Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization DOI Creative Commons
Min Gee Cho, Katherine Sytwu, Luis Rangel DaCosta

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(43), P. 29736 - 29747

Published: Oct. 19, 2024

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals various industrial applications, such as catalysis, sensing, energy conversion. However, traditional ensemble measurements often overlook the subtle distributions of individual nanocrystals, hindering establishment robust structure–property relationships. In this study, we uncover intricate evolutions growth mechanisms Co3O4 at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters cobalt precursor concentration water amount then using high resolution electron microscopy imaging to identify geometric features study provides insights into interplay between conditions size-dependent evolution colloidal nanocrystals. Utilizing population-wide data encompassing over 441,067 analyze their characteristics elucidate previously unobserved size-resolved evolution. This high-throughput analysis representing entire population accurately enables dependency regimes shaping Our findings provide experimental quantification regime transition based on crystals, specifically (i) faceting (ii) from thermodynamic kinetic, evidenced transitions convex concave polyhedral crystals. Additionally, introduce concept an "onset radius," which describes critical thresholds these occur. discovery has implications beyond achieving with desired morphology; it finely tuned correlation geometry material properties, advancing field its applications.

Language: Английский

Machine learning applications in nanomaterials: Recent advances and future perspectives DOI
Liang Yang, Hong Wang,

Deying Leng

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 156687 - 156687

Published: Oct. 1, 2024

Language: Английский

Citations

22
Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence DOI
Min A Kim, Qianxiang Ai, Alexander J. Norquist

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(22), P. 14514 - 14522

Published: May 22, 2024

Ligands play a critical role in the optical properties and chemical stability of colloidal nanocrystals (NCs), but identifying ligands that can enhance NC is daunting, given high dimensionality space. Here, we use machine learning (ML) robotic screening to accelerate discovery photoluminescence quantum yield (PLQY) CsPbBr3 perovskite NCs. We developed ML model designed predict relative PL enhancement NCs when coordinated with ligand selected from pool 29,904 candidate molecules. Ligand candidates were using an active (AL) approach accounted for uncertainty quantified by twin regressors. After eight experimental iterations batch AL (corresponding 21 initial 72 model-recommended ligands), decreased, demonstrating increased confidence predictions. Feature importance counterfactual analyses predictions illustrate potential field strength designing PL-enhancing ligands. Our versatile framework be readily adapted screen effect on wide range nanomaterials.

Language: Английский

Citations

6
Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization DOI
Xiaojing Xia, Eric Sivonxay, Brett A. Helms

et al.

Nano Letters, Journal Year: 2023, Volume and Issue: 23(23), P. 11129 - 11136

Published: Dec. 1, 2023

The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize brightness, these (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers compositional structural parameters multishell heterostructures make optimizing optical challenging. Here, we demonstrate use Bayesian optimization (BO) to learn structure design rules for UCNPs with bright ultraviolet violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures then simulates emission spectra using kinetic Monte Carlo. Yb3+/Er3+- Yb3+/Er3+/Tm3+-codoped nanostructures optimized this BO achieve 10- 110-fold brighter within 22 40 iterations, respectively. This can be expanded higher complexity, accelerating discovery novel while domain-specific knowledge is being developed.

Language: Английский

Citations

13
Unraveling the myths and mysteries of photon avalanching nanoparticles DOI
Artiom Skripka, Emory M. Chan

Materials Horizons, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Photon avalanching (PA) nanomaterials exhibit some of the most nonlinear optical phenomena reported for any material, allowing them to push frontiers applications ranging from nanoscale imaging and sensing computing. But PA remains shrouded in mystery, with its underlying physics limitations misunderstood. is not, fact, an avalanche photons, at least not same way that snowballs beget more snowballing actual avalanche. In this focus article, we dispel these other common myths surrounding lanthanide-based nanoparticles unravel mysteries unique effect. We hope removing misconceptions will inspire new interest harness giant nonlinearity across a broad range scientific fields.

Language: Английский

Citations

0
Chemically driven dimensionality modulation on hybrid tin (II) halide perovskites microcrystals DOI Creative Commons
Raúl Iván Sánchez Alarcón,

Omar Solís,

Cristina Momblona

et al.

Journal of Materials Chemistry C, Journal Year: 2024, Volume and Issue: 12(21), P. 7605 - 7614

Published: Jan. 1, 2024

Synthesis conditions determine the dimensionality of TEA–Sn perovskites from low emissive 2D structures to highly luminescent 0D structures. Br-derivatives can be tuned form and structures, Cl I-based cannot dimensional tuned.

Language: Английский

Citations

2
Antisolvent controls the shape and size of anisotropic lead halide perovskite nanocrystals DOI Creative Commons
Kilian Frank,

Nina A. Henke,

Carola Lampe

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 17, 2024

Language: Английский

Citations

2
Precursor Chemistry of Lead Bromide Perovskite Nanocrystals DOI
Jakob C. Dahl,

Ethan B. Curling,

Matthias Loipersberger

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(33), P. 22208 - 22219

Published: Aug. 8, 2024

We investigate the early stages of cesium lead bromide perovskite formation through absorption spectroscopy stopped-flow reactions, high-throughput mapping, and direct synthesis titration potential precursor species. Calorimetric spectroscopic measurements complex titrations combined with theoretical calculations suggest that complexes higher coordination numbers than previously considered for nonpolar systems can better explain observed behaviors. Synthesis mapping binary halides reveals multiple species peaks 300 nm, including a peak at 313 nm two 345 370 also appear as reaction intermediates during perovskites. Based on excitonic energies match within 50 meV, we give preliminary assignment these two-dimensional magic-sized clusters side lengths 2, 3, 4 unit cells. Kinetic conversion benzoyl are connected to product demonstrate (i.e., nucleation) is controlled by decomposition, whereas growth rate 2D 3D perovskites significantly slower.

Language: Английский

Citations

1
Luminescent Perovskite Quantum Dots: Progress in Fabrication, Modelling and Machine Learning Approaches for Advanced Photonic and Quantum Computing Applications DOI
K. Deepthi Jayan, K. Jayanth Babu

Journal of Luminescence, Journal Year: 2024, Volume and Issue: unknown, P. 120906 - 120906

Published: Sept. 1, 2024

Language: Английский

Citations

1
Automated Gold Nanorod Spectral Morphology Analysis Pipeline DOI Creative Commons
Samuel P. Gleason, Jakob C. Dahl, Mahmoud Elzouka

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(51), P. 34646 - 34655

Published: Dec. 13, 2024

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often time-consuming, iterative process. This due quantitative uncertainties in the required reaction conditions time, resources, expertise intensive characterization methods for determination nanomaterial shape. Absorption spectroscopy easiest method characterization. However, lack reliable extract nanoparticle shapes from absorption spectroscopy, it generally treated as more qualitative measure metal nanoparticles. work demonstrates gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which fast accurate structural information AuNR spectra. To demonstrate practical utility this model, we apply three distinct applications. First, model's an automated tool high-throughput by generating optical Second, use predictions generated model train machine learning predict resulting distributions under specified conditions. Third, spectra extracted literature where no are reported impute unreported on synthesis. approach can potentially be extended any other nanocrystal system dependent, numerical simulation possible. In addition, pipeline could integrated into apparatuses provide interpretable data simple measurements, help explore science nanoparticles rational manner, or facilitate closed-loop workflows.

Language: Английский

Citations

1
An Intermediate-Aided Perovskite Phase Purification for High-Performance Solar Cells DOI

Jinghao Ge,

Yiru Huang,

Xuexiao Chen

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

In recent years, perovskite solar cells (PSCs) have garnered considerable attention as a prime candidate for next-generation photovoltaic technology. Ensuring the structural stability of perovskites is crucial to operational reliability these devices. However, nonphotoactive yellow phase (δ-FAPbI

Language: Английский

Citations

1