Device Configuration Research Articles

Lead-Free Perovskite Light-Emitting Diodes.

Metal halide perovskites have been identified as a promising class of materials for light-emitting applications. The development of lead-based perovskite light-emitting diodes (PeLEDs) has led to substantial improvements, with external quantum efficiencies (EQEs) now surpassing 30% and operational lifetimes comparable to those of organic LEDs (OLEDs). However, the concern over the potential toxicity of lead has motivated a search for alternative materials that are both eco-friendly and possess excellent optoelectronic properties, with lead-free perovskites emerging as a strong contender. In this review, the properties of various lead-free perovskite emitters are analyzed, with a particular emphasis on the more well-reported tin-based variants. Recent progress in enhancing device efficiencies through refined crystallization processes and the optimization of device configurations is also discussed. Additionally, the remaining challenges are examined, and propose strategies that may lead to stable device operation. Looking forward, the potential future developments for lead-free PeLEDs are considered, including the extension of spectral range, the adoption of more eco-friendly deposition techniques, and the exploration of alternative materials.

A deep brain stimulation-conditioned RF coil for 3T MRI.

To develop and test an MRI coil assembly for imaging deep brain stimulation (DBS) at 3 T with a reduced level of local specific absorption rate of RF fields near the implant. A mechanical rotatable linearly polarized birdcage transmitter outfitted with a 32-channel receive array was constructed. The coil performance and image quality were systematically evaluated using bench-level measurements and imaging performance tests, including SNR maps, array element noise correlation, and acceleration capabilities. Electromagnetic simulations and phantom experiments were performed with clinically relevant DBS device configurations to evaluate the reduction of specific absorption rate and temperature near the implant compared with a circular polarized body coil setup. The linearly polarized birdcage coil features a block-shaped low electric field region to be co-aligned with the implanted DBS lead trajectory, while the close-fit receive array enables imaging with high SNR and enhanced encoding capabilities. The 3T coil assembly, consisting of a rotating linear birdcage and a 32-channel close-fit receive array, showed DBS-conditioned imaging technology with substantially reduced heat generation at the DBS implants.

On the Design of a Simulation-Assisted Human-Centered Quasi-Stiffness-Based Actuator for Ankle Orthosis

Most exoskeletons designed to assist users in load-bearing tasks face a mechanical dilemma in their conception. Designers may find a compromise between stiff active actuators-based architectures which are powerful but bulky and compliant actuator-based designs which are much less assistive but less constraining for users. This article presents a new open-source simulation-based design tool and a human-centered method that lets orthosis designers explore different device configurations and evaluate some performance criteria. This framework was applied in three different young-adult subjects. The effects of design personalization on user morphology and gait were studied. First, an ankle–foot orthosis designed to support a 20 kg backpack was defined according to the user’s height, weight, and walking speed. Then, a simulation of the subjects fitted with their customized design walking at a self-selected speed on flat ground carrying this additional load was performed. First, the results showed that the designed method inspired by natural joint stiffness behavior provided viable personalized mechanisms. Second, significant reductions in peak joint torque and mean joint activity were observed when comparing muscle-generated torques while the subject was wearing the 20 kg backpack with ankle–foot orthoses on both legs or without. Finally, it shows the value of an open-access tool for exploring the coupling of passive and active actuators to generate lighter and more compliant designs.

Wrinkled Rhenium Disulfide for Anisotropic Nonvolatile Memory and Multiple Artificial Neuromorphic Synapses.

Two-dimensional materials are emerging as potential solutions for high-density nonvolatile memory and efficient neuromorphic computing. However, integrating multidimensional memory and an ideal linear weight updating synapse in a simple device configuration to achieve versatile biomimetic neuromorphic systems remains challenging. Here, we introduce a wrinkled rhenium disulfide (ReS2) transistor, where the wrinkled structure facilitates the carrier trapping/detrapping at the dielectric interface, thus enabling the fusion of nonvolatile memory and both electronic and optoelectronic synaptic functionalities. As a nonvolatile memory, anisotropic wrinkled ReS2 can yield three distinct sets of data across three crystal orientations under identical programming operations. Each set demonstrates exceptional retention and endurance properties. As a neuromorphic synapse, it realizes the linear and symmetric updates of conductance states up to 9 bits and 8 bits, the ultra-low-energy consumption of 75 fJ and 2.5 pJ under the electrical and optical stimuli, respectively. The artificial neural network (ANN) based on electronic synapses gives a superior recognition accuracy of 92.9% for the original handwritten digits. The anisotropic synaptic responses and multiwavelength sensitivities of optoelectronic synapses enable them to execute advanced memory and recognition functions for complex images that encompass a variety of pattern features or color information. This underscores its substantial potential for integration into efficient biomimetic visual systems.

Design and economic analysis of energy storage optimization allocation for campus microgrid

In the twenty-first century, the "double carbon" plan is advancing steadily. Energy saving and emission reduction is one of the key points of the plan. A power grid independent park, if energy storage equipment can be configured, it can greatly improve the park microgrid power utilization, in order to achieve the purpose of energy saving. Therefore, research on the optimization of the proportion of power grid and microgrid in the park and the optimization of the configuration of energy storage devices can greatly improve the energy utilization rate, so as to achieve the significance of environmental protection. After collecting and sorting the daily load, daily wind power generation and 12-month daily wind power generation data of the three parks A, B and C, and processing the data, a linear function model is established. After using linear programming, operations research and genetic algorithm, the research results are obtained when the three parks are combined and equipped with 50kW/100kWh energy storage devices.

Decoding roughness perception in distributed haptic devices

Abstract The ability to render realistic texture perception using haptic devices has been consistently challenging. A key component of texture perception is roughness. When we touch surfaces, mechanoreceptors present under the skin are activated and the information is processed by the nervous system, enabling perception of roughness/smoothness. Several distributed haptic devices capable of producing localized skin stretch have been developed with the aim of rendering realistic roughness perception; however, current state-of-the-art devices rely on device fabrication and psychophysical experimentation to determine whether a device configuration will perform as desired. Predictive models can elucidate physical mechanisms, providing insight and a more effective design iteration process. Since existing models (1, 2) are derived from responses to normal stimuli only, they cannot predict the performance of laterally actuated devices which rely on frictional shear forces to produce localized skin stretch. They are also unable to predict the augmentation of roughness perception when the actuators are spatially dispersed across the contact patch or actuated with a relative phase difference (3). In this study, we have developed a model that can predict the perceived roughness for arbitrary external stimuli and validated it against psychophysical experimental results from different haptic devices reported in literature. The model elucidates two key mechanisms: 1) The variation in the change of strain across the contact patch can predict roughness perception with strong correlation 2) The inclusion of lateral shear forces is essential to correctly predict roughness perception. Using the model can accelerate device optimization by obviating the reliance on trial-and-error approaches.

Understanding the Impact of SAM Fermi Levels on High Efficiency p-i-n Perovskite Solar Cells.

Completing the picture of the underlying physics of perovskite solar cell interfaces that incorporate self-assembled molecular layers (SAMs) will accelerate further progress in p-i-n devices. In this work, we modified the Fermi level of a nickel oxide-perovskite interface by utilizing SAM layers with a range of dipole strengths to establish the link between the resulting shift of the built-in potential of the solar cell and the device parameters. To achieve this, we fabricated a series of high-efficiency perovskite solar cells with no hysteresis and characterized them through stabilize and pulse (SaP), JV curve, and time-resolved photoluminescence (TRPL) measurements. Our results unambiguously show that the potential drop across the perovskite layer (in the range of 0.6-1 V) exceeds the work function difference at the device's electrodes. These extracted potential drop values directly correlate to work function differences in the adjacent transport layers, thus demonstrating that their Fermi level difference entirely drives the built-in potential in this device configuration. Additionally, we find that selecting a SAM with a deep HOMO level can result in charge accumulation at the interface, leading to reduced current flow. Our findings provide insights into the device physics of p-i-n perovskite solar cells, highlighting the importance of interfacial energetics on device performance.

Numerical investigation of graphene derivatives as HTL in PBDB-T:NCBDT bulk heterojunction organic solar cell

The damaging effects of the most widely used PEDOT:PSS have led researchers to search for an alternative hole transport layer (HTL) in non-fullerene acceptor (NFA) based bulk heterojunction organic solar cells (BHJOSC’s). This work highlights the numerical simulation study of possible new approach of utilizing graphene, graphene oxide (GO), reduced graphene oxide (rGO), and p type doped graphene (p-graphene), as an alternative to PEDOT:PSS in non-fullerene acceptor based bulk heterojunction organic solar cell (NFABHJOSC) with PBDB-T as donor and NCBDT as acceptor using SCAPS1D. NCBDT is an emerging small-molecule non-fullerene acceptor (SM-NFA) whose bandgap can be tuned and reduced, making it a better option as an acceptor. Validation of the software is done by matching simulation results with experimental results. Diverse electron transport layers (ETL’s), including PDINO, ZnO, IGZO, TiO2, TiO2:gr (20%) composite, and TiO2:gr (10%) composite, are introduced, and device performance of various configurations is analysed. Optimization of the best device configuration ITO (4.8 eV)/rGO/PBDB-T:NCBDT/PDINO/Al gave an improved efficiency of 17.47%, fill factor (FF) of 77.45%, short circuit current density (Jsc) of 25.407 mA/cm2, and an open circuit voltage (Voc) of 0.8878 V.

Hafnia-based neuromorphic devices

The excellent complementary metal-oxide-semiconductor compatibility and rich physicochemical properties of hafnia-based materials, in particular the unique ferroelectricity that surpasses of conventional ferroelectrics, make hafnia-based devices promising candidates for industrial applications. This Perspective examines the fundamental properties of hafnia-based materials relevant to neuromorphic devices, including their dielectric, ferroelectric, antiferroelectric properties, and the associated ultra-high oxygen-ion conductivity. It also reviews neuromorphic devices developed leveraging these properties, such as resistive random-access memories, ferroelectric random-access memories, ferroelectric tunnel junctions, and (anti)ferroelectric field-effect transistors. We also discuss the potential of these devices for mimicking synaptic and neuronal functions and address the challenges and future research directions. Hafnia-based neuromorphic devices promise breakthrough performance improvements through material optimization, such as crystallization engineering and innovative device configuration designs, paving the way for advanced artificial intelligence systems.

Development of the Viscous Plane Damper Applicable in Limited Space within Structures Subjected to Dynamic Loads

Shipping impact and wave loads impose dynamic loads on jetties and platforms in the sea, which cause the vibration of structures. Recently, many advanced viscous damper devices have been developed for implementation in structures to diminish structural vibration due to earthquakes or wind. However, the longitudinal configuration of conventional viscous damper devices requires adequate space to locate the damper device within the frame structure, which limits the application of viscous dampers for use in jetties or platforms to dissipate the vibrations imposed by ship impact or wave force. For this reason, in this study, an attempt has been made to develop a new viscous plane damper device applicable in limited space positions where the longitudinal damper device is not able to fit. For this purpose, the initial design for the viscous plane damper device is proposed, and the prototype of the device is manufactured. Then, the performance of the fabricated viscous plane damper is examined through experimental tests by applying cyclic loads using a dynamic actuator. In order to investigate the effect of the diameter and configuration of the piston’s orifices, five different diameters for the orifices of 1, 2, 5, 8, and 10 mm are included, and three different distribution configurations of the orifices in the piston plate as Configurations A, B, and C are manufactured and tested experimentally. The lab testing is conducted by applying cyclic loads with different frequencies to evaluate the performance of the developed plane damper device under various load velocities. Accordingly, the dynamic performance of the damper device, including the damping force, effective damping and stiffness and the energy dissipation capacity obtained from the hysteresis response (force–displacement result), is investigated. The results of the experimental tests prove the functionality of the developed device to generate the desired damping force and vibration energy dissipation during applied cyclic loads. Therefore, the new plane damper device can be implemented in any structure to dissipate the effect of imposed vibration.

"My Best Friend's Husband Sees and Knows Everything": A Cross-Contextual and Cross-Country Approach to Understanding Smart Home Privacy

As smart home devices proliferate, protecting the privacy of those who encounter the devices is of the utmost importance both within their own home and in other people's homes. In this study, we conducted a large-scale survey (N=1459) with primary users of and bystanders to smart home devices. While previous work has studied people's privacy experiences and preferences either as smart home primary users or as bystanders, there is a need for a deeper understanding of privacy experiences and preferences in different contexts and across different countries. Instead of classifying people as either primary users or bystanders, we surveyed the same participants across different contexts. We deployed our survey in four countries (Germany, Mexico, the United Kingdom, and the United States) and in two languages (English and Spanish). We found that participants were generally more concerned about devices in their own homes, but perceived video cameras—especially unknown ones—and usability as more concerning in other people's homes. Compared to male participants, female and non-binary participants had less control over configuration of devices and privacy settings—regardless of whether they were the most frequent user. Comparing countries, participants in Mexico were more likely to be comfortable with devices, but also more likely to take privacy precautions around them. We also make cross-contextual recommendations for device designers and policymakers, such as nudges to facilitate social interactions.

Understanding the role of flux chamber configuration in the measurement of VOC emissions from porous media

The accurate quantification of volatile organic compound (VOC) emission rates from porous media to the air is a challenging problem, as measurements are affected by the chemical and physical characteristics of the porous media, and the operating parameters of the sampling device itself. The main objective of this study is to investigate how flux chamber (the most commonly used sampling device) configurations influence emission rate measurement from three selected porous media. Various parameters were studied, including sweep air flow rate, presence of a mixing fan, headspace volume and thickness of media. Controlled experiments focused on the behaviour of two VOCs commonly found in area sources: acetic acid and 1-butanol. Sweep gas flow rate emerged as the most influential factor, inducing turbulence and dilution over porous media surfaces and impacting emission rate measurements more significantly than headspace volume and fan installation. Variations in porous media properties also affected mass transfer, with emissions from coco coir showing higher mass transfer as its porosity and particle size facilitated gas transportation. While behaviour of acetic acid emission through the media supported the diffusion theory, emission of 1-butanol was affected by a combination of factors, highlighting the role of both diffusive and advective transport mechanisms. Understanding how flux chamber setups and porous media properties influence emission rates is crucial for accurately interpreting data. This knowledge also guides the design of studies, especially when investigating complex sources like biosolids and organic-amended soil.

Quantumness of electron transport in quantum dot devices through Leggett–Garg inequalities: A non-equilibrium Green’s function approach

Although coherent manipulation of electronic states can be achieved in quantum dot (QD) devices by harnessing nanofabrication tools, it is often hard to fathom the extent to which these nanoelectronic devices can behave quantum mechanically. Witnessing their nonclassical nature would thus remain of paramount importance in the emerging world of quantum technologies, since the coherent dynamics of electronic states plays there a crucial role. Against this backdrop, we resort to the general framework of Leggett-Garg inequalities (LGI) as it allows for distinguishing the classical and quantum transport through nanostructures by way of various two-time correlation functions. Using the local charge detection at two different time, we investigate here theoretically whether any quantum violation of the original LGI exists with varying device configurations and parameters under both Markovian and non-Markovian dynamics. Two-time correlators within LGI are derived in terms of the non-equilibrium Green’s functions (NEGFs) by exactly solving the quantum Langevin equations. The present study of non-Markovian dynamics of quantum systems interacting with reservoirs is significant for understanding the relaxation phenomenon in the ultrafast transient regime to especially mimic what happens to high-speed quantum devices. We can potentially capture the effect of finite reservoir correlation time by accounting for level-broadening at the electrodes along with non-Markovian memory effects. Furthermore, the large bias restriction is no longer imposed in our calculations so that we can safely consider a finite bias between the electronic reservoirs. Our approach is likely to open up new possibilities of witnessing the quantumness for other quantum many-body systems as well that are driven out of the equilibrium.

Copper-Based Para-Phthalic Acid Metal-Organic Framework: An Efficient Anodic material for Super-capattery Hybrid Systems

The battery-supercapacitor hybrid, an asymmetric energy storage system, has garnered considerable attention due to its outstanding energy storage capabilities. Anodic materials have become a central focus, driven by the quest for enhanced energy density, power density, cycle life, and overall performance. In this study, we have synthesized copper-based para-phthalic acid (PTA) metal-organic frameworks (MOFs) via a facile hydrothermal method to be employed as the positive electrode material in a battery-supercapacitor hybrid matrix. The initial testing indicates the battery-type nature of the prepared electrode along with maximum specific capacity of 260.5 C g-1 at 0.7 A g-1. Subjection of Cu-PTA MOF electrode to real device configuration (Super-capattery device) results in considerable specific capacity of 152.2 C g-1 along with specific energy and power of 35.9 Wh kg-1 and 5950 W kg-1, respectively at 0.3 A g-1. The device also demonstrated a stability potential of 92.15% when tested for 5000 continuous charge-discharge cycles. The present study presents Cu-PTA MOF an exciting addition for advancing current state of research in the field of energy storage technology.

Comparative Study of Zinc Sulfide, Tin Selenide, and Their Composite Electrocatalysts for Oxygen Evolution Reaction: Towards Efficient and Stable Water Splitting

Oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are critical processes in electrochemical water splitting, driving demand for efficient and durable electrocatalysts. This study presents a comprehensive evaluation of water splitting performance of ZnS@SnSe2 nano–composite in 1.0 M KOH solution. With ZnS nanoparticles decorated on SnSe2, exhibited remarkable catalytic activity for both HER and OER. ZnS@SnSe₂ composite demonstrated an low overpotential of 22 mV to reach a current density of 10 mA cm-2, outperforming ZnS (245 mV) and SnSe2 (280 mV). Furthermore, it achieved a Tafel slope of 25 mV dec-1 and a charge transfer resistance (Rct) of 5.2 Ω, reflecting its enhanced kinetics compared to ZnS (101 mV dec-1, Rct = 35 Ω) and SnSe2 (230 mV dec-1, Rct = 7.5 Ω). These results are even competitive with benchmark Pt/C catalyst (30 mV overpotential, 46 mV dec-1, and 2.2 Ω Rct), highlighting efficiency of composite for hydrogen production. For OER, ZnS@SnSe2 composite required an overpotential of 234 mV to reach 10 mA cm⁻², a substantial improvement over ZnS (361 mV) and SnSe2 (375 mV). Composite showcased a low Tafel slope of 48 mV dec-1 and an Rct of 1.5 Ω, comparable to commercial RuO2 catalyst (44 mV dec-1, Rct = 2.2 Ω), further underscoring its superior oxygen evolution capabilities. In practical device configuration, ZnS@SnSe₂/NF electrode achieved a Tafel slope of 21.335 mV dec-1 for HER and 58.028 mV dec-1 for OER, demonstrating its bifunctional efficiency in a two-electrode system. The device exhibited exceptional long-term stability over 50 hours of continuous operation, highlighting its potential for scalable and sustainable water splitting applications.

Passivator Materials Based on the Benzothiazole-Sulfonamide Hybrid: Synthesis, Optical, Electrochemical Properties, and Molecular Modeling for Perovskite Solar Cells

This study presents the synthesis of N-(4-(N-carbamimidoylsulfamoyl)phenyl)benzo[d]thiazole-2-carbohydrazonoyl cyanide (N-CSBC), a novel passivator material for perovskite solar cells (PSCs). The structure was confirmed by FTIR, ¹H NMR, and ¹³C NMR, while Hall-effect measurements revealed a hole mobility of 1.15 × 10³ cm²/Vs. UV-Vis analysis showed absorption peaks at 295 and 422 nm, corresponding to transitions related to azo groups and aromatic rings in N-CSBC. The compound exhibited an optical energy gap of 2.47 eV, consistent with DFT-based molecular modeling. Thermal stability and electrochemical properties further validated its suitability for photovoltaic applications. Incorporating N-CSBC into PSCs with an FTO/c-TiO2/MAPbI3/N-CSBC/spiro-MeOTAD/Ag device configuration resulted in a power conversion efficiency (PCE) of 16.21%, compared to 14.47% for the control. This work highlights N-CSBC's potential for cost-effective, high-efficiency defect passivation in PSCs.

Configuración de una red LAN utilizando Python scripting para generar redes definidas por software (SDN).

Network management is an essential task in modern organizations, where the complexity of technological infrastructures demands efficient and accurate solutions. With network devices from leading manufacturers such as Cisco and Mikrotik. In response to these challenges, automating the configuration of network devices has gained popularity as an approach to improve efficiency and reduce errors. Python, a programming language widely adopted for its versatility and ease of use, has become a key tool for this automation. Within this ecosystem, Netmiko emerges as a specialized library that facilitates automated interaction with network devices using SSH. Netmiko allows network administrators to write scripts that can connect to Cisco and Mikrotik devices, execute configuration commands, and retrieve information about the network status. This approach not only speeds up the configuration process, but also ensures consistency and accuracy in deploying configurations, especially in multi-device environments. As networks continue to grow in complexity, the adoption of tools such as Netmiko becomes essential to maintaining the operability and security of network infrastructures.

Issues and Challenges in Small-Signal Low-Power Amplifiers: A Review

Background: Contemporary linear electronic circuit designs frequently include small-signal low-power amplifiers as a fundamental building block. These amplifiers are widely used as LNAs (Low Noise Amplifiers) in the pre-amplifier stages of both low-frequency and high-frequency featured electronic circuits. Objectives: To review the recent advances in the design of small-signal low-power amplifiers based on device configuration of BJTs, JFETs and MOSFETs for low (≤1KHz) and high frequencies (≥1MHz) at smaller input voltage (<1 Volt). Methods: The articles published between 2010 to 2024 are shortlisted from IEEE Xplore, Springer, Science Direct Elsevier, Google Scholar, and MDPI databases and the outcomes of the different techniques are compared on various parameters. Findings: It is found that four-stage CMOS amplifier at TSMC 40nm process technology emerges with highest 84.59dB gain, InP (Indium Phosphide) Distributed Amplifier Using 3-D Interdigital Capacitors carries widest bandwidth of 160 GHz, two stage darlington pair amplifier at 0.18μm under dual input and dual output topologies and four stage CMOS amplifier at 40nm technology realizes lower power consumption of 0.5mW and 0.00086 mW respectively. Small Signal Amplifier based on single stage BJT-JFET Sziklai pair operates at lowest frequency of 11.36 Hz, single stage LNA at 180nm CMOS technology using inductive degenerate topology works at very low supply voltage at +0.5V and, recently reported complementary sziklai based small signal amplifier under CC mode works at lowest -15V supply voltage. Novelty: The review proposed on ‘Small Signal Low Power Amplifier’ is first time reported in this paper. Moreover, it discusses low power small signal amplifiers at both low (≤1KHz) and high (≥MHz) frequencies considering gain enhancement technique, power dissipation reduction technique, and noise reduction technique whereas other review papers emphasize on the general discussion of different topologies only at high frequencies. Keywords: Small-Signal Amplifiers, Low Power Amplifiers, BJTs, JFETs, MOSFETs

A combined DFT and finite difference simulation study on hybrid halide perovskite-based solar cells

This work reports the modelling and numerical estimation of the photovoltaic parameters of perovskite solar cells (PSCs) containing formamidinium lead iodide (FAPbI3) and formamidinium tin iodide (FASnI3) in the form of light active materials using SCAPS 1D software. The analysis is done by introducing diverse hole and electron transport materials and back metal contacts to identify the most appropriate device configuration that can deliver optimum photovoltaic output. The optoelectronic properties and the absorption spectra of the two compounds are estimated by DFT calculation using WIEN2k, which incorporates density functional theory (DFT) principles and are provided as input to SCAPS 1D to improve the accuracy of modelling study. For FAPbI3 as absorber material, the configuration FTO/IGZO/FAPbI3/NiO/Au shows the highest photovoltaic parameter exhibiting a power conversion efficiency (PCE) and fill factor (FF) of 19.75 % and 74.29 % respectively. With FASnI3 as the absorber material, FTO/PCBM/FASnI3/P3HT/Au gives a PCE and FF of 21.6 % and 67.74 % respectively. The work also includes the analysis of influence of defect density at the interface layers and series and shunt resistance on performance of above-mentioned device architectures to identify their limitations under real experimental conditions. The influence of various attributes of perovskite on the performance of the device is examined to determine their optimum values.

Influence of device configuration and noise on a machine learning predictor for the selection of nanoparticle small-angle X-ray scattering models

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.