Quantitative measurement of cation-mediated adhesion of DNA to anionic surfaces
Single molecule force spectroscopy quantifies divalent-cation-mediated attraction of double-stranded DNA to an anionic self-assembled monolayer.
Hao, Xian↗
Engineering topics
Ye, Tao
Publications and source records attributed to Ye, Tao.
Formation of Linear Plasmonic Heterotrimers Using Nanoparticle Docking to DNA Origami Cages
The fabrication of complex assemblies with interesting collective properties from plasmonic nanoparticles (NPs) is often challenging. While DNA-directed self-assembly has emerged as one of the most promising approaches to forming such complex assemblies, the resulting structures tend to have large variability in gap sizes and shapes, as the DNA strands used to organize these particles are flexible, and the polydispersity of the NPs leads to variability in these critical structural features. Here, we use a new strategy termed docking to DNA origami cages (D-DOC) to organize spherical NPs into a linear heterotrimer with a precisely defined geometrical arrangement. Instead of binding NPs to the exterior of the DNA templates, D-DOC binds the NPs to either the interior or the opening of a 3D cage, which significantly reduces the variability of critical structural features by incorporating multiple diametrically arranged capture strands to tether NPs. Additionally, such a spatial arrangement of the capture strand can work synergistically with shape complementarity to achieve tighter confinement. To assemble NPs via D-DOC, we developed a multistep assembly process that first encapsulates an NP inside a cage and then binds two other NPs to the openings. Microscopic characterization shows low variability in the bond angles and gap sizes. Both UV–vis absorption and surface-enhanced Raman scattering (SERS) measurements showed strong plasmonic coupling that aligned with predictions by electrodynamic simulations, further confirming the precision of the assembly. These results suggest D-DOC could open new opportunities in biomolecular sensing, SERS and fluorescence spectroscopies, and energy harvesting through the self-assembly of NPs into more complex 3D assemblies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Single molecule insights into interfacial molecular recognition for model electrochemical DNA biosensors
Electrochemical sensors that use surface-immobilized DNA to bind analytes and transduce the binding into electrochemical signals, have the potential for rapid, specific, and sensitive detection of bioanalytes via a compact and portable platform. However, accessing the structure of these surfaces/interfaces at the relevant spatial scale (< 10 nm), which determines the interfacial interactions and ultimately sensing performance, remains an unsolved challenge. Here, we review studies that have used high resolution atomic force microscope imaging and spatial statistical analysis tools to understand crowding interactions between thiolated DNA probes immobilized on gold electrodes and how such interactions impact target binding. We also review related studies that attempt to control the nanoscale spatial arrangement of the immobilized recognition elements to optimize sensing performance. Furthermore, these efforts have led to new advances in understanding of the structure-function relationships of DNA-based electrochemical biosensors to move the field toward rational engineering of these biosensing interfaces.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Self‐Assembly of 0D/3D Perovskite Bi‐Layer from a Micro‐Emulsion Ink
Abstract 2D/3D bilayer perovskite synthesized using sequential deposition methods has shown effectiveness in enhancing the stability of perovskite solar devices. However, these approaches present several limitations such as uncontrolled chemical processes, disordered interfacial states, and microscale heterogeneities that can chemically, structurally, and electronically compromise the performance of solar modules. Here, this work demonstrates an emulsion‐based self‐assembly approach using natural lipid biomolecules in a nonionic solution system to form a 0D/3D bilayer structure. The new capping layer is composed of 0D‐entity nanoparticles of perovskite encapsulated by a hydrophobic lipid membrane, analogous to a cell structure, formed through a molecular self‐assembly process. This 0D layer provides a strong water repellent characteristics, optimum interface microstructure, and excellent homogeneity that drives significant enhancement in stability. Solar modules with a large active area of 70 cm 2 fabricated using films comprising of 0D/3D bilayer structure are found to show consistent efficiency of >19% for 2800 h of continuous illumination in the air (60% relative humidity). This emulsion‐based self‐assembly approach is expected to have a transformative impact on the design and development of stable perovskite‐based devices.
0D/3D bilayers↗
Retina-inspired narrowband perovskite sensor array for panchromatic imaging
The retina is the essential part of the human visual system that receives light, converts it to neural signal, and transmits to brain for visual recognition. The red, green, and blue (R/G/B) cone retina cells are natural narrowband photodetectors (PDs) sensitive to R/G/B lights. Connecting with these cone cells, a multilayer neuro-network in the retina provides neuromorphic preprocessing before transmitting to brain. Inspired by this sophistication, we develop the narrowband (NB) imaging sensor combining R/G/B perovskite NB sensor array (mimicking the R/G/B photoreceptors) with a neuromorphic algorithm (mimicking the intermediate neural network) for high-fidelity panchromatic imaging. Compared to commercial sensors, we use perovskite “intrinsic” NB PD to exempt the complex optical filter array. In addition, we use an asymmetric device configuration to collect photocurrent without external bias, enabling a power-free photodetection feature. These results display a promising design for efficient and intelligent panchromatic imaging.
42 ENGINEERING↗
One-Step Ligand-Exchange Method to Produce Quantum Dot–DNA Conjugates for DNA-Directed Self-Assembly
To address the current challenges in making bright, stable, and small DNA-functionalized quantum dots (QDs), we have developed a one-step ligand-exchange method to produce QD–DNA conjugates from commonly available hydrophobic QDs. We show that by systematically adjusting the reaction conditions such as ligand-to-nanoparticle molar ratio, pH, and solvent composition, stable and highly photoluminescent water-soluble QD–DNA conjugates with relatively high ligand loadings can be produced. Moreover, by site specifically binding these QD–DNA conjugates to a DNA origami template, we demonstrate that these bioconjugates have sufficient colloidal stability for DNA-directed self-assembly. Fluorescence quenching by an adjacent gold nanoparticle (AuNP) was demonstrated. Such QD–AuNP dimers may serve as biosensors with improved sensitivity and reproducibility. Furthermore, our simple method can facilitate the assembly of QDs into more complex superlattices and discrete clusters that may enable novel photophysical properties.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Potential-Dependent Adhesion Forces between dsDNA and Electroactive Surfaces
A promising approach to regulating the interactions between polyelectrolytes and materials is the use of electroactive surfaces that can change their charge state. However, common electroactive groups are too unstable to be practical for this purpose. Here we have performed a single molecule force spectroscopy study of the interactions between dsDNA and an 1,1'-biferrocenylene (BFD = bis(fulvalene)diiron)-terminated self-assembled monolayer surface that allows us to reversibly change the charge state. We found that the interaction force between DNA and the surface is correlated to the oxidation state of the BFD groups, which is conveniently controlled by the electrochemical potentials. We discovered that the electroactive SAM produces much stronger interaction forces than its nonelectroactive counterpart. A model based on the Grahame equation was able to quantitatively reproduce the experimentally observed relation between the applied potentials and adhesion forces. Our electroactive surface provides a model system for quantitative studies of the interactions between polyelectrolyte and charged surfaces in liquid. Furthermore, these insights may enable new opportunities for actively manipulating the binding, orientations, and conformations of polyelectrolytes for biosensing, nanomotors, and other applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data
The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitationalwave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Terrestrial noise sources may manifest characteristic disturbances in these auxiliary channels, inducing non-trivial correlations with glitches in the gravitational-wave data. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the MLAs themselves.
gravitational-wave data↗