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Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A k−1k machine uses a single partition (or bag) of k – 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible k−1k machines available for a given partition. We find that with increasing k in the k-bag or k−1k machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and 34 fraction machines suggest that the 34 fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines. 

Abstract Seed-mediated synthesis strategies, in which small gold nanoparticle precursors are added to a growth solution to initiate heterogeneous nucleation, are among the most prevalent, simple, and productive methodologies for generating well-defined colloidal anisotropic nanostructures. However, the size, structure, and chemical properties of the seeds remain poorly understood, which partially explains the lack of mechanistic understanding of many particle growth reactions. Here, we identify the majority component in the seed solution as an atomically precise gold nanocluster, consisting of a 32-atom Au core with 8 halide ligands and 12 neutral ligands constituting a bound ion pair between a halide and the cationic surfactant: Au 32 X 8 [AQA + •X - ] 12 (X = Cl, Br; AQA = alkyl quaternary ammonium). Ligand exchange is dynamic and versatile, occurring on the order of minutes and allowing for the formation of 48 distinct Au 32 clusters with AQAX (alkyl quaternary ammonium halide) ligands. Anisotropic nanoparticle syntheses seeded with solutions enriched in Au 32 X 8 [AQA + •X - ] 12 show narrower size distributions and fewer impurity particle shapes, indicating the importance of this cluster as a precursor to the growth of well-defined nanostructures. 

Supported lipid bilayers are often used as model systems for studying interactions of biological membranes with protein or nanoparticles. A supported lipid bilayer is a phospholipid bilayer built on a solid substrate. The latter is typically made of silica or a metal oxide due to the ease of its formation and range of compatible measurement techniques. Recently, a solvent-assisted method involving supported lipid bilayer formation has allowed the extension of compatible substrate materials to include noble metals such as gold. Here, we examine the influence of substrate composition (SiO2 vs Au) on the interactions between anionic ligand-coated Au nanoparticles or cytochrome c and zwitterionic supported lipid bilayers using quartz crystal microbalance with dissipation monitoring. We find that anionic nanoparticles and cytochrome c have higher adsorption to bilayers formed on Au relative to those on SiO2 substrates. We examine the substrate-dependence of nanoparticle adsorption with DLVO theory and all-atom simulations, and find that the stronger attractive van der Waals and weaker repulsive electrostatic forces between anionic nanoparticles and Au substrates vs anionic nanoparticles and SiO2 substrates could be responsible for the change in adsorption observed. Our results also indicate that the underlying substrate material influences the degree to which nanoscale analytes interact with supported lipid bilayers; therefore, interpretation of the supported lipid bilayer model system should be conducted with understanding of support properties. 

Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network k-ensemble model for wavelength prediction through regression performed significantly better than either a standard artificial neural network or a single-stage artificial neural network k-ensemble regression model. The accuracy of the model predictions was evaluated against CD emission wavelengths measured from experiments, and the minimum mean average error is 25.8 nm. Overall, the models developed in this work can effectively predict the photoluminescence emission of CDs and help design CDs with targeted optical properties. 

We investigate the excited electron dynamics in [Au 25 (SR) 18 ] −1 (R = CH 3 , C 2 H 5 , C 3 H 7 , MPA, PET) [MPA = mercaptopropanoic acid, PET = phenylethylthiol] nanoparticles to understand how different ligands affect the excited state dynamics in this system. The population dynamics of the core and higher excited states lying in the energy range 0.00–2.20 eV are studied using a surface hopping method with decoherence correction in a real-time DFT approach. All of the ligated clusters follow a similar trend in decay for the core states (S 1 –S 6 ). The observed time constants are on the picosecond time scale (2–19 ps), which agrees with the experimental time scale, and this study confirms that the time constants observed experimentally could originate from core-to-core transitions and not from core-to-semiring transitions. In the presence of higher excited states, R = H, CH 3 , C 2 H 5 , C 3 H 7 , and PET demonstrate similar relaxations trends whereas R = MPA shows slightly different relaxation of the core states due to a smaller gap between the LUMO+1 and LUMO+2 gap in its electronic structure. The S 1 (HOMO → LUMO) state gives the slowest decay in all ligated clusters, while S 7 has a relatively long decay. Furthermore, separate electron and hole relaxations were performed on the [Au 25 (SCH 3 ) 18 ] −1 nanocluster to understand how independent electron and hole relaxations contribute to the overall relaxation dynamics.