Supramolecular Semiconductivity through Emerging Ionic Gates in Ion–Nanoparticle Superlattices

Abstract

The self-assembly of nanoparticles driven by small molecules or ions may produce colloidal superlattices with features and properties reminiscent of those of metals or semiconductors. However, to what extent the properties of such supramolecular crystals actually resemble those of atomic materials often remains unclear. Here, we present coarse-grained molecular simulations explicitly demonstrating how a behavior evocative of that of semiconductors may emerge in a colloidal superlattice. As a case study, we focus on gold nanoparticles bearing positively charged groups that self-assemble into FCC crystals via mediation by citrate counterions. In silico ohmic experiments show how the dynamically diverse behavior of the ions in different superlattice domains allows the opening of conductive ionic gates above certain levels of applied electric fields. The observed binary conductive/nonconductive behavior is reminiscent of that of conventional semiconductors, while, at a supramolecular level, crossing the “band gap” requires a sufficient electrostatic stimulus to break the intermolecular interactions and make ions diffuse throughout the superlattice’s cavities.

Probing the electrostatic aggregation of nanoparticles with oppositely charged molecular ions

“…This analysis is consistent with previous studies. [ 2c,13 ] It is worth noting that the classification criterion for the anion classes adopted in this study is somewhat different from the previous studies. [ 2c,13 ] Still, we notice remarkably similar results with high probabilities within clusters (i.e., numbers on the diagonal) and low transaction probability.…”

“…[ 2c,13 ] It is worth noting that the classification criterion for the anion classes adopted in this study is somewhat different from the previous studies. [ 2c,13 ] Still, we notice remarkably similar results with high probabilities within clusters (i.e., numbers on the diagonal) and low transaction probability. This suggests that the two clustering methods give similar results and indicate that anions tend to stay in their clusters due to the strong electrostatic attractions with the Au‐TMA nanoparticle.…”

“…For this analysis, we considered two variables (i.e., DIST and Rmin ) similar to previous studies. [ 2c,13 ] Herein, the two gold cores are denoted as I and II , respectively. The distribution of each anion on the Au‐TMA nanoparticles was calculated by its distance to the COMs of the gold core I and II (denoted ad Dist(I) and Dist(II) , respectively).…”

Photocleavable Anionic Glues for Light-Responsive Nanoparticle Aggregates

“…This result can be attributed to the aging of the Au•TMA/NBTS aggregates. We note that NBTS is a highly flexible molecule; over time, it can adopt a conformation optimal for maximizing the electrostatic interactions with NPs' charged TMA headgroups (in fact, we have previously postulated 63,72 that these "ionic glues" have a dynamic character in that they constantly bind to and unbind from the NPs). This hypothesis is further supported by our experiments with the significantly more rigid "inorganic PAG", as discussed in the next section.…”

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

“…This also holds true at the molecular scale, where phenomena such as nucleation, defect propagation, and phase transitions are intricately linked to these fluctuations. The integration of advanced molecular descriptors with machine learning (ML) has been playing a key role in analyzing molecular trajectories, contributing to a better understanding of diverse nanoscale systems, ranging from atomistic to supramolecular levels [1][2][3][4][5][6][7][8][9][10][11]. Standard human-based descriptors, tailored for building detailed analyses and investigating specific systems like, i.e.…”