolymer concrete

The construction industry significantly contributes to global greenhouse gas emissions, highlighting the imperative for developing environmentally friendly construction materials. Geopolymers, particularly those utilizing metakaolin (MK), have emerged as a promising green alternative to conventional concrete. However, the acquisition of MK-based geopolymer concrete with optimal mechanical properties poses challenges due to numerous influential factors, disagreement over various findings, and the lack of a reliable predictive model. This study aimed to address this gap by employing a wide range of machine learning methods, namely gradient boosting machine, random forest, decision tree, artificial neural network, and support vector machine. Different optimization and regularization techniques were used to comprehensively understand the factors affecting the compressive strength of MK-based geopolymer concrete, including mixture design, chemical characteristics of the initial binder and activators, and different curing regimes. The results demonstrated the exceptional performance of the gradient boosting machine in predicting the compressive strength of MK-based geopolymer concrete, achieving a coefficient of determination of 0.983 and a mean absolute error of 1.615 MPa. Additionally, the study employed partial dependence plots, feature importance analysis, and SHapley Additive exPlanations (SHAP) to elucidate the proposed models. The coarse-to-fine aggregate ratio, H2O/Na2O molar ratio, extra water content, and sodium hydroxide concentration were identified as the most critical parameters affecting the compressive strength of MK-based geopolymer concrete. This research contributes to advancing the development of sustainable construction materials, streamlining experimental tasks, minimizing the need for labor and materials, improving time efficiency, and providing valuable insights for optimizing the design of MK-based geopolymer concrete.

Research on inversion of wheel-rail force based on neural network framework

Wheel-rail force is a critical parameter for vehicle-track-bridge (VTB) structure system, playing a pivotal role in ensuring the safe operation of trains and service performance of track and bridge structures. This paper proposes a methodological framework for efficient and accurate prediction of wheel-rail force, that bidirectional gated recurrent unit (Bi-GRU) neural network is developed and the hyperparameters of networks are optimized through grey wolf optimization (GWO). Based on the developed VTB system model, numerical experiments are carried out to produce datasets which are verified through field test. In order to make the data more universal and comprehensive, Latin hypercube sampling (LHS) is introduced to randomize the parameters of VTB model. The datasets generated from VTB system, with rail seat force as input and wheel-rail force as output, serve as the training data for framework. Additionally, the inversion effects under various working conditions are examined and discussed. The results indicate that the model can precisely inverse continuous wheel-rail forces from the moment the wheels enter the first test rail seat until leave the last test point of rail seat. When the neural network learns from the features of at least three or more rail seats on each track slab (with intervals not exceeding 2 rail seats), it can accurately inverse the time history curve of wheel-rail forces. The framework presents a viable alternative for the wheel-rail force analysis, and promising potential for the realization of online monitoring of wheel-rail force.

ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring.

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.

Genetic variation in heading dates and phenological parameters of Myanmar rice

ABSTRACT Genetic variation in heading dates is essential for developing a rational rice cultivation program, optimizing irrigation practices, and advancing plant breeding in year-round irrigated environments. In this study, we investigated the heading dates of eight rice varieties with varying degrees of photoperiod sensitivity under natural field conditions by conducting year-round periodic sowing in 2019 in Nay Pyi Taw, located in the central region of Myanmar. We elucidated genetic differences in critical day length by observing the longest day length at 30 days before heading, which is likely to be the starting point for floral induction. We analyzed two phenological models: the conventional developmental rate (DVR) model and a modified model considering the critical day length. These analyses aimed to uncover the genetic differences in phenological parameters: temperature sensitivity, photoperiod sensitivity, and earliness among rice varieties. Incorporating the critical day length into the DVR model significantly improved its accuracy in predicting heading dates, particularly for photoperiod-sensitive rice varieties. The parameters derived from the 2019 data proved effective for predicting heading dates in 2018, especially for photoperiod-sensitive varieties. The genetic variation in critical day length and model parameters could be valuable for adapting rice cultivars to different seasons and determining yield and agronomic practices for varietal development programs. These findings contribute to a deeper understanding of rice phenology and the genetic basis for photoperiod sensitivity in Myanmar rice.

A theoretical study of all-inorganic perovskite solar cells: Computational modeling of the CsPbI[formula omitted]/RbGeI[formula omitted] bilayer absorber structure

All-inorganic perovskite solar cells (AI-PSCs) have garnered significant attention in the perovskite community due to their stability and efficient photogeneration and absorption capabilities. Despite the toxicity associated with all-inorganic CsPbI3 perovskites containing lead (Pb), its presence remains crucial for achieving excellent photovoltaic properties. However, the imperative shift towards AI-PSCs using less toxic materials with suitable photovoltaic properties is imperative. The incorporation of a double perovskite absorber layer allows for broadening the absorption spectrum of PSCs and enhancing cell stability. In this study, we propose a novel AI-PSC structure with CsPbI3 as the top absorber with a wide bandgap of 1.69 eV and a lead-free RbGeI3 perovskite as the bottom absorber layer with a low bandgap of 1.31 eV. Utilizing numerical simulations with the SCAPS-1D simulator, we analyze the photovoltaic parameters of AI-PSCs with single and double absorber layers. Our investigations reveal that a double absorber layer structure, comprising CsPbI3 and RbGeI3 materials, outperforms a single-junction PSC based on CsPbI3. By optimizing critical device parameters, we achieved an impressive efficiency of 31.91% in an AI-PSC device with a CsPbI3(0.6 μm)/RbGeI3(2 μm) bilayer configuration. This study positions lead-free RbGeI3 perovskite as a promising partner for CsPbI3, offering a potential pathway for the development of highly efficient AI-PSCs.