Replica molding often induces tribocharge on elastomers. To date, this phenomenon has been studied only on untextured elastomer surfaces even though replica molding is an effective method for their nanotexturing. Here we show that on elastomer surfaces nanotextured through replica molding the induced tribocharge also becomes patterned at nanoscale in close correlation with the nanotexture. By applying Kelvin probe microscopy, electrohydrodynamic lithography, and electrostatic analysis to our model nanostructure, poly(dimethylsiloxane) nanocup arrays replicated from a polycarbonate nanocone array, we reveal that the induced tribocharge is highly localized within the nanocup, especially around its rim. Through finite element analysis, we also find that the rim sustains the strongest friction during the demolding process. From these findings, we identify the demolding-induced friction as the main factor governing the tribocharge’s nanoscale distribution pattern. By incorporating the resulting annular tribocharge into electrohydrodynamic lithography, we also accomplish facile realization of nanovolcanos with 10 nm-scale craters.

We use a variety of experimental techniques to characterize Cu clusters on bulk MoS₂ formed via physical vapor deposition of Cu in ultrahigh vacuum, at temperatures ranging from 300 K to 900 K. We find that large facetted clusters grow at elevated temperatures, using high Cu exposures. The cluster size distribution is bimodal, and under some conditions, large clusters are surrounded by a denuded zone. We propose that defect-mediated nucleation, and coarsening during deposition, are both operative in this system. At 780 K, a surprising type of facetted cluster emerges, and at 900 K this type predominates: pyramidal clusters with a triangular base, exposing (311) planes as side facets. This is a growth shape, rather than an equilibrium shape.

Attempts to use machine learning to discover hidden physical rules are in their infancy, and such attempts confront more challenges when experiments involve multifaceted measurements over three-dimensional objects. Here we propose a framework that can infuse scientists’ basic knowledge into a glass-box rule learner to extract hidden physical rules behind complex physics phenomena. A “convolved information index” is proposed to handle physical measurements over three-dimensional nano-scale specimens, and the multi-layered convolutions are “externalized” over multiple depths at the information level, not in the opaque networks. A transparent, flexible link function is proposed as a mathematical expression generator, thereby pursuing “glass-box” prediction. Consistent evolution is realized by integrating a Bayesian update and evolutionary algorithms. The framework is applied to nano-scale contact electrification phenomena, and results show promising performances in unraveling transparent expressions of a hidden physical rule. The proposed approach will catalyze a synergistic machine learning-physics partnership.

High aspect-ratio elastomeric micropillars play important roles as the platform for microscale sensing and actuation. Many soft-lithographic techniques have been developed for their facile realization but most of the techniques are limited to build the micropillars only on totally flat, widely accessible substrate areas with the micropillar’s structural characteristics completely predetermined, leaving little room for in situ control. Here we demonstrate a new technique which overcomes these limitations by directly drawing micropillars from pipette-dispensed PDMS microdroplets using vacuum-chucked microspheres. The combined utilization of PDMS microdroplets and microspheres not only enables the realization of microsphere-tipped PDMS micropillars on non-flat, highly space-constrained substrate areas at in situ controllable heights but also allows arraying of micropillars with dissimilar heights at a close proximity. To validate the new technique’s utility and versatility, we realize PDMS micropillars on various unconventional substrate areas in various configurations. We also convert one of them, the optical fiber/micropillar hybrid, into a soft optical contact sensor. Both the fabrication technique and the resulting sensing scheme will be useful for future biomedical microsystems.