Absolute Counting Method with Multiplexing Capability for Estimating the Number Concentration of Nanoparticles Using Anisotropically Collapsed Gels

Optical Microscopy and Deep Learning for Absolute Quantification of Nanoparticles on a Macroscopic Scale and Estimating Their Number Concentration

We present a simplistic and absolute method for estimating the number concentration of nanoparticles. Macroscopic volumes of a nanoparticle dispersion (several μL) are dropped on a glass surface and the solvent is evaporated. The optical microscope scans the entire surface of the dried droplet (several mm2), micrographs are stitched together (several tens), and all nanoparticles are counted (several thousand per droplet) by using an artificial neural network. We call this method evaporated volume analysis (EVA) because nanoparticles are counted after droplet volume evaporation. As a model, the concentration of ∼60 nm Tm3+-doped photon-upconversion nanoparticles coated in carboxylated silica shells is estimated with a combined relative standard uncertainty of 2.7%. Two reference methods provided comparable concentration values. A wider applicability is tested by imaging ∼80 nm Nile red-doped polystyrene and ∼90 nm silver nanoparticles. Theoretical limits of EVA such as the limit of detection, limit of quantification, and optimal working range are discussed.

Amplification-Free Digital Immunoassay down to the Attomolar Level by Synergistic Sedimentation of Brownian Motion Suppression and Dehydration Transfer

Amplification-free digital immunoassays (DIAs) typically utilize optical nanoparticles to enhance single immunocomplex molecule detection. The efficiency and uniformity of transferring the nanoparticles from a bulk solution to a solid surface determine the limit of detection (LOD) and the accuracy of DIAs. Previous methods suffer from issues like low efficiency, nonuniform distribution, and particle aggregation. Here, we present a novel technique named synergistic sedimentation of Brownian motion suppression and dehydration transfer (SynSed) for nanoparticles using water-soluble polymers. The efficiency of transferring quantum dots (QDs) was increased from 10.7 to 91.4%, and the variation in QD distribution was restricted to 8.8%. By incorporating SynSed into DIAs, we achieved a remarkable reduction in the LOD (down to 3.9 aM) for carcinoembryonic antigen and expanded the dynamic range to cover 3 orders of magnitude in concentration, ranging from 0.01 to 10 fM. DIAs enhanced with SynSed possess ultrahigh sensitivity, advanced accuracy, and specificity, offering a great premise in early disease diagnostics, risk stratification, and treatment response monitoring.

Artificial Intelligence-Aided Massively Parallel Spectroscopy of Freely Diffusing Nanoscale Entities

Massively parallel spectroscopy (MPS) of many single nanoparticles in an aqueous dispersion is reported. As a model system, bioconjugated photon-upconversion nanoparticles (UCNPs) with a near-infrared excitation are prepared. The UCNPs are doped either with Tm3+ (emission 450 and 802 nm) or Er3+ (emission 554 and 660 nm). These UCNPs are conjugated to biotinylated bovine serum albumin (Tm3+-doped) or streptavidin (Er3+-doped). MPS is correlated with an ensemble spectra measurement, and the limit of detection (1.6 fmol L-1) and the linearity range (4.8 fmol L-1 to 40 pmol L-1) for bioconjugated UCNPs are estimated. MPS is used for observing the bioaffinity clustering of bioconjugated UCNPs. This observation is correlated with a native electrophoresis and bioaffinity assay on a microtiter plate. A competitive MPS bioaffinity assay for biotin is developed and characterized with a limit of detection of 6.6 nmol L-1. MPS from complex biological matrices (cell cultivation medium) is performed without increasing background. The compatibility with polydimethylsiloxane microfluidics is proven by recording MPS from a 30 μm deep microfluidic channel.