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The limited intragap distance tunability of clustering nanoparticles with different surface chemistries resulted in unwanted interference between embedded nanoparticles and restricted functionality. This leads to an urgent need for a new strategy that enables the assembly of building block nanoparticles obtained from solvents with different polarities in defined compositions, distances, and orientations. Herein, we used organosilicasome (OSS), an organic–inorganic hybrid compound covered with a surfactant, to cluster diverse nanoparticles with high configurational tunability regardless of their surface chemistry. OSS is capable of gluing building block nanoparticles and inducing their co-encapsulation. The dissolvable characteristics of OSS facilitate controlled dissolution of nanoclusters, allowing the adjustment of the gap distance between building block nanoparticles and resulting in closely packed and loosely packed nanoclusters. Owing to OSS’s silica-based chemical nature, the synthesized nanoclusters can be silicified through simple addition of a silica precursor to have a robust mesoporous silica body. In consequence, OSS enables the synthesis of multifunctional nanoclusters, especially with enhanced fluorescence, by controlling the distance in long range between gold nanoparticles and quantum dots. These tunable and multifunctional nanoclusters, synthesized from OSS, should provide a new platform ranging from the exploration of interactions at the nanoscale to the advent of unconventional multifunctional nanomaterials.

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.

Mesoporous silica nanoparticles (MSNPs) are well known for their adhesive properties with hydrogels and living tissues. However, achieving direct contact between the silica nanoparticle surface and the adherend necessitates the removal of capping agents, which can lead to severe aggregation when exposed to wet surfaces. This aggregation is ineffective for simultaneously bridging the two adherends, resulting in a reduced adhesive strength. In this study, we designed and synthesized mesoporous silica nanochains (MSNCs) to enhance the interactions with hydrogels by promoting the formation of coarser structures with increased nanopore exposure. Chain-like one-dimensional assemblies in the MSNCs were generated by depleting the capping ligand, cetyltrimethylammonium bromide, from the surface of the MSNPs. To quantify the porous areas of the MSNCs, we analyzed scanning electron microscopy (SEM) images using an in-house SEM image analysis algorithm. Additionally, we conducted a comparative assessment of the adhesion energies of MSNCs and MSNPs on a poly(dimethylacrylamide) hydrogel using a universal testing machine. The MSNCs exhibited a maximum adhesion energy of 13.7 ± 0.7 J/m2at 3 wt %, surpassing that of MSNPs (10.9 ± 0.3 J/m2) at 2 wt %. Moreover, the unique stacking structure of the MSNCs enabled them to maintain an adhesion energy of 13.4 ± 1.0 J/m2at a high concentration of 9 wt %, whereas the adhesion energy of MSNPs decreased to 8.2 ± 0.4 J/m2. This underscores their potential as superior hydrogel adhesives in challenging wet tissue-like environments.

We demonstrate a strategy for the synthesis of discrete and uniform gold nanorod (GNR)@mesoporous silica (mSiO2) core–shell nanoparticles (NPs) with finely tuned shell thickness by significantly suppressing the formation of undesired core-free NPs. We could control the thickness of the mSiO2shell with high precision, close to 1 nm, and hence the rotational diffusion mode of NPs by simply controlling the silica precursor injection times. The growth of the mSiO2shell on the GNR seeds and the formation of core-free mSiO2NPs could be explained by two models based on the modified LaMer’s theory. Between these two, we found the most suitable one for the synthesis of GNR@mSiO2NPs with a precisely controlled shell thickness and negligible core-free NPs as the synthesis mostly occurs via heterogeneous nucleation on GNR seeds. As our results are very simple and highly reproducible, we expect this work to provide profound insights into the synthesis of a variety of heterogeneous nanostructures.