A significant amount of noteworthy articles reviewing different label-free biosensors are being published in the last years. Most of the times, the comparison among the different biosensors is limited by the procedure used of calculating the limit of detection and the measurement uncertainty. This article clarifies and establishes a simple procedure to determine the calibration function and the uncertainty of the concentration measured at any point of the measuring interval of a generic label-free biosensor. The value of the limit of detection arises naturally from this model as the limit at which uncertainty tends when the concentration tends to zero. The need to provide additional information, such as the measurement interval and its linearity, among others, on the analytical systems and biosensor in addition to the detection limit is pointed out. Finally, the model is applied to curves that are typically obtained in immunoassays and a discussion is made on the application validity of the model and its limitations.

In previous works we demonstrated the benefits of using micro–nano patterning materials to be used as bio-photonic sensing cells (BICELLs), referred as micro–nano photonic structures having immobilized bioreceptors on its surface with the capability of recognizing the molecular binding by optical transduction. Gestrinone/anti-gestrinone and BSA/anti-BSA pairs were proven under different optical configurations to experimentally validate the biosensing capability of these bio-sensitive photonic architectures. Moreover, Three-Dimensional Finite Difference Time Domain (FDTD) models were employed for simulating the optical response of these structures. For this article, we have developed an effective analytical simulation methodology capable of simulating complex biophotonic sensing architectures. This simulation method has been tested and compared with previous experimental results and FDTD models. Moreover, this effective simulation methodology can be used for efficiently design and optimize any structure as BICELL. In particular for this article, six different BICELL's types have been optimized. To carry out this optimization we have considered three figures of merit: optical sensitivity, Q-factor and signal amplitude. The final objective of this paper is not only validating a suitable and efficient optical simulation methodology but also demonstrating the capability of this method for analyzing the performance of a given number of BICELLs for label-free biosensing

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