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A comprehensive framework to automatically perform size and morphology recognition of nanoparticles in SEM images in a high-throughput manner.
Using host-guest molecular recognition at the oil-water interface, a new type of photoresponsive nanoparticle surfactant (NPS) was designed and prepared to structure liquids. With the help of a polymeric surfactant, the interfacial host-guest interactions can be significantly enhanced, leading to the rapid formation and assembly of a NP monolayer and offering sufficient binding energy to hold the NPs in a jammed state. The assembly of the NPSs can be reversibly manipulated via a photoswitchable jamming-to-unjamming transition, endowing the interface as well as the macroscopic assemblies with responsiveness to the external trigger (photons). Finally, this study for the first time opens a pathway for the construction of multiresponsive, structured all-liquid systems by introducing host-guest chemistry, showing promising potential applications in encapsulation, delivery systems, and unique microfluidic devices.
The development of germline-targeting vaccines represents a potentially transformative strategy to elicit broadly neutralizing antibodies (bnAbs) against HIV and other antigenically diverse pathogens. Here, we report on structural characterization of vaccine-elicited VRC01-class bnAb precursors in the IAVI G001 Phase 1 clinical trial with the eOD-GT8 60mer nanoparticle as immunogen. High-resolution X-ray structures of eOD-GT8 monomer complexed with Fabs of five VRC01-class bnAb precursors with >90% germline identity revealed a conserved mode of binding to the HIV CD4-binding site via IGHV1-2-encoded heavy chains, mirroring mature bnAb interactions. The light-chain V-gene diversity emulated VRC01 bnAbs and stabilized antigen engagement, while their conserved five-residue LCDR3 motifs prevented steric clashes. Notably, the VRC01-class bnAb precursors accommodated the N276 glycan, a key barrier in HIV Env recognition, through structural rearrangements in HCDR3 or LCDR1, despite its absence in the immunogen. Surface plasmon resonance analysis showed that 87% of elicited antibodies retained glycan binding capacity, albeit with reduced affinity. These findings validate the ability of eOD-GT8 60mer nanoparticles to prime VRC01-class bnAb precursors with native-like paratopes but with intrinsic glycan adaptability. Structural mimicry of mature bnAbs was observed even with limited somatic hypermutation, indicating that critical features are encoded in the germline repertoire. The structures highlight how germline-encoded features drive bnAb-like recognition at early stages. This work provides molecular evidence supporting germline targeting in humans and provides guidance for designing booster immunogens to shepherd affinity maturation toward broad neutralization.
Peptoids (or poly-N-substituted glycines) hold immense potential for assembling into hierarchically structured functional materials via controlled molecular interactions. To create self-assembled materials with tailored functionalities, peptoid sequences are often conjugated with reactive or recognition motifs to enable applications including specific binding, biomimetic catalysis, and fluorescence imaging. However, the direct integration of bulky functional motifs into peptoid sequences can disrupt assembly processes and structural outcomes. Herein, we present a post-modification strategy for functionalizing pre-formed 2D crystalline assemblies. Through introducing clickable active sites, such as azide, alkyne, or thiol groups into a peptoid sequence, site-specific conjugation is achieved post-assembly via efficient “click”-type reactions. This strategy enables the ordered alignment of functional groups and gold nanoparticles (Au NPs) on the surface of 2D peptoid nanomaterials with controlled density, while preserving their high crystallinity and structural integrity. Furthermore, we demonstrated that nanomembranes functionalized with both Au NPs and porphyrins enhance the efficiency and selectivity of the photooxidation of 2-chloroethyl ethyl sulfide, a simulant of sulfur mustard. This innovative strategy lays the groundwork for advancing peptoid-based functional materials across diverse applications, from catalysis to biomedicine.
Abstract The jamming of nanoparticle surfactants (NPSs) at liquid–liquid interface imparts attractive properties to the interfacial assemblies and enables the structuring of liquids. Herein, we report photoresponsive supramolecular microcapsules with jammed NPS assemblies at the oil‐water interface, taking advantage of host–guest molecular recognition. The permeability of the colloidal membrane can be effectively manipulated by switching the NPSs from a jammed state to an unjammed state with a photo trigger, leading to a controlled molecular diffusion and release, affording a versatile platform for the construction of next generation smart microcapsule systems.
The jamming of nanoparticle surfactants (NPSs) at liquid-liquid interface imparts attractive properties to the interfacial assemblies and enables the structuring of liquids. Herein, we report photoresponsive supramolecular microcapsules with jammed NPS assemblies at the oil-water interface, taking advantage of host-guest molecular recognition. The permeability of the colloidal membrane can be effectively manipulated by switching the NPSs from a jammed state to an unjammed state with a photo trigger, leading to a controlled molecular diffusion and release, affording a versatile platform for the construction of next generation smart microcapsule systems.
Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.
Scanning Electron Microscopy (SEM) images provide a variety of structural and morphological information for the characterization of the nanomaterials. This code offers automatic recognition and quantitative analysis of SEM images in a high-throughput manner using computer vision and machine learning techniques. The main function of this application is to extract particle size and morphology information of overlapping nanoparticles and core-shell nanostructures in a user friendly interface. The code is written in C++ with QT environment, and has been tested on MacOSX.
Broad immune responses are needed to mitigate viral evolution and escape. To induce antibodies against conserved receptor-binding domain (RBD) regions of SARS-like betacoronavirus (sarbecovirus) spike proteins that recognize SARS-CoV-2 variants of concern and zoonotic sarbecoviruses, we developed mosaic-8b RBD nanoparticles presenting eight sarbecovirus RBDs arranged randomly on a 60-mer nanoparticle. Mosaic-8b immunizations protected animals from challenges from viruses whose RBDs were matched or mismatched to those on nanoparticles. Here, we describe neutralizing mAbs isolated from mosaic-8b-immunized rabbits, some on par with Pemgarda, the only currently FDA-approved therapeutic mAb. Deep mutational scanning, in vitro selection of spike resistance mutations, and single-particle cryo-electron microscopy structures of spike–antibody complexes demonstrated targeting of conserved RBD epitopes. Rabbit mAbs included critical D-gene segment RBD-recognizing features in common with human anti-RBD mAbs, despite rabbit genomes lacking an equivalent human D-gene segment, thus demonstrating that the immune systems of humans and other mammals can utilize different antibody gene segments to arrive at similar modes of antigen recognition. These results suggest that animal models can be used to elicit anti-RBD mAbs with similar properties to those raised in humans, which can then be humanized for therapeutic use, and that mosaic RBD nanoparticle immunization coupled with multiplexed screening represents an efficient way to generate and select broadly cross-reactive therapeutic pan-sarbecovirus and pan-SARS-CoV-2 variant mAbs.
Flexible metal-organic frameworks (MOFs) are promising for gas separation due to their molecular recognition capabilities. However, gate-opening behavior in certain flexible MOFs can reduce gas selectivity, enabling simultaneous adsorption of multiple gases. We introduce a binder-free, pressure-free densification process to enhance gas selectivity in flexible zeolitic imidazolate frameworks (ZIF-7 and ZIF-9) by optimizing nanoparticle packing. Unlike powders, the densified ZIF-7 and ZIF-9 retain rhombohedral crystal structures after solvent removal, confirmed via synchrotron powder X-ray diffraction. This process modifies adsorption behavior by introducing a diffusion barrier, enabling diffusivity-driven kinetic separation and enhanced CO2/hydrocarbon selectivity. At 0.5 bar, ZIF-7 mono shows 2×, 4×, and 9.5× higher CO 2 selectivity over C 2 H 2 , C 2 H 4 , and C 2 H 6 , respectively, versus ZIF-7 pwd . ZIF-9 mono similarly shows 6.5×, 3×, and 39× improvements over ZIF-9 pwd . Both monoliths outperform powders in ternary gas mixtures. This study provides a novel strategy to control gate opening and enhance gas selectivity in flexible MOFs for practical separation applications.
Breaking symmetry in colloidal crystals is challenging due to the inherent chemical and structural isotropy of many nanoscale building blocks. If a non-particle component could be used to anisotropically encode such building blocks with orthogonal recognition properties, one could expand the scope of structural and compositional possibilities of colloidal crystals beyond what is thus far possible with purely particle-based systems. In this work, we report the synthesis and characterization of novel DNA dendrimers that function as symmetry-breaking synthons, capable of programming anisotropic and orthogonal interactions within colloidal crystals. When the DNA dendrimers have identical sticky ends, they hybridize with DNA-functionalized nanoparticles to yield three distinct colloidal crystals, dictated by dendrimer size, including a structure not previously reported in the field of colloidal crystal engineering, Si 2 Sr. When used as symmetry-breaking synthons (when the sticky ends deliberately consist of orthogonal sequences), the synthesis of binary and ternary colloidal alloys with structures that can only be realized through directional interactions is possible. Furthermore, by modulating the extent of shape anisotropy within the DNA dendrimers, the local distribution of the nanoparticles within the crystals can be directed.
Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with potential applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes such as (GT)n-SWCNTs recognizing the neuromodulator, dopamine. It remains unclear how the ssDNA conformation on the SWCNT surface contributes to functionality, as observations have been limited to computational models or experiments under dehydrated conditions that differ substantially from the aqueous biological environments in which the nanosensors are applied. We demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two (GT) n ssDNA oligomer lengths (n = 6, 15) that are used on SWCNTs in the context of dopamine sensing and measure the ssDNA conformational changes as a function of ionic strength and during dopamine interaction. Here, we show that the shorter oligomer, (GT) 6 , adopts a more periodically ordered ring structure along the SWCNT axis (inter-ssDNA distance of 8.6 ± 0.3 nm), compared to the longer (GT) 15 oligomer (most probable 5'-to-5' distance of 14.3 ± 1.1 nm). During molecular recognition, XSI reveals that dopamine elicits simultaneous axial elongation and radial constriction of adsorbed ssDNA on the SWCNT surface. Our approach using XSI to probe solution-phase morphologies of polymer-functionalized SWCNTs can be applied to yield insights into sensing mechanisms and inform future design strategies for nanoparticle-based sensors.
The chemical interactions that lead to the emergence of hierarchical structures are often highly complex and difficult to program. In this paper, the synthesis of a series of superlattices based upon 30 different structurally reconfigurable DNA dendrimers is reported, each of which presents a well-defined number of single-stranded oligonucleotides (i.e., sticky ends) on its surface. Such building blocks assemble with complementary DNA-functionalized gold nanoparticles (AuNPs) to yield five distinct crystal structures, depending upon choice of dendrimer and defined by phase symmetry. These DNA dendrimers can associate to form micelle-dendrimers, whereby the extent of association can be modulated based upon surfactant concentration and dendrimer length to produce a low-symmetry Ti5Ga4-type phase that has yet to be reported in the field of colloidal crystal engineering. Taken together, colloidal crystals that feature three different types of particle bonding interactions.template-dendron, dendrimer-dendrimer, and DNA-modified AuNP-dendrimer.are reported, illustrating how sequence-defined recognition and dynamic association can be combined to yield complex hierarchical materials.
Polyelectrolyte complex micelles are hydrophilic nanoparticles that self-assemble in aqueous environments due to associative microphase separation between oppositely charged blocky polyelectrolytes. In this work, we employ a suite of physical characterization tools to examine the effect of charged block length mismatch on the equilibrium structure of double diblock polyelectrolyte complex micelles (D-PCMs) by mixing a diverse library of peptide and synthetic charged-neutral block polyelectrolytes with a wide range of charged block lengths (25–200 units) and chemistries. Early work on D-PCMs suggested that this class of micelles can only be formed from blocky polyelectrolytes with identical charged block lengths, a phenomenon referred to as chain length recognition. Here, we use salt annealing to create PCMs at equilibrium, which shows that chain length recognition, a longstanding hurdle to repeatable self-assembly from mismatched polyelectrolytes, can be overcome. Interestingly, D-PCM structure–property relationships display a range of values that vary systematically with the charged block lengths and chemical identity of constituent polyelectrolyte pairings and cannot be described by generalizable scaling laws. We discuss the interdependent growth behavior of the radius, ionic pair aggregation number, and density in the micelle core for three chemically distinct diblock pairings and suggest a potential physical mechanism that leads to this unique behavior. By comparing the results of these D-PCMs to the scaling laws recently developed for single diblock polyelectrolyte complex micelles (S-PCMs: diblock + homopolymer), we observe that D-PCM design schemes reduce the size and aggregation number and restrict their growth to a function of charged block length relative to S-PCMs. Understanding these favorable attributes enables more predictive use of a wider array of charged molecular building blocks to anticipate and control macroscopic properties of micelles spanning countless storage and delivery applications.
Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.
Plant nanobiotechnology has the potential to revolutionize agriculture. However, the lack of effective methods to deliver nanoparticles (NPs) to the precise locations in plants where they are needed impedes these technological innovations. Here, model gold nanoparticles (AuNP) were coated with citrate, bovine serum albumin (BSA) as a protein control, or LM6-M, an antibody with an affinity for functional groups unique to stomata on leaf surfaces to deliver the AuNPs to stomata. One-month-old Vicia faba leaves were exposed via drop deposition to aqueous suspensions of LM6-M-coated AuNPs and allowed to air dry. After rinsing, Au distribution on the leaf surface was investigated by enhanced dark-field microscopy and X-ray fluorescence mapping. While citrate-coated AuNPs randomly covered the plant leaves, LM6M-AuNPs strongly adhered to the stomata and remained on the leaf surface after rinsing, and BSA-AuNPs specifically targeted trichome hairs. To the authors’ knowledge, this is the first report of active targeting of live leaf structures using NPs coated with molecular recognition molecules. Finally, this proof-of-concept study provides a strategy for future targeted nanopesticide delivery research.
Abstract Challenges to deep sample imaging have necessitated the development of special techniques such as spatially offset optical spectroscopy to collect signals that have travelled through several layers of tissue. However, these techniques provide only spectral information in one dimension (i.e., depth). Here, we describe a general and practical method, referred to as Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID). The sensing strategy integrates (1) the spatial offset detection concept by computationally binning 2D optical data associated with digital offsets based on selected radial pixel distances from the excitation source; (2) hyperspectral imaging using tunable filter; and (3) digital image binding and collation. ORCHID is a versatile modality that is designed to collect optical signals deep inside samples across three spatial (X, Y, Z) as well as spectral dimensions. The ORCHID method is applicable to various optical techniques that exhibit narrow-band structures, from Raman scattering to quantum dot luminescence. Samples containing surface-enhanced Raman scattering (SERS)-active gold nanostar probes and quantum dots embedded in gel were used to show a proof of principle for the ORCHID concept. The resulting hyperspectral data cube is shown to spatially locate target emitting nanoparticle volumes and provide spectral information for in-depth 3D imaging.
Crystallization defines what the world is all about, ranging from biomineralization in living organisms to various materials in our lives. During these processes, biomineralization set a superexcellent example of the additive-controlled crystallization. However, the high complexity and low stability of peptides and proteins preclude their wide applications in controlling inorganic crystallization beyond the biological systems. Peptoids are one type of sequence-defined biomimetic polymers with a simple structure, high stability, protein-like molecular recognition, and distinctive self-assembly properties. They provide a promising alternative to mimic peptides and proteins for controlling inorganic crystallization and extend its application to predictive materials synthesis. In this review article, we extract the main concepts of peptoid engineering and highlight the recent advances in peptoid-controlled inorganic crystallization. Our focus is to understand the principles of sequence engineering that lead to the predictable physiochemical properties of these substances, which will give insights into other inorganic systems.