High contrast cleavage detection for enhancing porous silicon sensor sensitivity

Four-wavelength laser interferometry for the demodulation of dual-cavity fiber-optic extrinsic Fabry-Perot interferometric sensors

A four-wavelength passive demodulation algorithm is proposed and experimentally demonstrated for the interrogation of the one cavity in a dual-cavity extrinsic Fabry-Perot interferometric (EFPI) sensor. The lengths of two cavities are adjusted to generate four quadrature signals for each individual cavity. Both simulation and experimental results are presented to validate the performance of this technique. The experimental results demonstrate that dynamic signals at frequencies of 100 Hz, 200 Hz, and 300 Hz with varying amplitude are successfully extracted from a dual-cavity EFPI sensor with initial lengths of 93.4803 µm and 94.0091 µm. The technique shows the potential application to measure dynamic signals in dual-cavity fiber-optic EFPI sensors.

Machine learning assisted BOFDA for simultaneous temperature and strain sensing in a standard optical fiber

We report, to our knowledge for the first time on simultaneous distributed temperature and strain sensing in a standard telecom optical fiber using a machine learning assisted Brillouin frequency domain analysis (BOFDA) system. The well-known temperature and strain cross-sensitivity problem is addressed by developing a BOFDA system with a high signal-to-noise ratio and applying machine learning. The spectrum consists of four highly resolved peaks, whose Brillouin frequency shifts are extracted and serve as features for the machine learning algorithms. The spectra result from a 450-m standard SMF-28 optical fiber, and particularly from a segment of 30 m. This fiber segment is coiled around a stretcher and placed in a climate chamber. The applied temperature and strain values range from 20 °C to 40 °C and from 0 µɛ to 1380 µɛ, respectively. The total measurement time to achieve a high SNR and resolve four peaks with a spatial resolution of 6 m is 16 min. To discriminate temperature and strain effects, simple frequentist and more sophisticated Bayesian-based algorithms are employed with the powerful Gaussian process regression (GPR) delivering the best performance in terms of temperature and strain errors, which are found to be 2 °C and 45 µɛ, respectively. These errors are calculated using leave-one-out cross-validation, so that an unbiased estimation of the sensor's performance is provided.

Biofunctionalization of Multiplexed Silicon Photonic Biosensors

Silicon photonic (SiP) sensors offer a promising platform for robust and low-cost decentralized diagnostics due to their high scalability, low limit of detection, and ability to integrate multiple sensors for multiplexed analyte detection. Their CMOS-compatible fabrication enables chip-scale miniaturization, high scalability, and low-cost mass production. Sensitive, specific detection with silicon photonic sensors is afforded through biofunctionalization of the sensor surface; consequently, this functionalization chemistry is inextricably linked to sensor performance. In this review, we first highlight the biofunctionalization needs for SiP biosensors, including sensitivity, specificity, cost, shelf-stability, and replicability and establish a set of performance criteria. We then benchmark biofunctionalization strategies for SiP biosensors against these criteria, organizing the review around three key aspects: bioreceptor selection, immobilization strategies, and patterning techniques. First, we evaluate bioreceptors, including antibodies, aptamers, nucleic acid probes, molecularly imprinted polymers, peptides, glycans, and lectins. We then compare adsorption, bioaffinity, and covalent chemistries for immobilizing bioreceptors on SiP surfaces. Finally, we compare biopatterning techniques for spatially controlling and multiplexing the biofunctionalization of SiP sensors, including microcontact printing, pin- and pipette-based spotting, microfluidic patterning in channels, inkjet printing, and microfluidic probes.

Botulinum Neurotoxin C Dual Detection through Immunological Recognition and Endopeptidase Activity Using Porous Silicon Interferometers

Botulinum neurotoxins (BoNTs) are the most potent toxins known in nature produced by Clostridium botulinum strains, which can cause life-threatening diseases in both humans and animals. The latter is of serious environmental and economic concern, resulting in high mortality, production losses, and rejection of contaminated animal feed. The available in vivo mouse assay is inadequate for real-time and on-site assessment of outbreaks. Herein, we present a reflective-based approach for the detection of BoNT/C while estimating its activity. Two adjacent porous Si Fabry-Pérot interferometers are simultaneously utilized to quantify minute BoNT/C concentrations by a competitive immunoassay and to assess their endopeptidase activity. The reflectivity signals of each interferometer are amplified by biochemical reaction products infiltration into the scaffold or by peptide fragments detachment from the nanostructure. The optical assay is highly sensitive in compliance with the in vivo approach by presenting a detection limit of 4.24 pg mL^-1. The specificity and selectivity of the designed platform are cross-validated against BoNT/B and BoNT/D, also relevant to animal health. Finally, the analytical performances of both interferometers for real-life scenarios are confirmed using actual toxins while depicting excellent compliance to complex media analysis. Overall, the presented sensing scheme offers an efficient, rapid, and label-free approach for potential biodiagnostic elucidation of botulism outbreaks.

Porous Silicon-Based Aptasensors: Toward Cancer Protein Biomarker Detection

The anterior gradient homologue-2 (AGR2) protein is an attractive biomarker for various types of cancer. In pancreatic cancer, it is secreted to the pancreatic juice by premalignant lesions, which would be an ideal stage for diagnosis. Thus, designing assays for the sensitive detection of AGR2 would be highly valuable for the potential early diagnosis of pancreatic and other types of cancer. Herein, we present a biosensor for label-free AGR2 detection and investigate approaches for enhancing the aptasensor sensitivity by accelerating the target mass transfer rate and reducing the system noise. The biosensor is based on a nanostructured porous silicon thin film that is decorated with anti-AGR2 aptamers, where real-time monitoring of the reflectance changes enables the detection and quantification of AGR2, as well as the study of the diffusion and target-aptamer binding kinetics. The aptasensor is highly selective for AGR2 and can detect the protein in simulated pancreatic juice, where its concentration is outnumbered by orders of magnitude by numerous proteins. The aptasensor's analytical performance is characterized with a linear detection range of 0.05-2 mg mL^-1, an apparent dissociation constant of 21 ± 1 μM, and a limit of detection of 9.2 μg mL^-1 (0.2 μM), which is attributed to mass transfer limitations. To improve the latter, we applied different strategies to increase the diffusion flux to and within the nanostructure, such as the application of isotachophoresis for the preconcentration of AGR2 on the aptasensor, mixing, or integration with microchannels. By combining these approaches with a new signal processing technique that employs Morlet wavelet filtering and phase analysis, we achieve a limit of detection of 15 nM without compromising the biosensor's selectivity and specificity.

Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens

The COVID-19 pandemic has made it abundantly clear that the state-of-the-art biosensors may not be adequate for providing a tool for rapid mass testing and population screening in response to newly emerging pathogens. The main limitations of the conventional techniques are their dependency on virus-specific receptors and reagents that need to be custom-developed for each recently-emerged pathogen, the time required for this development as well as for sample preparation and detection, the need for biological amplification, which can increase false positive outcomes, and the cost and size of the necessary equipment. Thus, new platform technologies that can be readily modified as soon as new pathogens are detected, sequenced, and characterized are needed to enable rapid deployment and mass distribution of biosensors. This need can be addressed by the development of adaptive, multiplexed, and affordable sensing technologies that can avoid the conventional biological amplification step, make use of the optical and/or electrical signal amplification, and shorten both the preliminary development and the point-of-care testing time frames. We provide a comparative review of the existing and emergent photonic biosensing techniques by matching them to the above criteria and capabilities of preventing the spread of the next global pandemic.

High contrast cleavage detection

Photonic biosensors that use optical resonances to amplify signals from refractive index changes offer high sensitivity, real-time readout, and scalable, low-cost fabrication. However, when used with classic affinity assays, they struggle with noise from nonspecific binding and are limited by the low refractive index and small size of target biological molecules. In this Letter, we evaluate the performance of an integrated microring photonic biosensor using the high contrast cleavage detection (HCCD) mechanism, which we recently introduced. The HCCD sensors make use of dramatic optical signal amplification caused by the cleavage of large numbers of high-contrast nanoparticle reporters instead of the adsorption of labeled or unlabeled low-index biological molecules. We evaluate the advantages of the HCCD detection mechanism over conventional target-capture detection techniques with the same label and the same sensor platform, using an example of a silicon ring resonator as an optical transducer decorated with silicon nanoparticles as high-contrast reporters. In the practical realization of this detection scheme, detection specificity and signal amplification can be achieved via collateral nucleic acid cleavage caused by enzymes such as CRISPR Cas12a and Cas13 after binding to a target DNA/RNA sequence in solution.

Inverse design of a single-frequency diffractive biosensor based on the reporter cleavage detection mechanism

Vertically interrogated porous silicon (PSi) interferometric biosensors have shown high potential for sensing bio-molecules as they combine high detection sensitivity with simplicity of fabrication, functionalization, optical coupling, and interfacing with microfluidic systems. However, most interferometric sensor designs require either broadband or wavelength-tunable light sources as well as wide-angle detection schemes, increasing their complexity and cost for point-of-care biosensing applications. The limit of detection of such sensors is also constrained by the small size and low refractive index of biological molecules, making it hard to detect very low concentrations of pathogens. In this work, we use a large-scale computational "inverse design" technique to demonstrate a single-frequency, fixed-angle PSi-based biosensor, which exploits a recently developed high-contrast reporter cleavage detection (HCCD) technique. The HCCD sensors detect high-index reporter cleavage events instead of low-index target analyte capture events as typical for traditional label-free optical biosensors. We use the inverse design approach to discover an optimal configuration of a PSi biosensor that makes use of the extended achievable range of cleavage-induced PSi effective index variations and can be interrogated at a single frequency and at a fixed angle. The optimized design in the form of a one-dimensional PSi grating exhibits the change in the reflectance up to 55 % at the interrogation angle of 12^∘ and wavelength of 600 nm, which is caused by cleavage of Au nanoparticle reporters initially occupying 2% of the sensor surface area. The maximum possible change in reflectance is predicted to be 222 % (for a two-dimensional freeform design not amenable to fabrication). This demonstration may pave the way for developing new or redesigned conventional interferometric and colorimetric point-of-care biosensor systems in combination with the cleavage-based detection schemes.