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Surface-capping agents—for example, amphiphilic surfactant molecules, water-soluble polymers, or polyelectrolytes—play a critical role during polymerization reactions for both the formation and stability of colloidal polymer particles.
Light trapping nanostructures are often necessary to improve carrier collection yields in semiconductor photoelectrodes with intrinsically poor electron transport. Photonic crystal templates can generate tailorable light trapping via periodic and precise nanostructure, though this is not a scalable strategy for photoelectrochemical (PEC) applications. It is therefore critical to identify alternative mechanisms for light trapping that tolerate disorder. Light trapping in disordered media is generated by the diffusive transport caused by multiple scattering. In some cases, multiple scattering can generate resonances that resemble those observed in photonic crystals. While resonant multiple scattering is a disorder tolerant light trapping mechanism, it is unclear if the effect is sufficiently adaptable, or even useful, for PEC applications. Here, we describe a photonic omission glass, a nanostructure that can controllably induce resonances in multiple scattering transport. We characterized the emergence of these resonances after coating a disordered SiO 2 colloidal structure with a layer of TiO 2 , which functions both as dielectric contrast and as a light absorbing semiconductor. We show in finite element simulations and spectroscopic characterization that the resonant multiple scattering effect improves light trapping near the interface between the structure and the bulk electrolyte. This effect, coupled with the increased electrochemically active surface area, results in a hierarchically structured TiO 2 photoanode with orders of magnitude higher photocurrents compared to an equivalent planar photoanode for PEC reactions such as alkaline water oxidation. We show that controlling this resonant multiple scattering effect can be advantageous for improving PEC energy conversion in disordered photoelectrodes.
Transport mechanisms at the micro- and nano-scale play an essential role in regulating intracellular organization. Recent work indicates that directed motion of constituents inside cells can emerge through diffusiophoretic transport, in which colloidal particles move under the influence of chemical gradients. Here, we examine how blockers—passive or actively consuming—reshape those gradients and thereby influence the motion of diffusiophoretic particles. By combining analytical solutions with finite element simulations, we first show that a single blocker can distort a background gradient enough to create or eliminate stagnation points, significantly modifying particle transport. We then introduce a second, explicitly sized blocker at one of these stagnation points and measure how its finite radius alters the diffusiophoretic velocity field for a test particle. Even moderate changes in the second blockers size can cause noticeable shifts in the substrate distribution, highlighting the importance of accounting for explicit particle radii under crowded or consumption-driven conditions. Our findings underscore that subtle geometric variations—such as the radii and positions of two or more blockers—can profoundly affect diffusiophoretic motion, providing a more complete picture of how blocking and crowding phenomena shape intracellular transport.
Low-temperature, scalable routes to transition metal nitride (TMN) nanoparticles are desirable for a wide range of applications, yet their synthesis typically requires high temperatures (>350 °C) and reactive gas environments (e.g., NH 3 or H 2 /N 2 ). Here, we report a colloidal synthesis of mono- and bimetallic TMN nanoparticles using preformed metal carbonyl clusters as precursors and urea or diethylenetriamine (DETA) as nitrogen sources. This strategy enables access to size-controlled, phase-pure ε-Fe 3 N x and Fe y Ni 3−y N nanoparticles at temperatures below 300 °C, without the need for flowing reactive gas atmospheres. By systematically varying nitrogen precursor, reaction temperature, and cluster identity, we achieve tunable nitrogen stoichiometry (x) and phase selectivity between N-rich and N-poor TMNs. Structural and magnetic characterization confirms clean decomposition of the precursors and phase formation consistent with controlled nitridation at the nanoscale. Preliminary electrochemical measurements in alkaline media demonstrate that these materials exhibit oxygen evolution reaction (OER) overpotentials comparable to RuO 2 , highlighting their viability for future electrocatalytic applications.
Alkali-metal-based materials are promising building blocks for energy conversion and storage technologies. Here, we use a molecular reactivity-based solution-phase approach to selectively synthesize multiple phases and specific polymorphs of lithium- and sodium-containing triel chalcogenide nanocrystals, LiTrCh 2 , NaTrCh 2 , and NaTr 3 Ch 5 , where Tr = Ga and In and Ch = S, Se, and Te. Analogous to the case of binary II–VI and III–V tetrahedral semiconductors, where the two commonly isolated zinc blende and wurtzite polymorphs are separated by only 1–50 meV f.u. −1 , we find that LiTrCh 2 nanocrystals easily adopt tetragonal (chalcopyrite) and orthorhombic polymorphs separated by only 2.7–6.2 meV f.u. −1 Because of this small energy difference, soft colloidal synthesis succeeds in accessing either one of these polymorphs, depending on the specific dichalcogenide precursor used. Highly reactive diethyl diselenide favors the thermodynamically more stable tetragonal I$\bar{4}2$d phase, whereas mildly reactive diphenyl diselenide favors the kinetic, metastable orthorhombic Pna2 1 phase. Density functional theory calculations confirm the relative energies among multiple LiTrCh 2 polymorphs and also model the observed powder X-ray diffraction pattern of a new C2 NaIn 3 Te 5 phase. 7 Li, 69 Ga, and 77 Se solid-state NMR spectra are consistent with phase-pure ternary LiGaSe 2 nanocrystals. A majority of the nanocrystal compositions are visible-light emitters. This work opens the door to new Li/Na-based ternary triel chalcogenide nanostructures for energy storage and conversion applications.
The 1.76 eV band gap of antimony(iii) sulphide (Sb 2 S 3 ) makes this semiconductor material a promising light absorber for photoelectrochemical water splitting, but scalable fabrication approaches to efficient devices are still lacking. Here we show that compact Sb 2 S 3 films on FTO can be obtained by electrochemical growth from aqueous colloidal sulphur and antimony trichloride solutions, followed by mild annealing. These films can be converted into hydrogen evolution photocathodes after coating with In 2 S 3 passivation layers and the addition of Pt proton reduction co-catalysts. For the first time, vibrating Kelvin probe surface photovoltage (VKP-SPV) spectroscopy is used to observe the carrier dynamics in such photoelectrodes. While the bare Sb 2 S 3 films suffer from high surface recombination rates and poor electron extraction, the In 2 S 3 overlayer is found to raise the photovoltage and cathodic photocurrent density, due to passivation of surface defects and formation of a p–n heterojunction. In thick In 2 S 3 films, these benefits are offset by shading and slow electron transfer. Also, we find that O 2 strongly affects the band bending in the Sb 2 S 3 –air and In 2 S 3 –air junctions and their photovoltage. The optimised devices evolve H 2 at 77.5% Faradaic efficiency and with 0.084% applied bias photon-to-current efficiency (ABPE) at 0.12 V vs. RHE. The low ABPE value is attributed to Sb 2 S 3 sub-bandgap defects visible in SPV spectra, the random orientation of Sb 2 S 3 crystallites in the films, which inhibits charge transport, the absence of crystal facets of Sb 2 S 3 , and a detrimental Schottky junction at the FTO|Sb 2 S 3 interface.
We report a drastic difference in stacking behavior of oleic acid-stabilized 4-monolayer (4 ML) CdSe nanoplatelets (NPLs) in toluene and methylcyclohexane (MCH), two nonpolar solvents that differ in the conformational flexibility of their molecules. Using liquid cell transmission electron microscopy (TEM) and small angle scattering (SAXS) techniques, we show that NPLs form microns-long ribbons consisting of 4 ML CdSe NPLs in toluene, the solvent widely used to form stable colloidal solutions of a broad range of quasi-spherical nanoparticles. In contrast, 4 ML CdSe NPLs are well dispersed in MCH. The difference in stacking behavior of NPLs in toluene and MCH suggests that the conformational flexibility of the solvent molecules, such as the ability to adopt multiple chair conformations, modulates nanoplatelet interactions. Molecular dynamics (MD) simulations reveal that solvent molecules subtly alter the structure of the organic ligand shell. These solvent-dependent changes propagate to the inorganic core, modulating the degree of CdSe nanoplatelet (NPL) twisting and, consequently, the properties of the nanoparticles. We show that toluene better solvates oleate ligands while MCH induces a bimodal oleate span distribution, which can lead to increased solubility of CdSe NPLs. In addition, the solvent can also influence the inorganic core, which, in turn, can modify the nanoparticle properties. We demonstrate that destabilization of toluene solution containing ribbons of 4 ML CdSe NPLs without CdS shells results in the formation of NPL assemblies with amplified spontaneous emission (ASE) with a low threshold of 14 µJ cm−2 that is comparable with that of CdSe/CdS core/shell NPLs. Our results emphasize that the solvent plays a major role in mediating interactions between NPLs and hence their processability for fabrication of functional structures.
Reported here are reactions of aqueous colloidal IrO x nanoparticles (NPs) with proton-coupled electron transfer (PCET) and oxygen-atom transfer (OAT) organic reagents, determining the reaction stoichiometries and thermochemistry. IrO x NPs have attracted much attention for their high electrocatalytic activity, but understanding of their fundamental reaction chemistry is limited. This IrO x NP model system is simple, with UV-vis titrations demonstrating reversible interconversion between predominantly Ir IV and predominantly Ir III NPs. This simplicity allows studies that reveal their complex non-idealities. The NP redox chemistry has a “super-Nernstian” stoichiometry of ∼1.3H + per 1e − transferred during both PCET and OAT reactions, as measured with electrochemistry and chemical methods. Spectroelectrochemistry revealed a broad distribution of surface IrO x –H bond dissociation free energies (BDFEs), becoming weaker as more H is added. Such variation in binding strengths—a non-ideal binding isotherm—is common for surface adsorbates. For IrO x , the variation of BDFE(IrO–H)s is fit well to a Frumkin isotherm with a width of 6.5 kcal mol −1 . For OAT from the reactive oxygen atoms of IrO x NPs, bracketing experiments gave 93 ± 24 kcal mol −1 for the average BDFE(O x Ir–O), with a predicted spread much larger than that for the BDFE(IrO–H). Taken together, the results show the importance of non-ideal stoichiometry and thermochemistry for IrO x NPs, and they open a path to more complete models to understand catalytic redox reactions at such surfaces.
Multi-twinned structures have been observed in technologically important crystal systems, for example diamond cubic and face-centered cubic (FCC) lattices, that include materials such as diamond, silicon, a wide range of noble metals, and their nanoscale counterparts. Beyond atomic building blocks, the special arrangements also occur in the self-assembly of nanoparticles (NP) and μm-sized colloidal particles and occupy parts of their phase diagrams. Spanning a wide range of length scales, the universality of the structures arises when the systems attempt to achieve multitwinned structures by overcoming geometric misfits during minimizing surface energies with entirely {111} or close-packing facets. While it is fundamental to understand how strain is sustained upon twinned structures and symmetry breaking, the knowledge will be paramount in practical aspects such as guiding and controlling the thin film growth, anisotropic NP growth, and self-assembly of NPs. Au decahedral (Dh) NP, as the most prevalent multi-twinned model system, fits five tetrahedral motifs into a circle by sharing an axis resulting in a geometric misfit angle of 7.35°, or a disclination with power of -7.35°. Postulating how Au FCC lattice adopts the misfit, theoretical models have been developed to address the underlying lattice symmetry and inhomogeneous strain distribution separately. Yet, experimental reports regarding the former have been limited due to the relatively large X-ray beam sizes that do not fit the sizes of NPs. On the other hand, though the latter has been widely adapted in the thermodynamics of small (<10 nm) multi-twinned nanoparticles, previous literature has shown that, at edge length of 17 nm, the theory’s is invalidated by shear strain that is observed in a defect-free Au Dh NP by high-resolution transmission electron microscopy (HR-TEM) imaging. Though the advancement of aberrationcorrected scanning transmission electron microscopy (AC-STEM) imaging and ab initio calculation techniques brings new opportunities, along with challenges in complicated image analysis and limitation in particle size (usually below 10 nm), the gap between nanoscale and mesoscale has never been extended to gain insight from atomic system with straightforward interaction potentials.
Hydrodynamics provides a continuum-level description of fluid motion, but its applicability at the nanoscale becomes uncertain due to the emerging importance of molecular-level effects such as spatial heterogeneity. Hydrodynamic boundary conditions that incorporate molecular details allow us to partition the system into a near-wall region and a bulk fluid region. We identify a hydrodynamic wall located inside the fluid that determines where slip begins. By extending the hydrodynamic wall with the slip length, the position of the extrapolated wall is established. This offers a unified description of both slip and stagnant flow behaviors, with wall hydrophobicity characterized by the relative location of the extrapolated wall with respect to the physical wall. Employing this concept in analyses of equilibrium molecular dynamics (MD) and non-equilibrium MD simulations of Couette and Poiseuille flows, our results demonstrate consistency between equilibrium and non-equilibrium approaches across different flow types and confinement levels. This demonstrates the robust nature of linear response theory. We then explore the effects of fluid-wall and bulk fluid interactions on the hydrodynamic properties. These findings enhance the effectiveness of molecular-based simulations for investigating complex confined systems in nanofluidics, biology, and colloidal science, offering a complementary molecular-scale perspective to traditional continuum approaches.
Experimental observations of vibronic coherences in electronically excited colloidal semiconductor nanocrystals offer a window into the ultrafast dynamics of hot carrier cooling. In previous work, we showed that, in amine-passivated quantum dots (QDs), these coherences arise during relaxation through a cascade of conical intersections between electronically excited states. Here, in this work, we demonstrate the generality of this framework by application to QDs with surface-bound carboxylate ligands. A model involving a similar cascade of conical intersections accurately reproduces the frequencies of vibronic coherences observed with broadband multidimensional spectroscopy. The impact of ligands on the relaxation dynamics is attributed to two distinct mechanisms involving either electronic or vibrational coupling between the core and ligands. Compared to the amine-passivated QDs studied previously, the electronic coupling mechanism is less prominent in carboxylate-passivated QDs. Furthermore, comparison of acetate and formate ligands reveals that truncating the ligand alkyl chains alters the relaxation behavior predicted by the model.
The slow dynamics of non-concatenated ring melts remains a frontier problem in polymer science with implications for many soft material environments including cellular biophysics. Here, in this work, we report large-scale simulations of model ring melts that analyze the monomer and center-of-mass (CM) mean square displacements (MSD) and stress relaxation function on intermediate time and length scales. The degree of dynamical slowing down is characterized by the maximally sub-diffusive fractional time scaling exponents. The data span an exceptionally wide range of ring degrees of polymerization and stiffnesses and are not successfully organized based on the classic measure linear chain entanglement, N/N e . Rather, we find that the crossover degree of polymerization, N D , based on ring macromolecular caging that successfully allows master curves to be constructed for the long-time CM self-diffusion constant also collapses these temporal dynamic scaling exponents. Different properties display different exponents and exhibit one or two regimes of linear variation with the logarithm of N D / N . A distinct crossover of the CM-MSD and stress relaxation exponents emerges at sufficiently large N or stiffness that is not found for the monomer MSD, indicating a novel form of dynamic decoupling. This crossover aligns with the predicted critical degree of polymerization for transitioning from a weak to strong caging regime, indicative of activated transport. The latter may reflect the emergence of an intermolecular collective contribution to stress in analogy with dense soft colloidal matter. Suggestions are made for future theoretical work to address the rich patterns of behavior discovered.
Understanding the contact dynamics of nonspherical particles is crucial for accurately modeling colloidal and granular systems where shape anisotropy dictates structural organization and transport properties. We here introduce an energy-conserving contact dynamics framework for arbitrary convex rigid-body particles by implementing vertex–boundary interactions in 2D and vertex–surface and edge–edge detection in 3D. The established formulation enables continuous force evaluation and prevents particle overlap while conserving total energy during translational and rotational motion. We demonstrate the framework’s stability and its utility to capture packing behavior, anisotropic diffusion, and equations of state of polygonal and polyhedral particles as examples. The framework establishes a robust and extensible foundation for investigating nonequilibrium dynamics of complex nonspherical particulate systems, enabling enhanced understanding of phenomena across spatiotemporal scales in self- and directed-assembly, granular flows, and hydrodynamics, potentially coupled with interactions that represent underlying physical mechanisms.
Size-dependent phagocytosis is a well-characterized phenomenon in monocytes and macrophages. However, this size effect for preferential gene delivery to these important cell targets has not been fully exploited because commonly adopted stabilization methods for electrostatically complexed nucleic acid nanoparticles, such as PEGylation and charge repulsion, typically arrest the vehicle size below 200 nm. Here, we bridge the technical gap in scalable synthesis of larger submicron gene delivery vehicles by electrostatic self-assembly of charged nanoparticles, facilitated by a polymer structurally designed to modulate internanoparticle Coulombic and van der Waals forces. Specifically, our strategy permits controlled assembly of small poly(β-amino ester)/messenger ribonucleic acid (mRNA) nanoparticles into particles with a size that is kinetically tunable between 200 and 1,000 nm with high colloidal stability in physiological media. We found that assembled particles with an average size of 400 nm safely and most efficiently transfect monocytes following intravenous administration and mediate their differentiation into macrophages in the periphery. When a CpG adjuvant is co-loaded into the particles with an antigen mRNA, the monocytes differentiate into inflammatory dendritic cells and prime adaptive anticancer immunity in the tumor-draining lymph node. This platform technology offers a unique ligand-independent, particle-size-mediated strategy for preferential mRNA delivery and enables therapeutic paradigmsviamonocyte programming.
Colloidal nanoparticles are of great interest in modern science and industry. However, the thermodynamic mechanism and dynamics of nanoparticle growth have yet to be understood. Addressing these issues, we tracked hundreds of in-situ growth trajectories of a nanoparticle ensemble using liquid-phase TEM and found that the nanoparticle growth, including coalescence, exhibits nanoparticle size-dependent multiphasic dynamics, unexplainable by current theories. Motivated by this finding, we developed a model and theory for an ensemble of growing nanoparticles, providing a unified, quantitative understanding of the time-dependent mean and fluctuation of nanoparticle size and size-dependent growth rate profiles across various nanoparticle systems and experimental conditions. Our work reveals that the chemical potential in a small nanoparticle strongly deviates from the Gibbs-Thomson equation, shedding light on how it governs the size-dependent growth dynamics of nanoparticles.
Despite over a century of studies, fundamental questions remain about the processes governing crystal nucleation from melts or solutions. Research over the past three decades has presented mounting evidence for kinetic pathways of crystal nucleation that are more complex than envisioned by the simplest forms of classical theory. Such observations have been presented for colloidal and elemental systems with covalent and metallic bonding. Despite the technological and geochemical importance of molten salts, similar studies for these ionically bonded systems are currently lacking. Here we develop a machine learning interatomic potential for a model ionic system: LiF. The potential features quantum-level accuracy for both liquid and multiple solid polymorphs over wide temperature and pressure ranges and accurately reproduces experimentally measured properties. Thanks to the efficiency of the potential, which enables microsecond-scale molecular dynamics simulations, induction times for nucleation of LiF solids from their melts are computed over a range of undercoolings. With the aid of a set of robust local order parameters established here, the simulations reveal that homogeneous crystal nucleation in undercooled melts preferentially initiates from liquid regions showing slow dynamics and high bond orientational order simultaneously, and the second-shell order of both precritical nuclei and the surface of postcritical nuclei is dominated by hexagonal close packing and body-centered cubic local structure, even though the nucleus core is dominated by face-centered cubic structure corresponding to the stable rocksalt crystal structure. Finally, we establish a connection between the crystallization pathway and the equilibrium crystal-melt interface structure.
Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be nonconservative and nonreciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here, we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, nonidentical particles, and learns the effective nonreciprocal forces between particles with exquisite accuracy (R 2 > 0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model’s accuracy enables precise measurements of particle charge and screening length, identifying large deviations from common theoretical assumptions. Our ability to identify unknown physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.
Despite their adaptability and mechanical stability, Pickering emulsions based on the interfacial assembly of colloidal particles have not found use in iontronics, since the dense interfacial packing of micron-sized particles precludes functional connectivity between two droplets. In this work, we introduce a chemically reconfigurable droplet interface bilayer (DIB) platform based on the interfacial assembly of nanoparticle-surfactants (NPSs) that enables spontaneous or field-induced formation of ion-conducting nanochannels, eliminating the need of ionophores or nanochannel-forming proteins. These nanoscopic channels emerge from packing defects in the jammed interfacial assemblies of the charged NPSs and support size and charge selective, hysteretic ion transport governed by interfacial electrostatics and dimensional constraints. The NPS-DIBs show short-term and long-term plasticity, hallmarks of neuromorphic behavior, that are mediated by the structural and chemical design of the bilayer. These assemblies establish a versatile, chemically tunable platform that couples soft-matter mechanics with interfacial ionic functionality, offering a robust foundation for soft iontronic systems.