Over the past few decades, optical fibers have been widely deployed to implement various applications in high-speed long-distance telecommunication, optical imaging, ultrafast lasers, and optical sensors. Distributed optical fiber sensors characterized by spatially resolved measurements along a single continuous strand of optical fiber have undergone significant improvements in underlying technologies and application scenarios, representing the highest state of the art in optical sensing. This work is focused on a review of three types of distributed optical fiber sensors which are based on Rayleigh, Brillouin, and Raman scattering, and use various demodulation schemes, including optical time-domain reflectometry, optical frequency-domain reflectometry, and related schemes. Recent developments of various distributed optical fiber sensors to provide simultaneous measurements of multiple parameters are analyzed based on their sensing performance, revealing an inherent trade-off between performance parameters such as sensing range, spatial resolution, and sensing resolution. This review highlights the latest progress in distributed optical fiber sensors with an emphasis on energy applications such as energy infrastructure monitoring, power generation system monitoring, oil and gas pipeline monitoring, and geothermal process monitoring. This review aims to clarify challenges and limitations of distributed optical fiber sensors with the goal of providing a pathway to push the limits in distributed optical fiber sensing for practical applications.

Soft phonon modes in strongly anharmonic crystals are often neglected in calculations of phonon-related properties. Herein, we experimentally measure the temperature effects on the band gap of cubic SrTiO 3 , and compare with first-principles calculations by accounting for electron−phonon coupling using harmonic and anharmonic phonon modes. The harmonic phonon modes show an increase in the band gap with temperature using either Allen−Heine−Cardona theory or finite-displacement approach, and with semilocal or hybrid exchange-correlation functionals. This finding is in contrast with experimental results that show a decrease in the band gap with temperature. We show that the disagreement can be rectified by using anharmonic phonon modes that modify the contributions not only from the significantly corrected soft modes, but also from the modes that show little correction in frequencies. Our results confirm the importance of soft-phonon modes that are often neglected in the computation of phonon-related properties and particularly in electron−phonon coupling.

To gain additional insight into high-temperature functional material properties for applications in optical gas sensing, the temperature effects on the band gap and optical properties of rutile TiO2 are investigated using ab initio methods. By analyzing the contributions from electron–phonon interaction and lattice thermal expansion, we show that the electron–phonon interaction is the dominant factor for temperature band-gap renormalization. As the temperature increases, the band gap increases until 300 K and then narrows above 300 K. This behavior results from the acoustic phonons, which widen the band gap, dominate below 300 K, while the optical phonons, which narrow the band gap, dominate above 300 K. Our study suggests that the band gap is narrowed by about 138 meV at 1000 K. We also investigated the temperature effects on the dielectric constants, the refractive index, as well as the extinction coefficient. Both the rate of decrease of the refractive index at 650 and 800 nm as well as the experimentally derived band gap agree with experimentally measured data as the temperature increases. Our results and computational methods are of interest for developing high-temperature functional materials with applications toward gas sensing.

The potential to use single-crystal sapphire optical fiber as an alternative to silica optical fibers for sensing in high-temperature, high-pressure, and chemically aggressive harsh environments has been recognized for several decades. A key technological barrier to the widespread deployment of harsh environment sensors constructed with sapphire optical fibers has been the lack of an optical cladding that is durable under these conditions. However, researchers have not yet succeeded in incorporating a high-temperature cladding process into the typical fabrication process for single-crystal sapphire fibers, which generally involves seed-initiated fiber growth from the molten oxide state. While a number of advances in fabrication of a cladding after fiber-growth have been made over the last four decades, none have successfully transitioned to a commercial manufacturing process. This paper reviews the various strategies and techniques for fabricating an optically clad sapphire fiber which have been proposed and explored in published research. The limitations of current approaches and future prospects for sapphire fiber cladding are discussed, including fabrication methods and materials. The aim is to provide an understanding of the past research into optical cladding of sapphire fibers and to assess possible material systems for future research on this challenging problem for harsh environment sensors.

ABO3−δ (A = La, Sr, B = Fe, Co) perovskites are useful in a wide range of applications, including their recent exploration for application in high-temperature optical oxygen sensing for energy conversion devices such as solid oxide fuel cells.

Concerns about climate change have encouraged significant interest in concepts for zero-emission power generation systems. These systems are intended to produce power without releasing CO2 into the atmosphere. One method to achieve this goal is to produce hydrogen from the gasification of fossil or biomass fuels. Using various membrane and reforming technologies, the carbon in the parent fuel can be shifted to CO2 and removed from the fuel stream, followed by direct CO2 sequestration. The hydrogen fuel can be used directly in gas turbines fitted with low-NO x combustors. A second approach to producing zero-emission power is to replace the nitrogen diluent that accompanies conventional combustion in air with either CO2 or H2O. In this concept, CO2 or H2O is added to oxygen to control combustion temperatures in oxygen-fuel reactions. In the absence of nitrogen, the primary combustion products for any hydrocarbon under lean conditions are then simply CO2 and H2O. Thus, merely cooling the exhaust stream condenses the water and produces an exhaust of pure CO2, ready for sequestration. The dilute oxy-fuel combustion strategy can be incorporated in power cycles that are similar to Brayton or Rankine configurations, using CO2 or H2O as the primary diluent respectively. While the relative merits of the various strategies to zero-emission power are the subject of various technical and economic studies, very little work has focused on defining the combustion issues associated with the dilute oxy-fuel option. In this paper, the expected combustion performance of CO2 and H2O diluted systems are compared. Experimental results from a high-pressure oxy-fuel combustor are also presented.

The latest digital revolution involves the rise of smart devices composed of sensor hardware and artificial intelligence (AI) software for performing intelligent tasks. Smart sensors have become ubiquitous in our lives with varied applications ranging from voice-enabled home devices (Google Home, Alexa, etc.) to the Industrial Internet of Things (IIoT). This revolution has been fueled by 1) miniaturization of sensing hardware, 2) easy access to cloud and high-performance computing, 3) development of big data storage and analytics technologies, and 4) the latest breakthroughs in machine learning (ML) and AI technologies. The emergence of AI since 2012 and its major breakthroughs can be attributed to the research and development (R&D) in deep learning, a subfield of ML that uses biologically inspired neural networks to perform learning tasks. [1] The performance of conventional ML algorithms depends on the individual selection of specific features, while deep neural networks (DNN) automatically generate features as part of the learning process. Deep learningbased AI technologies are increasingly showing performance

On its revolutionary threshold, quantum sensing is creating potentially transformative opportunities to exploit intricate quantum mechanical phenomena in new ways to make ultrasensitive measurements of multiple parameters. Concurrently, growing interest in quantum sensing has created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. Safe and secure utilization of energy is dependent upon addressing challenges related to material stability and function, secure monitoring of infrastructure, and accuracy in detection and measurement. A summary of well‐established and emerging quantum sensing materials and techniques, as well as the corresponding sensing platforms that have been developed for their deployment is provided here. Specifically, the enhancement of existing advanced sensing technologies with quantum methods and materials is focused on, enabling the realization of an unprecedented level of sensitivity, placing an emphasis on relevance to the energy industry. The review concludes with a discussion on high‐value applications of quantum sensing to the energy sector, as well as remaining barriers to sensor deployment.