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Buonassisi, Tonio

Publications and source records attributed to Buonassisi, Tonio.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

High-throughput micro-scale bandgap mapping for perovskite-inspired materials with complex composition space

Abstract To realize the full promise of high-throughput experimental workflows, the rate of sample synthesis must be matched by that of characterization. Of growing interest are contactless optical techniques that can rapidly measure material homogeneity and properties. Here, we present a hyperspectral imaging method to measure local optical bandgap distributions within samples, utilizing spatially-resolved reflectance spectra coupled with automated data analysis. We collect approximately one million optical bandgap data across the compositional space of Cs 3 (Bi x Sb 1-x ) 2 (Br y I 1-y ) 9 perovskite-inspired materials. Our results show non-monotonic bandgap variations (i.e., bandgap bowing) along six composition gradient sequences, in addition to identifying samples with multiple bandgaps in statistics. High-throughput transient absorption spectroscopy reveals that within these compositions, the depletion of the ground state carriers to excited states occurred at discrete energy levels with independent carrier dynamics, consistent with the bandgap observation and indicative of phase separation. This work demonstrates the potential for rapid optical measurements to assess material quality and homogeneity in a high-throughput experimental setting, supporting screening and recipe optimization of optoelectronic material candidates with desired carrier dynamics and optical properties.

Science & Technology - Other Topics↗
DOE OSTI2024

Machine Learning Accelerates Innovation in Perovskite Manufacturing Scale-up (Final Technical Report (FTR))

We propose to address the challenge of the vast parameter space associated with perovskite manufacturing optimization, by developing a machine learning (ML)-assisted optimization framework for a scalable perovskite PV manufacturing tool. This framework will be interpretable, sequential, and rapidly adaptable to upgraded systems (e.g., via transfer learning). The tool is an open-air rapid spray plasma process (RSPP) of perovskite films, which has already been established at Stanford and is a unique platform to test and deploy the proposed ML-guided framework because the RSPP technique is able to conduct optimization experiments with a high throughput, and easily adjust a wide range of process variables.

14 SOLAR ENERGY↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗
DOE OSTI2024

Strategic styles of hardware product development could accelerate commercialization in cleantech startups

Hardware-based startups risk having longer times-to-market, deterring investment in the clean technologies that are critical to a sustainable future. We interviewed 55 leaders at hardware startups, 20 of which are cleantech, mapped their development timelines, and found prototyping to be the longest development step (median of 19 weeks per prototype) regardless of prototype complexity or iteration. Qualitative interview analysis reveals the prototyping team’s choice of development style is a major factor affecting timeline. We define two development styles: natural and structured, typified by free-form exploration and rule-based execution, respectively. On average, natural development takes 35% less time than structured, and is thus preferred for early iterations, but adopting structure at strategic points is needed for timely commercialization. Critical points of transition to a structured style include adding new team members or engaging external partners, which demand clear communication and expectations. When pivoting to a new product or market, returning to a natural style is beneficial.

Looney, Erin (ORCID:0000000168959312)↗
DOE OSTI2023

Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)

Abstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cage Molecules Stabilize Lead Halide Perovskite Thin Films

The environmental stability of hybrid organic-inorganic perovskite (HOIP) materials needs to increase to enable their widespread adoption in thin-film solar and optoelectronic devices. Molecular additives have recently emerged as an effective strategy for regulating HOIP crystal growth and passivating defects. However, to date the choice of additives is largely limited to a dozen or so materials under the design philosophy that high crystallinity is a prerequisite for stable HOIP thin films. Here, in this study, we incorporate porous organic cages (POCs) as functional additives into perovskite thin films for the first time and investigate the HOIP-POC interaction via a combined experimental and computational approach. POCs are significantly larger than the small-molecule additives explored for HOIP synthesis to date but much smaller than polymeric sealants. Partially amorphized composites of MAPbI 3 (methylammonium lead iodide, HOIP) and RCC3 (an amine POC) form a network-like surface topography and lead to an increase in the optical bandgap from 1.60 to 1.63 eV. Further in situ optical imaging suggests that RCC3 can delay the MAPbI 3 film degradation onset up to 50x under heat and humidity stresses, showing promise for improving reliability in HOIP-based solar-cell and light-emitting applications. Furthermore, there is evidence of molecular interactions between RCC3 and MAPbI 3 , as fingerprinted by the suppressed N-H stretching mode in MA + from Fourier transform infrared (FTIR) spectra and density functional theory (DFT) simulations that suggest strong hydrogen bonding between MA + and RCC3. Given the diversity of POCs and HOIPs, our work opens a new avenue to stabilize HOIPs via tailored molecular interactions with functional organic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2021

Tailoring capping-layer composition for improved stability of mixed-halide perovskites

Incorporating a low dimensional (LD) perovskite capping layer on top of a perovskite absorber, improves the stability of perovskite solar cells (PSCs). However, in the case of mixed-halide perovskites, which can undergo halide segregation into single-halide perovskites, a systematic study of the capping layer's effect on mixed-halide perovskite absorber is still lacking. This study bridges this gap by investigating how the 1D perovskite capping layers on top of MAPb(I x Br 1–x ) 3 (x = 0, 0.25, 0.5, 0.75, 1) absorbers affect the films' stability. We utilize a new method, dissimilarity matrix, to investigate the image-based stability performance of capping-absorber pair compositions across time. This method overcomes the challenge of analyzing various film colors due to bandgap difference in mixed-halide perovskites. We also discover that the intrinsic absorber stability plays an important role in the overall stability outcome, despite the capping layer's support. Within the 55 unique capping-absorber pairs, we observe a notable 1D perovskite material, 1-methoxynaphthalene-2-ethylammonium chloride (2MeO–NEA–Cl or 9-Cl), that improves the stability of MAPbI 3 and MAPb(I 0.5 Br 0.5 ) 3 by at least 8 and 1.5 times, respectively, compared to bare films under elevated humidity and temperature. Surface photovoltage results also show that the accumulation of electrostatic charges on the film surface depends on the capping layer type, which could contribute to the acceleration/deceleration of degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Autonomous experimentation systems for materials development: A community perspective

Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Enhanced charge carrier lifetime and mobility as a result of Rb and Cs incorporation in hybrid perovskite

Alkali addition in organic–inorganic perovskite has become the standard recipe for achieving solar cells with efficiencies exceeding 20%, but the mechanism is not well understood. We use non-contact carrier lifetime measurements, mobility measurements, and synchrotron-based x-ray characterization techniques to show that there is a unique benefit to adding hybrid perovskite samples with Rb and Cs simultaneously. When either Rb or Cs is added, charge carrier mobility increases with alkali concentration. Charge carrier lifetime benefits from alkali incorporation as well, but is optimized with only moderate concentration at 1%. When both Rb and Cs are introduced, however, the high mobility is maintained and the charge carrier lifetime increases considerably. Our results show that when incorporated alone, Rb and Cs have very similar roles in a perovskite crystal, but when co-added, halide distribution becomes homogenized correlating with improved charge transport properties.

Erodici, Matthew P.↗
DOE OSTI2020

How machine learning can help select capping layers to suppress perovskite degradation

Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI 3 ) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules’ low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI 3 film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI3 stability lifetime by 4 ± 2 times over bare MAPbI 3 and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantitative Specifications to Avoid Degradation during E-Beam and Induced Current Microscopy of Halide Perovskite Devices

Degradation due to electron beam exposure has posed a challenge in the use of electron microscopy to probe halide perovskite materials and devices. Herein, the interaction between the electron beam and the perovskite across acceleration voltages and at low probe currents is investigated in a scanning electron microscope (SEM) by monitoring the electron-beaminduced current (EBIC) response in perovskite solar cells in a planview configuration. SEM probe conditions are identified where dozens of repeated scans over a single region of the perovskite solar cell induce minimal electronic degradation. Overall, the induced current response of the perovskite device is found to strongly depend upon the beam condition: rapid decay occurs at high beam powers, the current activates at the lowest beam powers, and a newfound quasi-steady response is revealed at intermediate beam conditions. A quantitative window for the successful conduction of e-beam studies with minimal electronic degradation is revealed by evaluating induced current response over a wide range of perovskite devices, which invites broader use of SEM-based characterization techniques, including EBIC, as powerful techniques for correlative microscopy investigations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Systems and methods for crack detection

Embodiments related to systems and methods of crack detection in wafers (e.g., silicon wafers for photovoltaics, photovoltaic devices including silicon wafers) are disclosed. In some embodiments, an apparatus may include a light source configured to illuminate a side of a wafer and a camera directed towards a first face of the wafer. In some embodiments, a long axis of a field of view of the camera may be angled relative to a propagation direction of the light source. In some embodiments, at least a portion of the field of view of the camera is offset from the path of propagation of light emitted from the light source through the wafer. In some embodiments, at least a portion of a light beam may be oriented at a positive non-zero angle relative to the first face of the wafer, and a dimension of the light beam normal to the first face of the wafer may be larger than a thickness of the wafer.

14 SOLAR ENERGY↗
DOE OSTI2020

Analysis of CdTe photovoltaic cells for ambient light energy harvesting

This paper investigates the suitability of CdTe photovoltaic cells to be used as power sources for wireless sensors located in buildings. We fabricate and test a CdTe photovoltaic cell with a transparent conducting oxide front contact that provides for high photocurrents and low series resistance at low light intensities and measures the photovoltaic response of this cell across five orders of magnitude of AM1.5G light intensity. Efficiencies of 10% and 17.1% are measured under ~1 W m -2 AM1.5G and LED irradiance respectively, the highest values for a CdTe device under ambient lighting measured to date. We use our results to assess the potential of CdTe for internet of things devices from an optoelectronic, as well as a techno-economic perspective, considering its established manufacturing know-how, potential for low-cost, proven long-term stability and issues around the use of cadmium.

14 SOLAR ENERGY↗