Common-path interferometric label-free protein sensing with resonant dielectric nanostructures

Research toward photonic biosensors for point-of-care applications and personalized medicine is driven by the need for high-sensitivity, low-cost, and reliable technology. Among the most sensitive modalities, interferometry offers particularly high performance, but typically lacks the required operational simplicity and robustness. Here, we introduce a common-path interferometric sensor based on guided-mode resonances to combine high performance with inherent stability. The sensor exploits the simultaneous excitation of two orthogonally polarized modes, and detects the relative phase change caused by biomolecular binding on the sensor surface. The wide dynamic range of the sensor, which is essential for fabrication and angle tolerance, as well as versatility, is controlled by integrating multiple, tuned structures in the field of view. This approach circumvents the trade-off between sensitivity and dynamic range, typical of other phase-sensitive modalities, without increasing complexity. Our sensor enables the challenging label-free detection of procalcitonin, a small protein (13 kDa) and biomarker for infection, at the clinically relevant concentration of 1 pg mL−1, with a signal-to-noise ratio of 35. This result indicates the utility for an exemplary application in antibiotic guidance, and opens possibilities for detecting further clinically or environmentally relevant small molecules with an intrinsically simple and robust sensing modality

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables