Lilo Danielle Pozzo

Confined spaces at the nanoscale ─ pores, channels, and cavities that alter molecular affinities and restrict molecular motion, orientation and local distribution ─ are emerging as a powerful design principle for next-generation materials. By tailoring the structure and functionality of these confined spaces, it is possible to control reactivity, transport, and material properties in ways that are unattainable in bulk systems. This Perspective highlights nanoconfinement as a unifying principle for programming function and addressing major societal challenges in energy, water, health and sustainability. Drawing inspiration from nature’s remarkable precision in bio-molecular control, we showcase six representative areas in which confined spaces are transforming technology: chromatography, catalysis, nanofluidics, bioinspired pore design, confinement-programmed material states, and data-driven material development. Together, these examples illustrate the power of nanoconfinement as an enabling unifying concept. By integrating orthogonal functionalization strategies, advanced characterization, modeling, and digitalization, programmable nanoconfinement will become a versatile framework for engineering materials and processes that meet the demands of a sustainable and technologically advanced society.

The ADT workflow utilizes customized hardware and a liquid handling robot to automate and parallelize long-term membrane permeability testing.

Force in the milling jar extends the polymer coasters. Liquid-assisted grinding enables (top) the combination of normally incompatible parts that show self-assembly, and (bottom) the controlled growth of ultra-long tracks with thousands of segments.

Thin polymer films are widely used as functional and protective coatings. However, determining the composition and processing conditions that produce a desired function is a tedious process due to the large number of factors that must be considered and the manual nature of most synthesis and characterization methods. Self-driving labs (SDLs), or robotic systems that prepare and test material samples, are designed to overcome this bottleneck by enabling the efficient exploration of complex parameter spaces. In this paper, we report the development and testing of the polymer analysis and discovery array (PANDA)-film, a modular SDL for electrochemically synthesizing polymer films and then determining their water contact angle as a measure of surface energy. The system is designed to be highly modular and based upon a low-cost gantry platform to facilitate adoption. In addition to validating fluid handling and electrochemical tasks, we introduce two novel modular capabilities that enable PANDA-film to run sustained campaigns to study the wetting properties of films: (1) an electromagnetic capping/decapping system to mitigate fluid evaporation and (2) a top-down optical method to determine water contact angle based upon reflectance. These capabilities are validated by depositing and characterizing a poly(allyl methacrylate) film using electrodeposition of polymer networks. Comprehensive details for replicating the hardware and software of PANDA-film are included.

We investigate the influence of solvent quality on solution-state aggregation and solid-state morphology in a rigid indacenodithiophene-based donor–acceptor copolymer, PIDTC16-BT. Using UV–vis spectroscopy and temperature-dependent small-angle X-ray scattering (SAXS), we characterize aggregation behavior across three solvents of varying segmental compatibility. Anisole promotes rapid, cooperative aggregation with lamellar ordering, while toluene and tetrahydrofuran (THF) support more gradual or dispersed clustering. Grazing-incidence small-angle X-ray scattering (GISAXS) reveals that these solution-state differences partially translate into solid-state packing, with anisole-cast films exhibiting enhanced lamellar coherence and backbone alignment. Despite these structural variations, organic field-effect transistor (OFET) measurements show comparable hole mobilities across all solvents. These results suggest that backbone rigidity and kinetic trapping during casting suppress the functional impact of solvent-driven aggregation. Segment-specific solubility analysis using Hansen parameters provides a framework for understanding aggregation pathways in rigid D–A systems. Our findings highlight the morphological robustness of PIDTC16-BT and underscore the need for multiscale characterization when linking processing to performance in conjugated polymer devices.

DNA molecules, modified with cholesterol functional groups, are physically anchored to lipid-encapsulated silica nanoparticles to drive their colloidal assembly as the process is analyzed with scattering and simulations.

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

Integral aspects of green chemistry include utilizing minimal solvent and energy-efficient pathways to synthesize target materials. For polymer synthesis in particular, accessing sequences derived from immiscible feedstocks and masses in the ultra-high molecular weight regime often require specialized methods and extensive optimization. In this work, we report on the synthesis of diverse polyacrylates inspired by green chemistry principles using streamlined ball mill mechanochemistry technology. Mechanoredox reversible addition-fragmentation chain-transfer (MR-RAFT) polymerization methods to synthesize multiblock copolymers from immiscible monomers and overcome viscosity issues to access ultra-high molecular weight polymers. Both classes of materials further the discovery of novel macromolecular materials and are accessed herein using (nearly) solvent-free ball milling methodology.

This document describes research activities, products and outcomes of a DOE-funded program to help accelerate development of Organic Mixed Ionic Electronic Conductors (OMIECs) using neutron scattering and high-throughput experimentation. OMIECs are organic materials that conduct both ionic and electronic charge carriers. For these new OMIEC materials, self-assembling ion-conducting block copolymers (BCPs) are used as a structural template for electronic conducting polymers. This forms OMIECs with long-range structural order that can help facilitate long-range electronic transport. The conductive properties of OMIECs are closely associated with their structure, which is affected by solution conditions and polymer macromolecular designs. Thus high-throughput experimentation has been implemented to explore this large design space effectively. The BCP-CP OMIEC systems explored are composed of ion-conducting diblock or triblock copolymers containing polyethylene oxide (PEO), di(ethylene glycol) ethyl ether acrylate (DEGEEA) or poly(ethylene glycol) methyl ether acrylate) (PEGMEA) hydrophilic blocks. These blocks will be coupled with either polypropylene oxide (PPO) or polyheptafluorobutyl acrylate (PHFBA) hydrophobic blocks to drive assembly. The electronic conducting component consists of several different types of conjugated polymers. Small angle scattering of neutrons and X-rays (SANS/SAXS) as well as electrochemical analysis have been coupled with modern algorithms for autonomous research using artificial intelligence (AI).

An open-hardware automated workflow for mesoporous colloidal silica synthesis is developed and applied to study a compositional parameter space.