Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning

Abstract

Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10 −10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.

Plasmonic locator with sub‐diffraction‐limited resolution for continuously accurate positioning

“…[11][12][13][14][15][16][17][18][19][20][21][22] As one of the great advances in plasmonics, plasmon ruler, which is developed to investigate micro/nano interactions through nanoscale distance-dependent plasmon coupling of a homotypic pair of plasmonic nanoparticles, [3][4]23] has become a powerful optical tool for exploring conformational dynamics of a single protein, [24] localized mechanical force transduction, [25] and the interactive biophysics and biochemistry between two components, such as complementary DNA sequence, [8,26] enzyme-substrate, [27] and chemical reaction substrates. [28] Different from the scattering imaging of a single plasmonic nanoprobe, which reveals the ensemble of reaction, [29][30][31][32][33][34][35][36][37][38][39] interaction, [40][41][42][43], and connection processes [44][45] occurred on the surface of the probe, the plasmon ruler enables the recognition of molecular binding [3,24,[26][27] and chemical reaction events [28] at the single-mole...…”

High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning

“…Since it is effective to capture and analyze the scattered information of plasmonic nanoparticles, the DFM imaging technology is highly applied. , The localized surface plasmon resonance (LSPR) properties of plasmonic nanoparticles that are highly sensitive to the surrounding environment , make the plasmonic nanoprobe popular in DFM imaging for biosensing and real-time monitoring with high sensitivity and specificity. − Particularly, the combination of LSPR and DFM technologies can realize high sensitivity at a single nanoparticle level, − making the single nanoparticle DFM scattering imaging widely highly promising in real-time monitoring of the chemical reaction, analysis and detection, and exploration of biological processes. − Even though, two or more metal nanoparticles whose distance is below the diffraction limit cannot be accurately distinguished in DFM imaging, owing to the optical diffraction limit. Many effective efforts including those that start from modifying the scattering resonance properties of plasmonic nanoparticles or improving the instrument and imaging mode have been made, − but they only achieve subdiffraction-limited resolution and require the attachment of complex optical systems, numerical modeling, or the estimation of a point-spread function.…”

The unique physical shading pattern of Rayleigh scattering for the generally improved detection of scattering particles

“…Light scattering, [8][9][10][11][12] developed as a sensitive instrumental analysis method, is involved in diverse applications in inorganics, [13][14][15][16] organics, 17,18 nucleic acids, [19][20][21][22][23][24] biological particles 25 and proteins. [26][27][28][29][30][31] When Huang 21 studied the for-mation of supercoiled assemblies in α,β,γ,δ-tetrakis [4-(trimethylammoniumyl)phenyl]porphine and nucleic acids, he found that there were linear relationships between the enhanced extent of light scattering and the concentrations of nucleic acids within a certain range.…”