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Self-assembling DNA crystals have emerged over the last two decades as an efficient and effective means of organizing matter at the nanoscale, but functionalization of these lattices has proved challenging as physiological buffer conditions are required to maintain structural integrity. In this manuscript, we demonstrate the silicification of porous DNA crystals using sol-gel chemistry. We identify reaction conditions that produce the minimum coating thickness to confer environmental protection, and subsequently measure the protective ability of the silica coating to various stressors, including heat, low ionic strength solution, organic solvents, and unprotected flash freezing. By soaking ions and dyes into the lattice after silica coating, we demonstrate that the crystals maintain their pores, and that the major groove of the DNA can still be used as a site-specific template for chemical modifications. We image a library of different crystal motifs by electron microscopy and confirm the presence of silica using energy dispersive spectroscopy. Finally, we perform X-ray diffraction on these crystals, both with and without cryoprotection and determine the structure of the DNA frame, underscoring the conserved molecular order after coating. We anticipate these mesoporous silica composites for use in applications involving extreme, non-physiological conditions and for experiments which utilize the DNA glass described here as a template for surface science. 

Topology has emerged as a field for describing and controlling order and matter, and thereby the physical properties of materials. There are several largely disparate fields focused on examining and manipulating topology. One of these arenas is in the realm of real space, manipulating systems in terms of their spatial properties, to control the corresponding structural, mechanical, and self- assembling responses. Much of the work in soft matter topology falls within this domain. A second arena is in the domain of momentum or k-space wherein topology controls the features of the electronic band structure of materials, and topologically non-trivial features result in the development of materials with truly unique properties. This work focuses squarely on the realm of condensed matter physics. Here, we review concepts of real- and k-space topology and propose areas for convergence between these two disparate fields. 

The global COVID-19 pandemic has highlighted the need for rapid, reliable, and efficient detection of biological agents and the necessity of tracking changes in genetic material as new SARS-CoV-2 variants emerge. Here we demonstrate that RNA-based, single-molecule conductance experiments can be used to identify specific variants of SARS-CoV-2. To this end, we i) select target sequences of interest for specific variants, ii) utilize single-molecule break junction measurements to obtain conductance histograms for each sequence and its potential mutations, and iii) employ the XGBoost machine learning classifier to rapidly identify the presence of target molecules in solution with a limited number of conductance traces. This approach allows high-specificity and high-sensitivity detection of RNA target sequences less than 20 base pairs in length by utilizing a complementary DNA probe capable of binding to the specific target. We use this approach to directly detect SARS-CoV-2 variants of concerns B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron) and further demonstrate that the specific sequence conductance is sensitive to nucleotide mismatches, thus broadening the identification capabilities of the system. Thus, our experimental methodology detects specific SARS-CoV-2 variants, as well as recognizes the emergence of new variants as they arise. 

Quantum interference effects in single-molecule devices can significantly enhance the thermoelectric properties of these devices. However, single-molecule systems have limited utility for power conversion. In this work, we study the effects of destructive quantum interference in molecular junctions on the thermoelectric properties of hybrid, 2-dimensional molecule–nanoparticle monolayers. We study two isomers of benzenedithiol molecules, with either a para or meta configuration for the thiol groups, as molecular interlinkers between gold nanoparticles in the structure. The asymmetrical structure in the meta configuration significantly improves the Seebeck coefficient and power factor over the para configuration. These results suggest that thermoelectric performance of engineered, nanostructured material can be enhanced by harnessing quantum interference effects in the substituent components. 

Abstract The rational design of molecular electronics remains a grand challenge of materials science. DNA nanotechnology has offered unmatched control over molecular geometry, but direct electronic functionalization is a challenge. Here a generalized method is presented for tuning the local band structure of DNA using transmetalation in metal‐mediated base pairs (mmDNA). A method is developed for time‐resolved X‐ray diffraction using self‐assembling DNA crystals to establish the exchange of Ag⁺ and Hg²⁺ in T:T base pairs driven by pH exchange. Transmetalation is tracked over six reaction phases as crystal pH is changed from pH 8.0 to 11.0, and vice versa. A detailed computational analysis of the electronic configuration and transmission in the ensuing crystal structures is then performed. This findings reveal a high conductance contrast in the lowest unoccupied molecular orbitals (LUMO) as a result of metalation. The ability to exchange single transition metal ions as a result of environmental stimuli heralds a means of modulating the conductance of DNA‐based molecular electronics. In this way, both theoretical and experimental basis are established by which mmDNA can be leveraged to build rewritable memory devices and nanoelectronics. 

Abstract BackgroundThe all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. ResultsThis article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. ConclusionsA monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.