Using Surface Composition and Energy to Control the Formation of Either Tetrahexahedral or Hexoctahedral High-Index Facet Nanostructures.

High-index facet nanoparticles with structurally complex shapes, such as tetrahexahedron (THH) and hexoctahedron (HOH), represent a class of materials that are important for catalysis, and the study of them provides a fundamental understanding of the relationship between surface structures and catalytic properties. However, the high surface energies render them thermodynamically unfavorable compared to low-index facets, thereby making their syntheses challenging. Herein, we report a method to control the shape of high-index facet Cu nanoparticles (either THH with {210} facets or HOH with {421} facets) by tuning the facet surface energy with trace amounts of Te atoms. Density functional theory (DFT) calculations reveal that the density of Te atoms on Cu nanoparticles can change the relative stability of the high-index facets associated with either the THH or HOH structures. By controlling the annealing conditions and the rate of Te dealloying from CuTe nanoparticles, the surface density of Te atoms can be deliberately adjusted, which can be used to force the formation of either THH (higher surface Te density) or HOH (lower surface Te density) nanoparticles.

Energy transition needs new materials.

The decreasing cost of electricity worldwide from wind and solar energy, as well as that of end-use technologies such as electric vehicles, reflect substantial progress made toward replacing fossil fuels with alternative energy sources. But a full transition to clean energy can only be realized if numerous challenges are overcome. Many problems can be addressed through the discovery of new materials that improve the efficiency of energy production and consumption; reduce the need for scarce mineral resources; and support the production of green hydrogen, clean ammonia, and carbon-neutral hydrocarbon fuels. However, research and development of new energy materials are not as aggressive as they should be to meet the demands of climate change.

Machine Learning-Enabled Image Classification for Automated Electron Microscopy.

Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Reversible strain-promoted DNA polymerization.

Molecular strain can be introduced to influence the outcome of chemical reactions. Once a thermodynamic product is formed, however, reversing the course of a strain-promoted reaction is challenging. Here, a reversible, strain-promoted polymerization in cyclic DNA is reported. The use of nonhybridizing, single-stranded spacers as short as a single nucleotide in length can promote DNA cyclization. Molecular strain is generated by duplexing the spacers, leading to ring opening and subsequent polymerization. Then, removal of the strain-generating duplexers triggers depolymerization and cyclic dimer recovery via enthalpy-driven cyclization and entropy-mediated ring contraction. This reversibility is retained even when a protein is conjugated to the DNA strands, and the architecture of the protein assemblies can be modulated between bivalent and polyvalent states. This work underscores the utility of using DNA not only as a programmable ligand for assembly but also as a route to access restorable bonds, thus providing a molecular basis for DNA-based materials with shape-memory, self-healing, and stimuli-responsive properties.

Sub-Diffraction Correlation of Quantum Emitters and Local Strain Fields in Strain-Engineered WSe2 Monolayers.

Strain-engineering in atomically thin metal dichalcogenides is a useful method for realizing single-photon emitters (SPEs) for quantum technologies. Correlating SPE position with local strain topography is challenging due to localization inaccuracies from the diffraction limit. Currently, SPEs are assumed to be positioned at the highest strained location and are typically identified by randomly screening narrow-linewidth emitters, of which only a few are spectrally pure. In this work, hyperspectral quantum emitter localization microscopy is used to locate 33 SPEs in nanoparticle-strained WSe2 monolayers with sub-diffraction-limit resolution (≈30 nm) and correlate their positions with the underlying strain field via image registration. In this system, spectrally pure emitters are not concentrated at the highest strain location due to spectral contamination; instead, isolable SPEs are distributed away from points of peak strain with an average displacement of 240 nm. These observations point toward a need for a change in the design rules for strain-engineered SPEs and constitute a key step toward realizing next-generation quantum optical architectures.

Multimodal neuro-nanotechnology: Challenging the existing paradigm in glioblastoma therapy.

Integrating multimodal neuro- and nanotechnology-enabled precision immunotherapies with extant systemic immunotherapies may finally provide a significant breakthrough for combatting glioblastoma (GBM). The potency of this approach lies in its ability to train the immune system to efficiently identify and eradicate cancer cells, thereby creating anti-tumor immune memory while minimizing multi-mechanistic immune suppression. A critical aspect of these therapies is the controlled, spatiotemporal delivery of structurally defined nanotherapeutics into the GBM tumor microenvironment (TME). Architectures such as spherical nucleic acids or poly(beta-amino ester)/dendrimer-based nanoparticles have shown promising results in preclinical models due to their multivalency and abilities to activate antigen-presenting cells and prime antigen-specific T cells. These nanostructures also permit systematic variation to optimize their distribution, TME accumulation, cellular uptake, and overall immunostimulatory effects. Delving deeper into the relationships between nanotherapeutic structures and their performance will accelerate nano-drug development and pave the way for the rapid clinical translation of advanced nanomedicines. In addition, the efficacy of nanotechnology-based immunotherapies may be enhanced when integrated with emerging precision surgical techniques, such as laser interstitial thermal therapy, and when combined with systemic immunotherapies, particularly inhibitors of immune-mediated checkpoints and immunosuppressive adenosine signaling. In this perspective, we highlight the potential of emerging treatment modalities, combining advances in biomedical engineering and neurotechnology development with existing immunotherapies to overcome treatment resistance and transform the management of GBM. We conclude with a call to action for researchers to leverage these technologies and accelerate their translation into the clinic.

Space-tiled colloidal crystals from DNA-forced shape-complementary polyhedra pairing.

Generating space-filling arrangements of most discrete polyhedra nanostructures of the same shape is not possible. However, if the appropriate individual building blocks are selected (e.g., cubes), or multiple shapes of the appropriate dimensions are matched (e.g., octahedra and tetrahedra) and their pairing interactions are subsequently forced, space-filled architectures may be possible. With flexible molecular ligands (polyethylene glycol-modified DNA), the shape of a polyhedral nanoparticle can be deliberately altered and used to realize geometries that favor space tessellation. In this work, 10 new colloidal crystals were synthesized from DNA-modified nanocrystal building blocks that differed in shapes and sizes, designed to form space-filling architectures with micron-scale dimensions. The insights and capabilities provided by this new strategy substantially expand the scope of colloidal crystals possible and provide an expanded tool kit for researchers interested in designing metamaterials.

Colloidal quasicrystals engineered with DNA.

In principle, designing and synthesizing almost any class of colloidal crystal is possible. Nonetheless, the deliberate and rational formation of colloidal quasicrystals has been difficult to achieve. Here we describe the assembly of colloidal quasicrystals by exploiting the geometry of nanoscale decahedra and the programmable bonding characteristics of DNA immobilized on their facets. This process is enthalpy-driven, works over a range of particle sizes and DNA lengths, and is made possible by the energetic preference of the system to maximize DNA duplex formation and favour facet alignment, generating local five- and six-coordinated motifs. This class of axial structures is defined by a square-triangle tiling with rhombus defects and successive on-average quasiperiodic layers exhibiting stacking disorder which provides the entropy necessary for thermodynamic stability. Taken together, these results establish an engineering milestone in the deliberate design of programmable matter.

Discovering polyelemental nanostructures with redistributed plasmonic modes through combinatorial synthesis.

Coupling plasmonic and functional materials provides a promising way to generate multifunctional structures. However, finding plasmonic nanomaterials and elucidating the roles of various geometric and dielectric configurations are tedious. This work describes a combinatorial approach to rapidly exploring and identifying plasmonic heteronanomaterials. Symmetry-broken noble/non-noble metal particle heterojunctions (~100 nanometers) were synthesized on multiwindow silicon chips with silicon nitride membranes. The metal types and the interface locations were controlled to establish a nanoparticle library, where the particle morphology and scattering color can be rapidly screened. By correlating structural data with near- and far-field single-particle spectroscopy data, we found that certain low-energy plasmonic modes could be supported across the heterointerface, while others are localized. Furthermore, we found a series of triangular heteronanoplates stabilized by epitaxial Moiré superlattices, which show strong plasmonic responses despite largely comprising a lossy metal (~70 atomic %). These architectures can become the basis for multifunctional and cost-effective plasmonic devices.

Bimolecularly passivated interface enables efficient and stable inverted perovskite solar cells.

Compared with the n-i-p structure, inverted (p-i-n) perovskite solar cells (PSCs) promise increased operating stability, but these photovoltaic cells often exhibit lower power conversion efficiencies (PCEs) because of nonradiative recombination losses, particularly at the perovskite/C60 interface. We passivated surface defects and enabled reflection of minority carriers from the interface into the bulk using two types of functional molecules. We used sulfur-modified methylthio molecules to passivate surface defects and suppress recombination through strong coordination and hydrogen bonding, along with diammonium molecules to repel minority carriers and reduce contact-induced interface recombination achieved through field-effect passivation. This approach led to a fivefold longer carrier lifetime and one-third the photoluminescence quantum yield loss and enabled a certified quasi-steady-state PCE of 25.1% for inverted PSCs with stable operation at 65°C for >2000 hours in ambient air. We also fabricated monolithic all-perovskite tandem solar cells with 28.1% PCE.

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning.

A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.

Cellular Export Fate of Liposomal Spherical Nucleic Acids.

Liposomal spherical nucleic acids (LSNAs) are useful structures for oligonucleotide-based cell modulation because of their biocompatibility and ability to readily enter cells without transfection agents. Understanding LSNA trafficking is key to developing applications, but while much is understood about LSNA cell uptake, little is known about their export fate. Here, we study LSNA export through pulse-chase studies with fluorophore-labeled LSNAs. Our findings show that the components of LSNAs are differentially exported by cells, with the phospholipids showing 90-100% export and the oligonucleotides showing 30-45% export over 24 h in RAW264.7 macrophages. Despite the increase in the level of uptake of LSNAs, these percentages are not significantly influenced by whether the materials are taken up as LSNAs or as the individual components. The exported oligonucleotide material consists of a full-length oligonucleotide with the phospholipid anchor modified by loss of a fatty acid. The exported liposome-derived phospholipids also had a fatty acid removed. Moreover, the exported oligonucleotide-lysophospholipid conjugates retain immunostimulatory potential. These findings indicate that after cellular entry LSNAs are degraded into lysophospholipids, something to which they are susceptible due to the presence of hydrolyzable ester bonds. The export percentage of the resultant materials over 24 h is independent of the amount imported, such that greater initial import leads to a similar fold increase in exported material. This work therefore reveals an intracellular feature of LSNAs and shows that the enhanced uptake achieved with LSNAs can be exploited to maximize the amount of material subsequently exported, suggesting avenues for leveraging pharmacologic effects from exported LSNA components.

Ultrastrong colloidal crystal metamaterials engineered with DNA.

Lattice-based constructs, often made by additive manufacturing, are attractive for many applications. Typically, such constructs are made from microscale or larger elements; however, smaller nanoscale components can lead to more unusual properties, including greater strength, lighter weight, and unprecedented resiliencies. Here, solid and hollow nanoparticles (nanoframes and nanocages; frame size: ~15 nanometers) were assembled into colloidal crystals using DNA, and their mechanical strengths were studied. Nanosolid, nanocage, and nanoframe lattices with identical crystal symmetries exhibit markedly different specific stiffnesses and strengths. Unexpectedly, the nanoframe lattice is approximately six times stronger than the nanosolid lattice. Nanomechanical experiments, electron microscopy, and finite element analysis show that this property results from the buckling, densification, and size-dependent strain hardening of nanoframe lattices. Last, these unusual open architectures show that lattices with structural elements as small as 15 nanometers can retain a high degree of strength, and as such, they represent target components for making and exploring a variety of miniaturized devices.

Tuning DNA Dissociation from Spherical Nucleic Acids for Enhanced Immunostimulation.

The stability of the core can significantly impact the therapeutic effectiveness of liposome-based drugs. While the spherical nucleic acid (SNA) architecture has elevated liposomal stability to increase therapeutic efficacy, the chemistry used to anchor the DNA to the liposome core is an underexplored design parameter with a potentially widespread biological impact. Herein, we explore the impact of SNA anchoring chemistry on immunotherapeutic function by systematically studying the importance of hydrophobic dodecane anchoring groups in attaching DNA strands to the liposome core. By deliberately modulating the size of the oligomer that defines the anchor, a library of structures has been established. These structures, combined with in vitro and in vivo immune stimulation analyses, elucidate the relationships between and importance of anchoring strength and dissociation of DNA from the SNA shell on its biological properties. Importantly, the most stable dodecane anchor, (C12)9, is superior to the n = 4-8 and 10 structures and quadruples immune stimulation compared to conventional cholesterol-anchored SNAs. When the OVA1 peptide antigen is encapsulated by the (C12)9 SNA and used as a therapeutic vaccine in an E.G7-OVA tumor model, 50% of the mice survived the initial tumor, and all of those survived tumor rechallenge. Importantly, the strong innate immune stimulation does not cause a cytokine storm compared to linear immunostimulatory DNA. Moreover, a (C12)9 SNA that encapsulates a peptide targeting SARS-CoV-2 generates a robust T cell response; T cells raised from SNA treatment kill >40% of target cells pulsed with the same peptide and ca. 45% of target cells expressing the entire spike protein. This work highlights the importance of using anchor chemistry to elevate SNA stability to achieve more potent and safer immunotherapeutics in the context of both cancer and infectious disease.

Transforming Hairpin-like siRNA-Based Spherical Nucleic Acids into Biocompatible Constructs.

The design and synthesis of hairpin-like small interfering RNA spherical nucleic acids (siRNA-SNAs) based upon biocompatible liposome nanoparticle cores are described. The constructs were characterized by gel electrophoresis, dynamic light scattering, and OliGreen-based oligonucleotide quantification. These siRNA-SNA nanoconstructs enter cells 20-times more efficiently than linear siRNA in as little as 4 h, while exhibiting a 4-fold reduction in cytotoxicity compared with conventional siRNA-SNAs composed of gold nanoparticle cores. Importantly, these siRNA-SNA constructs effectively inhibit angiogenesis in vitro by silencing vascular endothelial growth factor, a key mediator of angiogenesis in a multitude of diseases, in human umbilical vein endothelial cells. This work shows how hairpin architectures can be chemically incorporated into biocompatible SNAs in a way that retains advantageous SNA properties and maximizes gene regulation capabilities.

Hints of Growth Mechanism Left in Supercrystals.

Supercrystals of DNA-functionalized nanoparticles are visualized in three dimensions using X-ray ptychographic tomography, and their reciprocal spaces are mapped with small-angle X-ray scattering in order to better understand their internal defect structures. X-ray ptychographic tomography reveals various types of defects in an assembly that otherwise exhibits a single crystalline diffraction pattern. On average, supercrystals composed of smaller nanoparticles are smaller in size than supercrystals composed of larger particles. Additionally, supercrystals composed of small nanoparticles are typically aggregated into larger "necklace-like" structures. Within these larger structures, some but not all pairs of connected domains are coherent in their relative orientations. In contrast, supercrystals composed of larger nanoparticles with longer DNA ligands typically form faceted crystals. The combination of these two complementary X-ray techniques reveals that the crystalline assemblies grow by aggregation of smaller assemblies followed by rearrangement of nanoparticles.