No One Size Fits All: Semiconductor Nanocrystal Sizing Curves

Kuno, Masaru; Gushchina, Irina; Toso, Stefano; Trepalin, Vadim

doi:10.1021/acs.jpcc.2c04734

Cited by 8 publications

(7 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequent X-ray diffraction (XRD) and selected area electron diffraction (SAED) patterns (Figure S3) match those expected for CsPbBr 3 . 32 The acquired linear absorption and band edge emission spectra further agree with previously established CsPbBr 3 NC sizing curves 33 and with reported, size-dependent Stokes shifts. 34,35 TEM-based energy dispersive X-ray spectroscopy (TEM-EDXS) reveals that medium-QY NCs possess a lead-and bromine-rich stoichiometry of Cs:Pb:Br = 1.0:1.3 ± 0.2:3.8 ± 0.2.…”

Section: T H Imentioning

confidence: 99%

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr₃ Nanocrystals

et al. 2023

Self Cite

View full text Add to dashboard Cite

Key features of syntheses, involving the quaternary ammonium passivation of CsPbBr3 nanocrystals (NCs), include stable, reproducible, and large (often near-unity) emission quantum yields (QYs). The archetypical example involves didodecyl dimethyl ammonium (DDDMA+)-passivated CsPbBr3 NCs where robust QYs stem from interactions between DDDMA+ and NC surfaces. Despite widespread adoption of this synthesis, specific ligand–NC surface interactions responsible for large DDDMA+-passivated NC QYs have not been fully established. Multidimensional nuclear magnetic resonance experiments now reveal a new DDDMA+-NC surface interaction, beyond established “tightly bound” DDDMA+ interactions, which strongly affects observed emission QYs. Depending upon the existence of this new DDDMA+ coordination, NC QYs vary broadly between 60 and 85%. More importantly, these measurements reveal surface passivation through unexpected didodecyl ammonium (DDA+) that works in concert with DDDMA+ to produce near-unity (i.e., >90%) QYs.

show abstract

Section: T H Imentioning

confidence: 99%

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr₃ Nanocrystals

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, calibration curves for size and concentration are available for wurtzite (WZ) CdSe, 168,[196][197][198] WZ CdS, 198 zinc blende (ZB) CdSe and CdS, 199 tetragonal CuInS 2 , 200 CsPbX 3 , 201,202 CdTe, 198,203,204 PbSe, 205,206 PbTe, PbS, 207,208 InP, InAs, 209 and ZnSe. 210…”

Section: Preparation Of Nc Samples For Reliable Measurementsmentioning

confidence: 99%

Thermodynamics of nanocrystal–ligand binding through isothermal titration calorimetry

et al. 2022

View full text Add to dashboard Cite

show abstract

“…[1][2][3][4] In this regard, there is a general consensus on the use of the so-called "sizing curves", which plays a crucial role in optimizing synthetic protocols and investigating size-dependent properties. 5 Typically, these sizing curves are empirical polynomials [6][7][8][9] or inverse size-dependent terms added to the bulk bandgap, 5,[10][11][12][13] mostly describing the trend of photophysical properties with size. They establish phenomenological relations between the average sizes of QDs, usually determined by transmission electron microscopy (TEM), and their absorption band edge energy, whose values are determined by the quantum confinement effect, which is particularly relevant for the size regimes investigated in syntheses and applications.…”

Section: Introductionmentioning

confidence: 99%

“…They establish phenomenological relations between the average sizes of QDs, usually determined by transmission electron microscopy (TEM), and their absorption band edge energy, whose values are determined by the quantum confinement effect, which is particularly relevant for the size regimes investigated in syntheses and applications. 1 However, TEM has important critical issues, mainly related to inadequate statistics 5 and underestimation of ultrasmall sizes, due to their low contrast with respect to the background signal. 14 Furthermore, TEM experiments are performed under vacuum and on dry QDs, whereas the corresponding spectroscopic data are collected on colloidal samples; this effect alone can potentially modify the sample by undesired ligand desorption and its size estimate if self-assembly phenomena trigger size-selection mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…18 Despite their broad applicability to many QD categories, recent articles have drawn attention to significant discrepancies among empirical sizing expressions derived from different datasets for the same class of materials. 5,12,14 These differences have been attributed to either inaccurate size determination from TEM (related to the poor electron scattering contrast between small particles and the underlying carbon support), or to unresolved band edge transitions (causing large errors in the first excitonic peak determination), and ultimately resulting in incomparable size estimation among different laboratories. 14 A recent attempt to overcome the need for empirical calibration curves and to provide a physically meaningful tool for QDs sizing has been reported by Hens and coworkers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data

Allara,

Bertolotti,

Guagliardi

2023

Preprint

View full text Add to dashboard Cite

Disclosing the full potential of functional nanomaterials requires the optimization of synthethic protocols and an effective size screening tool, aiming at efficiently triggering their size-dependent properties. Here we demonstrate the successful combination of a wide-angle X-ray total scattering approach with a deep learning classifier for directly sizing quantum dots in both colloidal and dry states. This work offers a compelling alternative to the lengthy process of deriving sizing curves from transmission electron microscopy coupled with spectroscopic measurements, especially in the ultra-small size regime, where traditional empirical functions exhibit larger discrepancies. The core of our algorithm is an all-convolutional neural network trained on Debye scattering equation X-ray simulations, incorporating atomistic models to capture structural and morphological features, and augmented with physics-informed perturbations to account for different predictable experimental conditions. The model performances are evaluated using both wide-angle X-ray total scattering simulations and experimental datasets collected on lead sulfide quantum dots, resulting in size classification accuracies surpassing 97%. With the developed deep learning size classifier, we overcome the need for calibration curves for quantum dots sizing and thanks to the unified modeling approach at the basis of the total scattering method implemented, we include simultaneously structural and microstructural aspects in the classification process, This algorithm can be complemented by incorporating input information from other experimental observations (e.g. small angle X-ray scattering data) and can be easily extended to other classes of nanocrystals, providing the nanoscience community with a powerful and broad tool to accelerate the development of novel functional (nano)materials.

show abstract

No One Size Fits All: Semiconductor Nanocrystal Sizing Curves

Cited by 8 publications

References 60 publications

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr₃ Nanocrystals

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr₃ Nanocrystals

Thermodynamics of nanocrystal–ligand binding through isothermal titration calorimetry

A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data

Contact Info

Product

Resources

About

No One Size Fits All: Semiconductor Nanocrystal Sizing Curves

Cited by 8 publications

References 60 publications

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr3 Nanocrystals

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr3 Nanocrystals

Thermodynamics of nanocrystal–ligand binding through isothermal titration calorimetry

A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data

Contact Info

Product

Resources

About

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr₃ Nanocrystals

Mixed Ligand Passivation as the Origin of Near-Unity Emission Quantum Yields in CsPbBr₃ Nanocrystals