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Formation kinetics of metal nanoparticles are generally describedviamass transport and thermodynamics‐based models, such as diffusion‐limited growth and classical nucleation theory (CNT). However, metal monomers are commonly assumed as precursors, leaving the identity of molecular intermediates and their contribution to nanoparticle formation unclear. Herein, liquid phase transmission electron microscopy (LPTEM) and reaction kinetic modeling are utilized to establish the nucleation and growth mechanisms and discover molecular intermediates during silver nanoparticle formation. Quantitative LPTEM measurements show that their nucleation rate decreases while growth rate is nearly invariant with electron dose rate. Reaction kinetic simulations show that Ag₄and Ag⁻follow a statistically similar dose rate dependence as the experimentally determined growth rate. We show that experimental growth rates are consistent with diffusion‐limited growthviathe attachment of these species to nanoparticles. The dose rate dependence of nucleation rate is inconsistent with CNT. A reaction‐limited nucleation mechanism is proposed and it is demonstrated that experimental nucleation kinetics are consistent with Ag₄²⁺aggregation rates at millisecond time scales. Reaction throughput analysis of the kinetic simulations uncovered formation and decay pathways mediating intermediate concentrations. We demonstrate the power of quantitative LPTEM combined with kinetic modeling for establishing nanoparticle formation mechanisms and principal intermediates.

 

In this paper, we aim to address a relevant estimation problem that aviation professionals encounter in their daily operations. Specifically, aircraft load planners require information on the expected number of checked bags for a flight several hours prior to its scheduled departure to properly palletize and load the aircraft. However, the checked baggage prediction problem has not been sufficiently studied in the literature, particularly at the flight level. Existing prediction approaches have not properly accounted for the different impacts of overestimating and underestimating checked baggage volumes on airline operations. Therefore, we propose a custom loss function, in the form of a piecewise quadratic function, which aligns with airline operations practice and utilizes machine learning algorithms to optimize checked baggage predictions incorporating the new loss function. We consider multiple linear regression, LightGBM, and XGBoost, as supervised learning algorithms. We apply our proposed methods to baggage data from a major airline and additional data from various U.S. government agencies. We compare the performance of the three customized supervised learning algorithms. We find that the two gradient boosting methods (i.e., LightGBM and XGBoost) yield higher accuracy than the multiple linear regression; XGBoost outperforms LightGBM while LightGBM requires much less training time than XGBoost. We also investigate the performance of XGBoost on samples from different categories and provide insights for selecting an appropriate prediction algorithm to improve baggage prediction practices. Our modeling framework can be adapted to address other prediction challenges in aviation, such as predicting the number of standby passengers or no-shows.

 

Observations of nanoparticle superlattice formation over minutes during colloidal nanoparticle synthesis elude description by conventional understanding of self-assembly, which theorizes superlattices require extended formation times to allow for diffusively driven annealing of packing defects. It remains unclear how nanoparticle position annealing occurs on such short time scales despite the rapid superlattice growth kinetics. Here we utilize liquid phase transmission electron microscopy to directly image the self-assembly of platinum nanoparticles into close packed supraparticles over tens of seconds during nanoparticle synthesis. Electron-beam induced reduction of an aqueous platinum precursor formed monodisperse 2–3 nm platinum nanoparticles that simultaneously self-assembled over tens of seconds into 3D supraparticles, some of which showed crystalline ordered domains. Experimentally varying the interparticle interactions ( e.g. , electrostatic, steric interactions) by changing precursor chemistry revealed that supraparticle formation was driven by weak attractive van der Waals forces balanced by short ranged repulsive steric interactions. Growth kinetic measurements and an interparticle interaction model demonstrated that nanoparticle surface diffusion rates on the supraparticles were orders of magnitude faster than nanoparticle attachment, enabling nanoparticles to find high coordination binding sites unimpeded by incoming particles. These results reconcile rapid self-assembly of supraparticles with the conventional self-assembly paradigm in which nanocrystal position annealing by surface diffusion occurs on a significantly shorter time scale than nanocrystal attachment.