Shape memory in self-adapting colloidal crystals.

Reconfigurable, mechanically responsive crystalline materials are central components in many sensing, soft robotic, and energy conversion and storage devices1-4. Crystalline materials can readily deform under various stimuli and the extent of recoverable deformation is highly dependent upon bond type1,2,5-10. Indeed, for structures held together via simple electrostatic interactions, minimal deformations are tolerated. By contrast, structures held together by molecular bonds can, in principle, sustain much larger deformations and more easily recover their original configurations. Here we study the deformation properties of well-faceted colloidal crystals engineered with DNA. These crystals are large in size (greater than 100 µm) and have a body-centred cubic (bcc) structure with a high viscoelastic volume fraction (of more than 97%). Therefore, they can be compressed into irregular shapes with wrinkles and creases, and, notably, these deformed crystals, upon rehydration, assume their initial well-formed crystalline morphology and internal nanoscale order within seconds. For most crystals, such compression and deformation would lead to permanent, irreversible damage. The substantial structural changes to the colloidal crystals are accompanied by notable and reversible optical property changes. For example, whereas the original and structurally recovered crystals exhibit near-perfect (over 98%) broadband absorption in the ultraviolet-visible region, the deformed crystals exhibit significantly increased reflection (up to 50% of incident light at certain wavelengths), mainly because of increases in their refractive index and inhomogeneity.

Open-channel metal particle superlattices.

Although tremendous advances have been made in preparing porous crystals from molecular precursors1,2, there are no general ways of designing and making topologically diversified porous colloidal crystals over the 10-1,000 nm length scale. Control over porosity in this size range would enable the tailoring of molecular absorption and storage, separation, chemical sensing, catalytic and optical properties of such materials. Here, a universal approach for synthesizing metallic open-channel superlattices with pores of 10 to 1,000 nm from DNA-modified hollow colloidal nanoparticles (NPs) is reported. By tuning hollow NP geometry and DNA design, one can adjust crystal pore geometry (pore size and shape) and channel topology (the way in which pores are interconnected). The assembly of hollow NPs is driven by edge-to-edge rather than face-to-face DNA-DNA interactions. Two new design rules describing this assembly regime emerge from these studies and are then used to synthesize 12 open-channel superlattices with control over crystal symmetry, channel geometry and topology. The open channels can be selectively occupied by guests of the appropriate size and that are modified with complementary DNA (for example, Au NPs).

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.

Molecular DNA dendron vaccines.

A foundational principle of rational vaccinology is that vaccine structure plays a critical role in determining therapeutic efficacy, but in order to establish fundamental, effective, and translatable vaccine design parameters, a highly modular and well-defined platform is required. Herein, we report a DNA dendron vaccine, a molecular nanostructure that consists of an adjuvant DNA strand that splits into multiple DNA branches with a varied number of conjugated peptide antigens that is capable of dendritic cell uptake, immune activation, and potent cancer killing. We leveraged the well-defined architecture and chemical modularity of the DNA dendron to study structure-function relationships that dictate molecular vaccine efficacy, particularly regarding the delivery of immune-activating DNA sequences and antigenic peptides on a single chemical construct. We investigated how adjuvant and antigen placement and number impact dendron cellular uptake and immune activation, in vitro. These parameters also played a significant role in raising a potent and specific immune response against target cancer cells. By gaining this structural understanding of molecular vaccines, DNA dendrons successfully treated a mouse cervical human papillomavirus TC-1 cancer model, in vivo, where the vaccine structure defined its efficacy; the top-performing design effectively reduced tumor burden (<150 mm3 through day 30) and maintained 100% survival through 44 d after tumor inoculation.

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.

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.

Spherical nucleic acids as an infectious disease vaccine platform.

SignificanceUsing SARS-CoV-2 as a relevant case study for infectious disease, we investigate the structure-function relationships that dictate antiviral spherical nucleic acid (SNA) vaccine efficacy. We show that the SNA architecture can be rapidly employed to target COVID-19 through incorporation of the receptor-binding domain, and that the resulting vaccine potently activates human cells in vitro and mice in vivo. Furthermore, when challenged with a lethal viral infection, only mice treated with the SNA vaccine survived. Taken together, this work underscores the importance of rational vaccine design for infectious disease to yield vaccines that elicit more potent immune responses to effectively fight disease.

DNA Anchoring Strength Directly Correlates with Spherical Nucleic Acid-Based HPV E7 Cancer Vaccine Potency.

Vaccination for cancers arising from human papillomavirus (HPV) infection holds immense potential, yet clinical success has been elusive. Herein, we describe vaccination studies involving spherical nucleic acids (SNAs) incorporating a CpG adjuvant and a peptide antigen (E711-19) from the HPV-E7 oncoprotein. Administering the vaccine to humanized mice induced immunity-dependent on the oligonucleotide anchor chemistry (cholesterol vs (C12)9). SNAs containing a (C12)9-anchor enhanced IFN-γ production >200-fold, doubled memory CD8+ T-cell formation, and delivered more than twice the amount of oligonucleotide to lymph nodes in vivo compared to a simple admixture. Importantly, the analogous construct with a weaker cholesterol anchor performed similar to admix. Moreover, (C12)9-SNAs activated 50% more dendritic cells and generated T-cells cytotoxic toward an HPV+ cancer cell line, UM-SCC-104, with near 2-fold greater efficiency. These observations highlight the pivotal role of structural design, and specifically oligonucleotide anchoring strength (which correlates with overall construct stability), in developing efficacious therapeutic vaccines.

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.

Proteomimetic Polymers Trigger Potent Antigen-Specific T Cell Responses to Limit Tumor Growth.

Elicitation of effective antitumor immunity following cancer vaccination requires the selective activation of distinct effector cell populations and pathways. Here we report a therapeutic approach for generating potent T cell responses using a modular vaccination platform technology capable of inducing directed immune activation, termed the Protein-like Polymer (PLP). PLPs demonstrate increased proteolytic resistance, high uptake by antigen-presenting cells (APCs), and enhanced payload-specific T cell responses. Key design parameters, namely payload linkage chemistry, degree of polymerization, and side chain composition, were varied to optimize vaccine formulations. Linking antigens to the polymer backbone using an intracellularly cleaved disulfide bond copolymerized with a diluent amount of oligo(ethylene glycol) (OEG) resulted in the highest payload-specific potentiation of antigen immunogenicity, enhancing dendritic cell (DC) activation and antigen-specific T cell responses. Vaccination with PLPs carrying either gp100, E7, or adpgk peptides significantly increased the survival of mice inoculated with B16F10, TC-1, or MC38 tumors, respectively, without the need for adjuvants. B16F10-bearing mice immunized with gp100-carrying PLPs showed increased antitumor CD8+ T cell immunity, suppressed tumor growth, and treatment synergy when paired with two distinct stimulator of interferon gene (STING) agonists. In a human papillomavirus-associated TC-1 model, combination therapy with PLP and 2'3'-cGAMP resulted in 40% of mice completely eliminating implanted tumors while also displaying curative protection from rechallenge, consistent with conferment of lasting immunological memory. Finally, PLPs can be stored long-term in a lyophilized state and are highly tunable, underscoring the unique properties of the platform for use as generalizable cancer vaccines.

Encoding hierarchical assembly pathways of proteins with DNA.

The structural and functional diversity of materials in nature depends on the controlled assembly of discrete building blocks into complex architectures via specific, multistep, hierarchical assembly pathways. Achieving similar complexity in synthetic materials through hierarchical assembly is challenging due to difficulties with defining multiple recognition areas on synthetic building blocks and controlling the sequence through which those recognition sites direct assembly. Here, we show that we can exploit the chemical anisotropy of proteins and the programmability of DNA ligands to deliberately control the hierarchical assembly of protein-DNA materials. Through DNA sequence design, we introduce orthogonal DNA interactions with disparate interaction strengths ("strong" and "weak") onto specific geometric regions of a model protein, stable protein 1 (Sp1). We show that the spatial encoding of DNA ligands leads to highly directional assembly via strong interactions and that, by design, the first stage of assembly increases the multivalency of weak DNA-DNA interactions that give rise to an emergent second stage of assembly. Furthermore, we demonstrate that judicious DNA design not only directs assembly along a given pathway but can also direct distinct structural outcomes from a single pathway. This combination of protein surface and DNA sequence design allows us to encode the structural and chemical information necessary into building blocks to program their multistep hierarchical assembly. Our findings represent a strategy for controlling the hierarchical assembly of proteins to realize a diverse set of protein-DNA materials by design.

Crystal structure engineering in multimetallic high-index facet nanocatalysts.

In the context of metal particle catalysts, composition, shape, exposed facets, crystal structure, and atom distribution dictate activity. While techniques have been developed to control each of these parameters, there is no general method that allows one to optimize all parameters in the context of polyelemental systems. Herein, by combining a solid-state, Bi-influenced, high-index facet shape regulation strategy with thermal annealing, we achieve control over crystal structure and atom distribution on the exposed high-index facets, resulting in an unprecedentedly diverse library of chemically disordered and ordered multimetallic (Pt, Co, Ni, Cu, Fe, and Mn) tetrahexahedral (THH) nanoparticles. Density functional theory calculations show that surface Bi modification stabilizes the {210} high-index facets of the nanoparticles, regardless of their internal atomic ordering. Moreover, we find that the ordering transition temperatures for the nanoparticles are dependent on their composition, and, in the case of Pt3Fe1 THH nanoparticles, increasing Ni substitution leads to an order-to-disorder transition at 900 °C. Finally, we have discovered that ordered intermetallic THH Pt1Co1 nanocatalysts exhibit a catalytic performance superior to disordered THH Pt1Co1 nanoparticles and commercial Pt/C catalysts toward methanol electrooxidation, highlighting the importance of crystal structure and atom distribution control on high-index facets in nanoscale catalysts.

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.

Arrays of Colloidal Single Crystals Engineered with DNA in Lithographically Defined Microwells.

Lithographically defined microwell templates are used to study DNA-guided colloidal crystal assembly parameters, including superlattice position, habit orientation, and size, in an effort to increase our understanding of the crystallization process. In addition to enabling the synthesis of arrays of individual superlattices in arbitrary predefined patterns, the technique allows one to study the growth pathways of the crystals via ex situ scanning electron microscopy. Importantly, a Volmer-Weber (VM) (island formation)-like growth mode is identified, which has been reproduced via simulations. Notably, both experiment and simulation reveal that the crystallites merge and reorient within the microwells that defined the crystal growth to form single-crystalline structures, an observation not common for VM pathways. The control afforded by this platform will facilitate efforts in constructing metamaterials from colloidal crystals as well as their integration into optical devices and applications.

Cellular Delivery of Large Functional Proteins and Protein-Nucleic Acid Constructs via Localized Electroporation.

Delivery of proteins and protein-nucleic acid constructs into live cells enables a wide range of applications from gene editing to cell-based therapies and intracellular sensing. However, electroporation-based protein delivery remains challenging due to the large sizes of proteins, their low surface charge, and susceptibility to conformational changes that result in loss of function. Here, we use a nanochannel-based localized electroporation platform with multiplexing capabilities to optimize the intracellular delivery of large proteins (ß-galactosidase, 472 kDa, 75.38% efficiency), protein-nucleic acid conjugates (protein spherical nucleic acids (ProSNA), 668 kDa, 80.25% efficiency), and Cas9-ribonucleoprotein complex (160 kDa, ∼60% knock-out and ∼24% knock-in) while retaining functionality post-delivery. Importantly, we delivered the largest protein to date using a localized electroporation platform and showed a nearly 2-fold improvement in gene editing efficiencies compared to previous reports. Furthermore, using confocal microscopy, we observed enhanced cytosolic delivery of ProSNAs, which may expand opportunities for detection and therapy.

Ethanol-Induced Condensation and Decondensation in DNA-Linked Nanoparticles: A Nucleosome-like Model for the Condensed State.

Inspired by the conventional use of ethanol to induce DNA precipitation, ethanol condensation has been applied as a routine method to dynamically tune "bond" lengths (i.e., the surface-to-surface distances between adjacent nanoparticles that are linked by DNA) and thermal stabilities of colloidal crystals involving DNA-linked nanoparticles. However, the underlying mechanism of how the DNA bond that links gold nanoparticles changes in this class of colloidal crystals in response to ethanol remains unclear. Here, we conducted a series of all-atom molecular dynamic (MD) simulations to explore the free energy landscape for DNA condensation and decondensation. Our simulations confirm that DNA condensation is energetically much more favorable under 80% ethanol conditions than in pure water, as a result of ethanol's role in enhancing electrostatic interactions between oppositely charged species. Moreover, the condensed DNA adopts B-form in pure water and A-form in 80% ethanol, which indicates that the higher-order transition does not affect DNA's conformational preferences. We further propose a nucleosome-like supercoiled model for the DNA condensed state, and we show that the DNA end-to-end distance derived from this model matches the experimentally measured DNA bond length of about 3 nm in the fully condensed state for DNA where the measured length is 16 nm in water. Overall, this study provides an atomistic understanding of the mechanism underlying ethanol-induced condensation and water-induced decondensation, while our proposed nucleosome-like model allows the design of new strategies for interpreting experimental studies of DNA condensation.

Symmetry-Breaking Dendrimer Synthons in Colloidal Crystal Engineering with DNA.

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. Herein, 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, Si2Sr. 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.

Dynamic Metal-Phenolic Coordination Complexes for Versatile Surface Nanopatterning.

We report a general nanopatterning strategy that takes advantage of the dynamic coordination bonds between polyphenols and metal ions (e.g., Fe3+ and Cu2+) to create structures on surfaces with a range of properties. With this methodology, under acidic conditions, 29 metal-phenolic complex-based precursors composed of different polyphenols and metal ions are patterned using scanning probe and large-area cantilever free nanolithography techniques, resulting in a library of deposited metal-phenolic nanopatterns. Significantly, post-treatment of the patterns under basic conditions (i.e., ammonia vapor) triggers a change in coordination state and results in the in situ generation of more stable networks firmly attached to the underlying substrates. The methodology provides control over feature size, shape, and composition, almost regardless of substrate (e.g., Si, Au, and silicon nitride). Under reducing conditions (i.e., H2) at elevated temperatures (180-600 °C), the patterned features have been used as nanoreactors to synthesize individual metal nanoparticles. At room temperature, the ammonia-treated features can reduce Ag+ to form metal nanostructures and be modified with peptides, proteins, and thiolated DNA via Michael addition and/or Schiff base reaction. The generality of this technique should make it useful for a wide variety of researchers interested in modifying surfaces for catalytic, chemical and biological sensing, and template-directed assembly purposes.

Molecular Thin Films Enable the Synthesis and Screening of Nanoparticle Megalibraries Containing Millions of Catalysts.

Megalibraries are centimeter-scale chips containing millions of materials synthesized in parallel using scanning probe lithography. As such, they stand to accelerate how materials are discovered for applications spanning catalysis, optics, and more. However, a long-standing challenge is the availability of substrates compatible with megalibrary synthesis, which limits the structural and functional design space that can be explored. To address this challenge, thermally removable polystyrene films were developed as universal substrate coatings that decouple lithography-enabled nanoparticle synthesis from the underlying substrate chemistry, thus providing consistent lithography parameters on diverse substrates. Multi-spray inking of the scanning probe arrays with polymer solutions containing metal salts allows patterning of >56 million nanoreactors designed to vary in composition and size. These are subsequently converted to inorganic nanoparticles via reductive thermal annealing, which also removes the polystyrene to deposit the megalibrary. Megalibraries with mono-, bi-, and trimetallic materials were synthesized, and nanoparticle size was controlled between 5 and 35 nm by modulating the lithography speed. Importantly, the polystyrene coating can be used on conventional substrates like Si/SiOx, as well as substrates typically more difficult to pattern on, such as glassy carbon, diamond, TiO2, BN, W, or SiC. Finally, high-throughput materials discovery is performed in the context of photocatalytic degradation of organic pollutants using Au-Pd-Cu nanoparticle megalibraries on TiO2 substrates with 2,250,000 unique composition/size combinations. The megalibrary was screened within 1 h by developing fluorescent thin-film coatings on top of the megalibrary as proxies for catalytic turnover, revealing Au0.53Pd0.38Cu0.09-TiO2 as the most active photocatalyst composition.