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At least 1,027 records · Page 57

Image Classification Using Convolutional Neural Networks to Automate Visual Inspection of CCO Containers (Rev. 1)

The Savannah River Site is automating the receipt and inspection of Criticality Control Overpack drums to aid in nuclear waste disposal. The Advanced Engineering group is automating this receipt and inspection process to assist the site’s goals. Part of this process includes visually inspecting CCO drums to ensure that no defects or security risks are present. This research focuses on developing a machine learning model to automatically classify drums as passing or failing inspection. The machine learning model was implemented using the open-source Tensorflow library and uses a Convolutional Neural Network architecture to extract features and differentiate between images. The model was able to achieve an average of 85% accuracy on a dataset of 288 drums. This project’s goal was to prove the validity of computer vision in an automation process and provide a starting place for continued research.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

R-Adaptivity to Enable Compression of Elementary Computations in Extreme-Scale Finite Element Simulators

Modern computing systems are capable of exascale calculations, which are revolutionizing the development and application of high-fidelity numerical models in computational science and engineering. While these systems continue to grow in processing power, the available system memory has not increased commensurately, and electrical power consumption continues to grow. A predominant approach to limit the memory usage in large-scale applications is to exploit the abundant processing power and continually recompute many low-level simulation quantities, rather than storing them. However, this approach can adversely impact the throughput of the simulation and diminish the benefits of modern computing architectures. We present three novel contributions to reduce the memory burden while maintaining, and sometimes improving, performance in simulations based on finite element discretizations. The first contribution develops dictionary-based data compression schemes that detect and exploit the structure of the discretization, due to redundancies across the finite element mesh. While these schemes are shown to reduce memory requirements by more than 99% on meshes with large numbers of identical mesh cells, there are applications where this structure does not exist. The second contribution leverages a recently developed augmented Lagrangian optimization algorithm to enable r-adaptivity for meshes with the goal of enhancing the redundancies in the mesh. The third contribution extends these methods to patch-based linear solvers and preconditioners by compressing local matrices. Numerical results demonstrate the effectiveness of the proposed methods to detect, enhance and exploit mesh structure on a suite of examples inspired by large-scale applications.

97 MATHEMATICS AND COMPUTING↗

Conformal Hierarchical Simulation-Based Inference with Local Validity

Trustworthy and interpretable uncertainty quantification is a long-standing challenge in artificial intelligence. Simulation-based inference (SBI) comprises a broad swath of approaches for estimating latent parameters with uncertainties. Although flexible neural density estimators in SBI can be remark- ably expressive capturing highly structured, high-dimensional posteriors their credible regions can be badly mis-calibrated and are often only accompanied by heuristic coverage checks. We present the first SBI framework that delivers finite-sample local valid coverage guarantees that hold in the neighborhood of each observation. Our framework can couple any off-the-shelf hierarchical SBI engine with a confor- mal Bayesian post-processing step that operates on the posterior predictive density. A kernel-weighted conformity score adapts the conformal quantile to the local geometry of the data, yielding prediction sets that are simultaneously (i) marginally calibrated, (ii) locally valid, and (iii) hierarchical, handling global and observation-specific parameters in a single pass. Through experiments on synthetic data and benchmarks from neuroscience and physics, we show that our approach attains 1 − α coverage, where prior SBI methods under- or over-cover. Our approach also maintains a competitive, credible set size with minimal computational overhead. Finally, our approach can be used to make predictions on real data and give valid credible regions modulo weight-initialization-based model mis-specification.

Trivedi, Shubhendu [Fermilab]↗

Fracture Mechanics—Theory, Modeling and Applications

The field of fracture mechanics was developed during the throes of World War II, and since then, it has been a very active area of research. This is a very energetic and vibrant field because fracture processes are relevant for a vast range of engineering applications spanning different temporal and length scales. Depending on the application, material fracture and fragmentation may be either a desirable feature (for example, for the purpose of hydraulic fracturing operations), or something that is to be avoided at all costs (for example, in key components of mechanical systems). In some other instances, fractures are already present and must be accommodated through novel design (for example, in the case of earthquake ruptures occurring in the Earth’s crust). Because of this wide spectra of motivation, there is a continuous need for the fracture mechanics research community to gain more insight on how fractures are created, evolve, and interact with themselves and with material microstructures. In addition, there are still many remaining questions about how fracture propagation, growth, and interaction impact overall material behavior, and how to develop accurate models to predict this behavior.

36 MATERIALS SCIENCE↗

Recoveries of Oil and Hydrolyzed Sugars from Corn Germ Meal by Hydrothermal Pretreatment: A Model Feedstock for Lipid-Producing Energy Crops

Vegetable oil is extracted from oil rich seeds, such as soybeans. Genetic engineering of green plants to accumulate oil in vegetative tissue is a future source of oil that promises increased land productivity and the use of marginal lands. However, the low concentration of lipids in current engineered plant biomass samples makes the oil extraction process challenging and expensive. In this study, liquid hot water (LHW) pretreatment was investigated to enhance oil recovery from the solids and increase enzymatic hydrolysis efficiency of such feedstocks. Corn germ meal was chosen as a model feedstock representing lipid-producing energy crops. Germ meal was pretreated at 160 and 180 °C for 10 and 15 min at 20% w/w solids loading. Enzymatic hydrolysis on the pretreated solid was performed. After pretreatment, the oil concentration increased by 2.2 to 4.2 fold. The most severe pretreatment condition of LHW, at 180 °C for 15 min, gave the maximum oil concentration (9.7%, w/w), the highest triacylglycerol (TAG) content of the extracted oil (71.6%), and the highest conversions of glucose and xylose (99.0% and 32.8%, respectively). This study demonstrates that the optimal pretreatment condition for corn germ meal is 180 °C LHW for 15 min. Pretreatment improves lipids recovery from oil bearing biomass with little or no effect on the lipid profile.

09 BIOMASS FUELS↗

Interphase Engineering Enabled by Using a Separator with Electrochemically Active Carbazole Polymers for Lithium-Ion Batteries

Separators are generally considered inert components in lithium-ion batteries. In the past, some electroactive polymers have been successfully applied in separator modifications for overcharge protection or as acid scavengers. This study highlights the first use of two “electroactive” carbazole polymers (copolymer 9-phenyl-9H-carbazole-phenyl [PCP] and poly(9-vinylcarbazole) [PVC]), which were each applied separately as coatings on the cathode-facing side of commercial Celgard 2325 separators, respectively, to enhance the cycling performance of 0.3Li 2 MnO 3 ·0.7LiMn 0.5 Ni 0.5 O 2 //graphite (LMR-NM//Gr) full cells through interphase engineering. The team observed an irreversible polymer oxidation process of the carbazole-functionalized polymers—occurring only during the first charge—for the modified separator cells, and the results were confirmed by dQ/dV analysis, cyclic voltammetry measurements, and nuclear magnetic resonance characterizations. During this oxidation, carbazole polymers participate in the process of interphase formation, contributing to the improved cycling performance of LMR-NM//Gr batteries. Particularly, oxidation takes place at voltages of ~4.0 and ~3.5 V when PCP and PVC are used as separator coatings, which is highly irreversible. Further postmortem examinations suggest that the improvements using these modified separators arise from the formation of higher-quality and more inorganic SEI, as well as the beneficial CEI enriched in LixPOyFz. These interphases effectively inhibit the crosstalk effect by reducing TM dissolution.

carbazole↗

Ducted Fuel Injection vs. Conventional Diesel Combustion: Extending the Load Range in an Optical Engine with a Four-Orifice Fuel Injector

Ducted fuel injection (DFI) is a technique to attenuate soot formation in compression ignition engines relative to conventional diesel combustion (CDC). The concept is to inject fuel through a small tube inside the combustion chamber to reduce equivalence ratios in the autoignition zone relative to CDC. DFI has been studied at loads as high as 8.5 bar gross indicated mean effective pressure (IMEP g ) and as low as 2.5 bar IMEP g using a four-orifice fuel injector. Across previous studies, DFI has been shown to attenuate soot emissions, increase NO x emissions (at constant charge dilution), and slightly decrease fuel conversion efficiencies for most tested points. This study expands on the previous work by testing 1.1 bar IMEPg (low-load/idle) conditions and 10 bar IMEPg (higher-load) conditions with the same four-orifice fuel injector, as well as examining potential causes of the degradations in NO x emissions and fuel conversion efficiencies. DFI and CDC are directly compared at each operating point in the study. At the low-load condition, the intake charge dilution was swept to elucidate the soot and NO x performance of DFI. The low-load range is important because it is the target of impending, more-stringent emissions regulations, and DFI is shown to be a potentially effective approach for helping to meet these regulations. The results also indicate that DFI likely has slightly decreased fuel conversion efficiencies relative to CDC. We find that the increase in NO x emissions with DFI is likely due to longer charge gas residence times at higher temperatures, which arise from shorter combustion durations and advanced combustion phasing relative to CDC.

42 ENGINEERING↗

Oscilloscope Data Push Program

This paper details the development of a Python program designed to automate the data acquisition and conversion for an oscilloscope for the purposes of a one-off/temporary data acquisition system for users that readily need data, and do not have the option of obtaining a Data Acquisition (DAQ) solution. Creating DAQ systems for analyzing a system requires expensive electronics and a dedicated team of engineers for support. Traditionally, manual data collection and processing are time consuming and prone to error. By automating these processes, the cost, efficiency and accuracy of data handling are improved upon. This project involves the creation of a program that interacts with the oscilloscope. During this interaction, there are various functions being performed such as the acquisition of waveform data via floating points, generating plots with the acquired wave points, and storing of floating points in a CSV file format for future reference and plotting purposes. While the initial aim of the project included continuous logging to a cloud database, this was deferred due to time constraints. The results portrayed an almost-instant rate of data collection with a buffer time, showcasing the potential for further integration and real-time data processing.

Osei-Tutu, Jason↗

In situ Monitoring of Lanthanide Reactions with Oxide Species via Combined Absorption Spectroscopy and Electrochemical Methods

Molten salts for engineering scale applications of spent nuclear fuel pyrochemical processing will inevitably have some level of oxygen impurities which can form insoluble oxide and oxychloride species with fission products. This work demonstrates real-time concentration monitoring of two lanthanide (Ln3+) fission products, Nd3+ and Pr3+, and their reactions with oxygen (O2-) impurities to form insoluble products in LiCl-NaCl-KCl eutectic salt. Combined absorption spectroscopy and electrochemical testing were used to track lanthanide concentrations. O2- impurity levels were controlled by adding Li2O to lanthanide-salt solutions. After the introduction of O2- impurities, Ln3+ concentrations were monitored via time-resolved absorption spectroscopy. Concentrations of both Ln3+ species in solution decreased with time as insoluble products formed. The initial impurity concentration controlled whether insoluble products were predominantly oxychlorides or mixtures of oxychlorides and oxides. However, absorption spectroscopy is limited for weakly absorbing species, such as Pr3+, and under conditions of high impurity concentrations where solutions can be turbid. To circumvent this limitation, the concentrations of Pr3+ were monitored with square wave voltammetry (SWV). Reaction rates and extent of Pr3+ removal from solution as monitored by SWV agreed closely with results found from spectroscopic monitoring. This demonstrates that simultaneous electrochemical testing complements the capabilities of absorption spectroscopy to monitor reactions of fission products in molten salts.

absorption spectroscopy↗

In situ monitoring of lanthanide reactions with oxide species via combined absorption spectroscopy and electrochemical methods

Molten salts for engineering scale applications of spent nuclear fuel pyrochemical processing will inevitably have some level of oxygen impurities which can form various insoluble oxide and oxychloride species with uranium and fission products. This work demonstrates real-time concentration monitoring of two trivalent lanthanide (Ln3+) fission products, Nd3+ and Pr3+, and their reactions with oxygen (O2-) impurities to form insoluble products in LiCl-NaCl-KCl eutectic salt. A combination of high-temperature absorption spectroscopy and electrochemical testing were used to track lanthanide concentrations. O2- impurity levels were controlled in the range of 0.001 M to 0.5 M by adding Li2O to lanthanide-salt solutions. After the introduction of O2- impurities, Ln3+ concentrations were monitored via time-resolved absorption spectroscopy. Concentrations of both Ln3+ species in solution decreased with time as insoluble products formed. The initial impurity concentration controlled whether insoluble products were predominantly oxychlorides (LnOCl) or mixtures of oxychlorides and oxides (Ln2O3). However, absorption spectroscopy is limited for weakly absorbing species, such as Pr3+, and under conditions of high impurity concentrations where solutions can be turbid. To circumvent this limitation, the concentrations of Pr3+ were monitored with square wave voltammetry (SWV). Estimated reaction rates and extent of Pr3+ removal from solution as monitored by SWV agreed to within ~10% of the results found from absorption spectroscopy monitoring. This demonstrates that simultaneous electrochemical testing complements and expands the capabilities of absorption spectroscopy to monitor reactions of fission products in molten salts.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

In situ monitoring of lanthanide reactions with oxide species via combined absorption spectroscopy and electrochemical methods

Molten salts for engineering scale applications of spent nuclear fuel pyrochemical processing will inevitably have some level of oxygen impurities which can form insoluble oxide and oxychloride species with fission products. This work demonstrates real-time concentration monitoring of two lanthanide (Ln3+) fission products, Nd3+ and Pr3+, and their reactions with oxygen (O2-) impurities to form insoluble products in LiCl-NaCl-KCl eutectic salt. Combined absorption spectroscopy and electrochemical testing were used to track lanthanide concentrations. O2- impurity levels were controlled by adding Li2O to lanthanide-salt solutions. After the introduction of O2- impurities, Ln3+ concentrations were monitored via time-resolved absorption spectroscopy. Concentrations of both Ln3+ species in solution decreased with time as insoluble products formed. The initial impurity concentration controlled whether insoluble products were predominantly oxychlorides or mixtures of oxychlorides and oxides. However, absorption spectroscopy is limited for weakly absorbing species, such as Pr3+, and under conditions of high impurity concentrations where solutions can be turbid. To circumvent this limitation, the concentrations of Pr3+ were monitored with cyclic voltammetry (CV). Reaction rates and extent of Pr3+ removal from solution as monitored by CV agreed closely with results found from spectroscopic monitoring. This demonstrates that simultaneous electrochemical testing complements the capabilities of absorption spectroscopy to monitor reactions of fission products in molten salts.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗

Development of prechamber enabled mixing-controlled combustion strategy for ultra-low methane emissions from lean burn natural gas engines

This numerical study explores the optimization of Prechamber Enabled Mixing-Controlled Combustion (PC-MCC) using natural gas in heavy-duty engines, aiming to enhance combustion efficiency to minimize methane slip and NOx emissions. The approach involves a prechamber ignition system, distinct from conventional spark ignition (SI) systems, to initiate combustion of direct injected natural gas. By leveraging the robust ignition characteristics of the prechamber, the PC-MCC method demonstrates significant potential in achieving efficient combustion akin to diesel engines but with lower greenhouse gas emissions. The research evaluates the effects of various geometric and operational parameters on the combustion process and emissions, including prechamber volume, nozzle diameter, direct injector (DI) geometry, and engine operating strategies. Computational Fluid Dynamics (CFD) simulations are utilized, focusing on a heavy-duty, single-cylinder engine modeled after the Caterpillar C9.3B engine. Key findings indicate that a prechamber volume of 3 cc, coupled with a nozzle diameter of 2.75 mm for two prechamber holes, strikes an optimal balance between combustion efficiency and emissions reduction. This configuration ensures robust combustion across a range of operating conditions while maintaining methane slip within targeted limits. Further investigation into DI geometry shows the significance of the injector umbrella angle and nozzle diameter in shaping the fuel-air mixing and combustion dynamics. An umbrella angle of 130° and a nozzle diameter of 300 microns are identified as optimal, promoting rapid and efficient combustion with minimized methane and NOx emissions. The study also investigates the impact of injection timing and pressure, highlighting their roles in controlling combustion timing and influencing emissions levels. Advanced injection timing is found to be crucial in achieving the desired low methane slip, whereas retarded injection timing assists to reduce NOx emissions while having a slight increase in methane emissions. Operating strategies incorporating various levels of Exhaust Gas Recirculation (EGR) are assessed for their effectiveness in further reducing emissions. The research demonstrates that a judicious combination of internal hot EGR and careful calibration of DI pressure and SOI timing can achieve significant reductions in NOx emissions while keeping methane slip under control. Specifically, an internal EGR level of 15%–25%, combined with DI pressures of 200–300 bar and injection timings at or after top dead center, is recommended. These findings contribute valuable insights into the development of advanced combustion techniques for natural gas engines, offering a viable pathway to reduce methane slip without compromising engine efficiency or performance. The PC-MCC system presents a promising solution for the future of heavy-duty natural gas engine technology to reduce methane emissions.

Nsaif, Osama↗

Current Status of the Synroc Waste Treatment Facility - 20263

Construction of the Synroc Waste Treatment Facility is well underway at ANSTO. This plant has been designed to treat intermediate level liquid waste arising from the production of Mo-99 within the newly constructed ANSTO Nuclear Medicine (ANM) precinct [1]. The facility utilises ANSTO Synroc technology that has been tailored to the chemical, physical, and radiological properties of the waste. The result is a highly durable wasteform with a significant reduction in the final volume of the treated waste. This paper presents the construction status of the Synroc Waste Treatment Facility and the status of the associated technology maturation plan. Construction of the building is scheduled for completion in late 2020 followed by process installation and commissioning. A key component of the technology maturation plan has been the construction of an Inactive Engineering Demonstrator [2]. This has aided in the mitigation of risks with respect to technology selection, process integration, and process nuclearisation. Furthermore, the demonstration facility has provided an environment for the design and development of the instrumentation and control philosophy resulting in a seamlessly integrated plant control system. Successful demonstration of the technology by the inactive engineering demonstrator has significantly reduced the associated risks. Details of the Synroc Waste Treatment Facility project progress will also be presented. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Multitude Characterization and Prediction of DOE Advanced Biofuels Properties

Advanced multitude of experiments ranging from the liquid fuel to combustion are conducted on Co-OPTIMA fuels to aid the characterization of the fuels. The series of targeted experiments characterized Co-OPTIMA fuel spray atomization, flame topology, flame speed, autoignition, volatility, viscosity, soot/coking, and compatibility. The fuels are selected and prioritized based on input from national lab members. The research characterized and predicted biomass-based, low greenhouse gas fuels and blends combustion, autoignition, and physical properties of mixtures of identified compounds at engine-relevant conditions, in particular those properties that blend non-linearly. The main challenge of Co-OPTIMA is the evaluation of a variety of biofuels and blends in all the reaction conditions that might be encountered in new high-efficiency engines. Despite the improved high-throughput experimental techniques, it seems unlikely that all of the performance metrics could be measured for all relevant petroleum derived, bio-derived molecules and mixtures, and reaction conditions. A series of targeted experiments ranging from the liquid fuel to the combustion process is required, and to extract the maximum information from each experiment. These targeted experiments evaluated how a specific fuel will perform in an engine. The series of targeted experiments are as follows: (a) Spray Atomization, Vaporization and Droplet Formation (b) Combustion Flame and Local Fuel/Air Image-Based Measurements (c) Laminar Flame Speed Measurements (d) Autoignition and Soot Measurements (e) Synchrotron Coupled Fundamental Autoignition Experiments (f) Fuel Coking and Hot Surface Deposit g) Fuel Volatility Measurements (h) Viscosity Measurements (i) Seal Flexible Fuel Compatibility These experimental processes provide an essential pathway for the prediction of fuel behaviors in engines and systematic process for fuel down select.

09 BIOMASS FUELS↗

Enhancing lipid production in plant cells through automated high-throughput genome engineering and phenotyping

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. Furthermore, these innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.

59 BASIC BIOLOGICAL SCIENCES↗

Data for "Enhancing Lipid Production in Plant Cells through Automated High-Throughput Genome Engineering and Phenotyping"

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.

AI/ML↗

Chloride modification of vacuum-assisted blade-coated perovskite solar cells and mini-modules in ambient environment

Scalable deposition of perovskite thin films under ambient conditions remains a key challenge for the commercialization of perovskite photovoltaics, primarily due to the difficulty of controlling crystallization kinetics and suppressing non-radiative recombination during film formation. In this work, we present an effective strategy to modulate crystallization kinetics and non-radiative recombination in vacuum-quenched blade-coated perovskite films processed in air using chloride-based additives. By systematically comparing PbCl 2 and 3-chloropropylamine hydrochloride (Cl-PACl), we elucidate how different chloride incorporation pathways influence crystallization behavior and optoelectronic properties. In situ transmission and in situ photoluminescence measurements reveal that PbCl 2 acts as a transient additive that slows intermediate/perovskite phase formation while suppressing non-radiative recombination, leading to improved film quality. In contrast, Cl-PACl introduces additional trap states that deteriorate optoelectronic performance. As a result, PbCl 2 -modified perovskite solar cells achieve a champion power conversion efficiency (PCE) of 24.2% and remarkable operational stability, retaining over 90% of their initial performance after 800 hours of continuous illumination. Furthermore, we successfully scaled the process, fabricating a perovskite mini-module that delivered a champion efficiency of 18.0%. This study underscores the critical importance of coupling vacuum-assisted blade coating with transient additive engineering for achieving high-performance, stable, and scalable perovskite photovoltaics processed in ambient air.

14 SOLAR ENERGY↗