Roadmap on optical sensors

Abstract

Optical sensors and sensing technologies are playing a more and more important role in our modern world. From micro-probes to large devices used in such diverse areas like medical diagnosis, defence, monitoring of industrial and environmental conditions, optics can be used in a variety of ways to achieve compact, low cost, stand-off sensing with extreme sensitivity and selectivity. Actually, the challenges to the design and functioning of an optical sensor for a particular application requires intimate knowledge of the optical, material, and environmental properties that can affect its performance. This roadmap on optical sensors addresses different technologies and application areas. It is constituted by twelve contributions authored by world-leading experts, providing insight into the current state-of-the-art and the challenges their respective fields face. Two articles address the area of optical fibre sensors, encompassing both conventional and specialty optical fibres. Several other articles are dedicated to laser-based sensors, micro- and nano-engineered sensors, whispering-gallery mode and plasmonic sensors. The use of optical sensors in chemical, biological and biomedical areas is discussed in some other papers. Different approaches required to satisfy applications at visible, infrared and THz spectral regions are also discussed.

Keywords: THz sensors; optical fibre sensors; optical sensors; specialty optical fibres.

Figure 1.

Optical fibre sensor opportunities for development.

Figure 2.

Selected applications of specialty fibre sensing for critical and emerging technologies.

Figure 3.

Typical mico- and and nanostructures optical sensors. (a) Schematic of an MNF enabled optical skin. Reproduced from [27]. CC BY 4.0. (b) Schematic of a tilted fibre Bragg grating sensor. Reproduced from [28]. CC BY 4.0. (c) SEM images of fabricated polymer clamped-beam probe on the fibre tip. Reproduced from [29]. CC BY 4.0. (d) SEM image of the fibre sensor, with a nanowire diameter of 800 nm. Scale bar, 1 µm. Reproduced from [30]. CC BY 4.0.

Figure 4.

(a) Illustration of sensing with a WGM microresonator. Light is evanescently coupled into the microresonator from an input waveguide and collected by the same or different output waveguide. The resonant spectrum of light is measured by the optical spectrum analyser. (b)–(d) Spherical, toroidal and bottle (SNAP) microresonators. (e) 3D SNAP microcapillary with a liquid droplet inside. (f) 1D SNAP microcapillary with liquid inside.

Figure 5.

(a) Spatially resolved sensing and manipulation of microfluid components inside a microcapillary. Sensing employs an SMR introduced along the extended length of the microcapillary. (b) The measured spectrogram of such microresonator fabricated in [42]. Reproduced from [42]. CC BY 4.0. (c) Illustration of a bat microresonator. (d) The measured spectrogram of a bat microresonator fabricated in [43]. © [2021] IEEE. Reprinted, with permission, from [43].

Figure 6.

Experimental setup of the plasmon-enhanced optoplasmonic WGM platform. The optoplasmonic WGM sensor is an approx. 100 um diameter glass microsphere with attached gold nanorods. (A) A prism coupler is used to excite WGMs in the glass microsphere. The sample chamber is made in Polydimethylsiloxane (PDMS) and filled with aqueous buffers to detect analytes ranging from bacteria and viruses to activity of the enzyme such as adenosine kinase (ADK). (B) Schematics showing the hot-electron mediated surface catalytic conversion of p-nitrothiophenol (pNTP) to p,p′-dimercaptoazobenzene (DMAB) via p-aminothiophenol (pATP) molecules. (C) Schematic of quantum enhanced sensing using a WGM microsphere. Entangled photon pairs or squeezed light generated in a nonlinear interaction (e.g. using a nonlinear crystal such as KTP) would make possible many different measurement schemes. In all cases, the aim is to suppress the noise level in a WGM measurement, per photon used.

Figure 7.

A few examples of laser-based sensing. Going clockwise from the top, automotive lidar, greenhouse gas and chemical sensing, frequency comb-based sensing, and spectroscopy and quantum.

Figure 8.

Rendering of the Laser Interferometer Space Antenna actively measuring gravitational waves from, for instance, an extreme mass ratio inspiral, by Simon Barke/University of Florida. Reproduced from Max-Planck-Gesellschaft/Albert Einstein Institute. © University of Florida/Simon Barke. CC BY 4.0.

Figure 9.

Two lasers of very different scale: (a) The 48 beamlines comprising the United States National Ignition Facility (‘Seen from above’ by U.S. Department of Energy is licensed under CC0 1.0.) and (b) a typical 3U cubesat (30 cm × 10 cm × 10 cm) into which a laser may be installed. A computer keyboard is visible as a point of reference (‘3U Cubesat with solar panel PCBs mounted’ by AphelionOrbitals is licensed under CC BY 2.0.). U.S. Department of Energy via Rawpixel.com / Image is stated to be in the public domain; Aphelion Orbitals via flickr.com / CC BY 2.0.

Figure 10.

The mid-IR contains fundamental absorption lines for trace gasses. Data from www.spectralcalc.com.

Figure 11.

Lead silicate ARHCF developed at the OptoFab node of the Australian ANFF at the University of Adelaide.

Figure 12.

(a) Schematic diagram of the dual-colour THz SLM. (b) Exploded view of the THz SLM. The resonant structures are on the back of the top quartz substrate, and the pixelated gold patches are on the front of the bottom quartz substrate. The thicknesses of the upper and lower quartz substrate are 300 and 500 μm, respectively, the thickness of the LC layer is 10 μm, and the side length of SLM (w) is 19.7 mm. (c) Simulated reflectance spectra of the SLM for different permittivities of LC (ϵLC). The inset is the unit cell of the MMA with d = 240 μm and b = 173 μm. Reproduced from [96]. CC BY 4.0.

Figure 13.

Aspect of miniature fibre optic Fabry-Perot biomedical pressure sensors Reproduced with permission of Resonetics, (formerly FISO Technologies). © Resonetics (Formerly FISO Technologies).

Figure 14.

(a) Analog read-out: signal increases as the analyte concentration increase. (b), (c) Digital readout: signal is digitized as it is originated from a single molecule. The read-out can be either light intensity (b) or spectrum shift (c).

Figure 15.

(a)–(d) Single molecule array technology [120]. Bead is coated with capture antibodies (a) and antibody-antigen sandwich complex is formed on bead (b). Beads are dispersed in microwell array (c). Fluorescence signals from enzyme reactions (d) are counted. E-g. Single molecule counting technology [121]. Reproduced from [120], with permission from Springer Nature. Antibody-antigen sandwich complex is formed on bead (e), then eluted to the focus spot of confocal imaging (f). Each fluorescent spike is counted (g). (h)–(j). Nanoneedle technology. Nanoneedles (<100 nm) are fabricated in the array format (Scanning Electron Microscope image in (h)). Antibody-antigen sandwich complex induce a spectrum change of each nanoneedle between the pre-image (i) and post-image (j).

Figure 16.

(a) Experimental set-up for hyperspectral PCM. (b) A hyperspectral image of NP probes is generated by recording a set of monochromatic images at defined wavelengths and combining them into a composite image in which each pixel contains a complete spectrum. (c) Field of view containing immobilized gold NPs (diameter ∼ 72 nm), zoom-in of one individual NP, and 2D Gaussian fit. Reprinted with permission from [130]. Copyright (2019) American Chemical Society.

Figure 17.

(a) Principle of detecting receptor (here epidermal growth factor, EGFR) clustering in the plasma membrane using PCM. NP binding in close vicinity due to spatial EGFR clustering induces spectral shifts in the plasmon resonance that can be detected in far field microscopy. (b) Darkfield scattering images of 80 nm gold NP labels before (left) and after (right) binding to an EGFR-expressing cell. (c) Colorimetric analysis of the spectral shift induced by NP binding to EGFR. The left histogram shows the distribution of the ratio, R, of the scattering intensities detected from NP labels at 570 nm and 540 nm before binding to EGFR. The right histogram shows the distribution of the intensity ratios after binding. The increase in R indicates a spectral shift in the NP plasmon due to clustering of EGFR-binding NPs. (b), (c) Reprinted (adapted) with permission from [130]. Copyright (2019) American Chemical Society.

Figure 18.

ADC vs. SqCC classification. (A) TTF-1-stained tissue core diagnosed by pathology as SqCC. (B) SHP prediction image (C) p40 stained tissue core diagnosed by pathology as ADC. (D) SHP prediction image. (Core diameter: ∼1.5 mm) Colour code for SHP prediction: orange: cancer adjacent normal; blue: SqCC; pink: ADC.

Figure 19.

Adeno-squamous (AdSqCC) case study. (A) p40-stained tissue core from patient diagnosed with AdSqCC. (B) SHP prediction image. (C) TTF-1- stained tissue core from same patient, different location. (D) SHP prediction image. Colour codes as in figure 18. (Core diameter: ∼1.5 mm).