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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 (I525nm/I630nm), demonstrating highly suitable properties for ratiometric sensing.

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The rapid progress in the marine industry has resulted in notable challenges related to biofouling and surface corrosion on underwater infrastructure. Conventional coating techniques prioritise individual protective properties, such as offering either antifouling or anticorrosion protection. Current progress and innovations in nanomaterials and technologies have presented novel prospects and possibilities in the domain of integrated multifunctional coatings. These coatings can provide simultaneous protection against fouling and corrosion. This review study focuses on the potential applications of various nanomaterials, such as carbon-based nanostructures, nano-metal oxides, polymers, metal-organic frameworks, and nanoclays, in developing integrated multifunctional nano-based coatings. These emerging integrated multifunctional coating technologies recently developed and are currently in the first phases of development. The potential opportunities and challenges of incorporating nanomaterial-based composites into multifunctional coatings and their future prospects are discussed. This review aims to improve the reader's understanding of the integrated multifunctional nano-material composite coating design and encourage valuable contributions to its development.

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A flexible weight sensor based on optical fibre macrobending loss, using 1550 nm wavelength light and small fibre bending path lengths is presented. An applied load depresses an impactor layer of cylindrical protrusions into a soft mat covered with optical fibre, causing the optical loss of the fibre to increase. An experimental study of two fibre types, two impactor materials, two impactor designs and a range of protrusion bend radii from 3 mm to 10 mm is shown. For weights greater than 2 kg, a linear response in optical loss (dB) is observed for an applied weight load in kg. The proportionality constant between loss and load, and thus the total amount of optical loss for up to 10 kg of weight load, can be tuned by changing the sensor physical parameters, shown here in ranges from 0.5 dB up to 25 dB.

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A robust-accurate estimation of fluid flow is the main building block of a distributed virtual flow meter. Unfortunately, a big leap in algorithm development would be required for this objective to come to fruition, mainly due to the inability of current machine learning algorithms to make predictions outside the training data distribution. To improve predictions outside the training distribution, we explore the continual learning (CL) paradigm for accurately estimating the characteristics of fluid flow in pipelines. A significant challenge facing CL is the concept of catastrophic forgetting. In this paper, we provide a novel approach for how to address the forgetting problem via compressing the distributed sensor data to increase the capacity of the CL memory bank using a compressive learning algorithm. Through extensive experiments, we show that our approach provides around 8% accuracy improvement compared to other CL algorithms when applied to a real-world distributed sensor dataset collected from an oilfield. Noticeable accuracy improvement is also achieved when using our proposed approach with the CL benchmark datasets, achieving state-of-the-art accuracies for the CIFAR-10 dataset on blurry10 and blurry30 settings of 80.83% and 88.91%, respectively.

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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.

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Energy and humanitarian action have long been uneasy bedfellows. In the field, many humanitarian practitioners lack the time or remit to engage with a complex issue such as energy, and the topic to date has received relatively little attention from the private, development and academic sectors. This paper hopes to provide more clarity on energy in forced displacement settings by analysing how energy is interwoven with the humanitarian cluster system. This paper has two aims: (1) to assess existing evidence in the sector and explain the links between energy and each of the humanitarian clusters and (2) to provide recommendations on how humanitarian response efforts can transition from informal action to a comprehensive response on sustainable energy provision. This paper is the first to investigate the role of energy using the cluster system as a framework and contributes to a rapidly evolving field of research and practice on energy in humanitarian contexts. Our analysis demonstrates that energy is not fully integrated within humanitarian programme planning. Further, it highlights pathways for improving humanitarian outcomes enabled by improved energy practices. We identify ten ways clusters can integrate action on energy to support crisis-affected communities.

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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.