Sensitive detection of engineered nanoparticles (NPs) in air and in liquid samples is an important task and still a major challenge in analytical chemistry. Recent work demonstrated that it can be performed using surface plasmon microscopy (SPM) where binding of single NPs to a surface leads to the formation of characteristic patterns in differential SPM images. However, these patterns have to be discriminated from a noisy background. Computer-assisted recognition of nanoparticles offers a solution but requires the development of respective tools for data analysis. Hereby a numerical method for automated detection and characterization of images of single adsorbing NPs in SPM image sequences is presented. The detection accuracy of the method was validated using computer generated images and manual counting. The method was applied for detecting and imaging of gold and silver NPs adsorbing from aqueous dispersions and for soot and NaCl NPs adsorbing from aerosols. The determined adsorption rate was in range 0.1–40 NPs per (s mm2) and linearly dependent on the concentration of nanoparticles. Depending on the type of NPs and signal to noise ratio, a probability of recognition of 90–95 % can be achieved.
The analytical performance of surface plasmon resonance imaging with charge coupled device detection can be improved significantly by splitting a macroscopic sensing surface into multiple microscopic neighboring sensing and referencing subareas. It is shown that such a multiple referencing reduces intensity fluctuations across the total sensing area and, therefore, improves the signal/noise (S/N)ratio proportional to the splitting factor. The approach is demonstrated by detection of biotin binding to a monolayer of streptavidin. An effective variation of the reflected intensity of about 10⁻⁴, which corresponds to the refraction index variation of 3 x 10⁻⁶, was detected with a S/N ratio about 10 without any temperature stabilization of the sensing area.
The detection limit of surface plasmon resonance (SPR) measurements has been improved by a factor of approximately 2-3.5 if the angle of incidence was near to the reflection minimum of the SPR resonance curve instead at the position of the steepest slope, the standard alignment in SPR imaging. The enhancement of the detection power, a result of signal-to-noise optimization, is demonstrated by applying a photodiode and a CCD camera for SPR detection. The experimental data are compared with data expected from theory.
A new technique for on-line monitoring of analyte binding to sensor surfaces by surface plasmon resonance (SPR) detection is described. It is based on differential measurements using two wavelengths provided by two diode lasers. The technique is as simple and robust as the conventional SPR detection measuring the reflected radiation at fixed incidence angle, but it has the advantage of being nonsensitive to variations of the resonance width and providing essentially higher signal/noise ratios. The paper presents the first four channel prototype system for parallel 2D-monitoring at four different spots. One channel is always used as a reference to compensate temperature fluctuations and nonspecific adsorptions. Calibration with sucrose solutions revealed an absolute sensitivity of Δn ∼ 5 × 10-6. The new technique is tested with a biotin−streptavidin binding and with hybridization/denaturation of DNA. Biotin binding to a streptavidin monolayer is detected with a signal/noise ratio of about 5, which demonstrates the high potential of the new technique for applications in drug discovery. Applications to gene analysis are tested with short oligonucleotides of the sequences used for genotyping human hepatitis C viruses. A selective response to complementary oligonucleotides is observed. The high reproducibility in subsequent cycles of hybridization/denaturation (by formamide or by heating) points out potential applications of the technique in medical diagnostics, food industry, genomics, and proteomics too.