First-Principles Density Functional Theory (DFT) Study of Gold Nanorod and Its Interaction with Alkanethiol Ligands

Integration of Electrochemical Sensing and Machine Learning to Detect Tuberculosis via Methyl Nicotinate in Patient Breath

Tuberculosis (TB) remains a significant global health issue; making early, accurate, and inexpensive point-of-care detection critical for effective treatment. This paper presents a clinical demonstration of an electrochemical sensor that detects methyl-nicotinate (MN), a volatile organic biomarker associated with active pulmonary tuberculosis. The sensor was initially tested on a patient cohort comprised of 57 adults in Kampala, Uganda, of whom 42 were microbiologically confirmed TB-positive and 15 TB-negative. The sensor employed a copper(II) liquid metal salt solution with a square wave voltammetry method tailored for MN detection using commercially available screen-printed electrodes. An exploratory machine learning analysis was performed using XGBOOST. Utilizing this approach, the sensor was 78% accurate with 71% sensitivity and 100% specificity. These initial results suggest the sensing methodology is effective in identifying TB from complex breath samples, providing a promising tool for non-invasive and rapid TB detection in clinical settings.

Gold Nanorods: The Most Versatile Plasmonic Nanoparticles

Gold nanorods (NRs), pseudo-one-dimensional rod-shaped nanoparticles (NPs), have become one of the burgeoning materials in the recent years due to their anisotropic shape and adjustable plasmonic properties. With the continuous improvement in synthetic methods, a variety of materials have been attached around Au NRs to achieve unexpected or improved plasmonic properties and explore state-of-the-art technologies. In this review, we comprehensively summarize the latest progress on Au NRs, the most versatile anisotropic plasmonic NPs. We present a representative overview of the advances in the synthetic strategies and outline an extensive catalogue of Au-NR-based heterostructures with tailored architectures and special functionalities. The bottom-up assembly of Au NRs into preprogrammed metastructures is then discussed, as well as the design principles. We also provide a systematic elucidation of the different plasmonic properties associated with the Au-NR-based structures, followed by a discussion of the promising applications of Au NRs in various fields. We finally discuss the future research directions and challenges of Au NRs.

Causal Inference Machine Learning Leads Original Experimental Discovery in CdSe/CdS Core/Shell Nanoparticles

The synthesis of CdSe/CdS core/shell nanoparticles was revisited with the help of a causal inference machine learning framework. The tadpole morphology with 1-2 tails was experimentally discovered. The causal inference model revealed the causality between the oleic acid (OA), octadecylphosphonic acid (ODPA) ligands, and the detailed tail shape of the tadpole morphology. Further, with the identified causality, a neural network was provided to predict and directly lead to the original experimental discovery of new tadpole-shaped structures. An entropy-driven nucleation theory was developed to understand both the ligand and temperature dependent experimental data and the causal inference from the machine learning framework. This work provided a vivid example of how the artificial intelligence technology, including machine learning, could benefit the materials science research for the discovery.

Atomically Precise Nanocluster Assemblies Encapsulating Plasmonic Gold Nanorods

The self-assembled structures of atomically precise, ligand-protected noble metal nanoclusters leading to encapsulation of plasmonic gold nanorods (GNRs) is presented. Unlike highly sophisticated DNA nanotechnology, this strategically simple hydrogen bonding-directed self-assembly of nanoclusters leads to octahedral nanocrystals encapsulating GNRs. Specifically, the p-mercaptobenzoic acid (pMBA)-protected atomically precise silver nanocluster, Na₄ [Ag₄₄ (pMBA)₃₀ ], and pMBA-functionalized GNRs were used. High-resolution transmission and scanning transmission electron tomographic reconstructions suggest that the geometry of the GNR surface is responsible for directing the assembly of silver nanoclusters via H-bonding, leading to octahedral symmetry. The use of water-dispersible gold nanoclusters, Au_≈250 (pMBA)_n and Au₁₀₂ (pMBA)₄₄ , also formed layered shells encapsulating GNRs. Such cluster assemblies on colloidal particles are a new category of precision hybrids with diverse possibilities.

Faceted particles formed by the frustrated packing of anisotropic colloids on curved surfaces

We use computer simulations and simple theoretical models to analyze the morphologies that result when rod-like particles end-attach onto a curved surface, creating a finite-thickness monolayer aligned with the surface normal. This geometry leads to two forms of frustration, one associated with the incompatibility of hexagonal order on surfaces with Gaussian curvature, and the second reflecting the deformation of a layer with finite thickness on a surface with non-zero mean curvature. We show that the latter effect leads to a faceting mechanism. Above threshold values of inter-particle attraction strength and surface mean curvature, the adsorbed layer undergoes a transition from orientational disorder to an ordered state that is demarcated by reproducible patterns of line defects. The number of facets is controlled by the competition between line defect energy and intra-facet strain. Tuning control parameters thus leads to a rich variety of morphologies, including icosahedral particles and irregular polyhedra. In addition to suggesting a new strategy for the synthesis of aspherical particles with tunable symmetries, our results may shed light on recent experiments in which rod-like HIV GAG proteins assemble around nanoscale particles.

Non-covalent adsorption of amino acid analogues on noble-metal nanoparticles: influence of edges and vertices

First-principles calculations on nanoscale-sized noble metal nanoparticles demonstrate that planes, edges and vertices show different noncovalent adsorption propensities depending on the adsorbate functional group.