Chemical sensing is of great importance in many application fields, such as medicine, environmental monitoring, and industrial process control. Distributed fibre-optic sensing received significant attention because of its unique feature to make spatially resolved measurements along the entire fibre. Distributed chemical sensing (DCS) is the combination of these two techniques and offers potential solutions to real-world applications that require spatially dense chemical measurements covering large length scales. This paper presents a review of the working principles, current status, and the emerging trends within DCS.

Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.

In this paper we introduce Distributed Acoustic Sensing (DAS) and discuss its potential application in Intelligent Transportation Systems (ITS). We present the fundamentals of the technology and data processing methods required for converting the raw data into information that can be used to track the position of vehicles along a road in real time. We present and discuss the results of experiments that explore the dependency of defined DAS signal quality metrics as a function of vehicle speed, size and distance from the fiber, as well as how the cables are installed. The experiments indicate that decisions concerning cable installation could influence data signal to noise by up to $\sim$ 10 dB. Finally, we demonstrate that DAS systems that operate by measuring phase shifts of laser light backscattered along the fiber, are capable of tracking smaller and slower moving traffic than DAS systems that rely only on measuring the intensity of the backscatter.

The phase errors resulting from in-phase/quadrature (I/Q) demodulation in phase-sensitive optical time-domain reflectometry (φOTDR) are investigated theoretically, numerically, and experimentally for two phase recovery schemes: one employing Kramers-Kronig (KK)-based coherent detection, and a second one based on a direct-detection scheme using an imbalanced Mach- Zehnder interferometer, a 3 × 3 coupler and 3 photodetectors. Mathematical models for the estimation of the differential phase variance are proposed and experimentally validated for these two schemes. The probability density function of the phase variance verifies an uneven longitudinal distribution of the phase errors in the two cases. Results point out that KK-based φOTDR sensors have no special advantages in terms of noise performance compared to standard full I/Q coherent detection, so that large phase errors can be verified at fading locations, leading to severe phase unwrapping problems. On the contrary, the direct-detection scheme based on interferometer is highly dependent on the signal difference between photodetectors, showing higher robustness against fading even if its probability density function is wider than the KK-based case. Experimental results also show the advantages of selecting data points with high in-phase and quadrature amplitudes to achieve reliable vibration measurements, especially in KK-based φOTDR sensors.

Ocean acidification has become a major climate change concern requiring continuous observation. Additionally, in the industry, pH surveillance is of great importance. Consequently, there is a pressing demand to develop robust and inexpensive pH sensors. Ratiometric fluorescence pH sensing stands out as a promising concept. The application of carbon dots in fluorescent sensing presents a compelling avenue for the advancement of pH-sensing solutions. This potential is underpinned by the affordability of carbon dots, their straightforward manufacturing process, low toxicity, and minimal susceptibility to photobleaching. Thus, investigating novel carbon dots is essential to identify optimal pH-sensitive candidates. In this study, five carbon dots were synthesized through a simple solvothermal treatment, and their fluorescence was examined as a function of pH within the range of 5–9, across an excitation range of 200–550 nm and an emission range of 250–750 nm. The resulting optical features showed that all five carbon dots exhibited pH sensitivity in both the UV and visible regions. One type of carbon dot, synthesized from m-phenylenediamine, displayed ratiometric properties at four excitation wavelengths, with the best results observed when excited in the visible spectrum at 475 nm. Indeed, these carbon dots exhibited good linearity over pH values of 6–9 in aqueous Carmody buffer solution by calculating the ratio of the green emission band at 525 nm to the orange one at 630 nm (${\mathcal{I}}_{525\mathrm{nm}}$/${\mathcal{I}}_{630\mathrm{nm}}$), demonstrating highly suitable properties for ratiometric sensing.

Surfaces that stay clean when immersed in water are important for an enormous range of applications from ships and buildings to marine, medical and other equipment. Up till now the main strategy for designing self-cleaning surfaces has been to combine hydrophilic/hydrophobic coatings with high aspect ratio structuring (typically micron scale pillars) to trap a (semi-)static water/air layer for drag and adhesion reduction. However, such coating and structuring can distort optical properties; get damaged in harsh environments; and contamination, i.e. particles, oil droplets and biofouling, can get trapped and aggregate in the structure. Here we present a radically different strategy for self-cleaning surface design: We show that a surface can be made self-cleaning by structuring with a pattern of very low aspect ratio pillars (“pancakes”). Now the water is not trapped. It can flow freely around the “pancakes” thus creating a dynamic water layer. We have applied the new pancake design to sapphire windows and made the first surfaces that are self-cleaning through structuring alone without the application of any coating. An offshore installation has now been running continuously with structured windows for more than one year. The previous uptime for unstructured windows was 7 days.