Bone injuries induced by accidents or bone-related disease have dramatically increased in the past decades. The application of biomaterials has become an inextricable part of treatment for new bone formation and regeneration. Different from traditional bone-regeneration materials, injectable biomaterials—ranging from bioceramics to polymers—have been applied as a means of promoting surgery with a minimal intervention approach. In this review, we summarize the most recent developments in minimally invasive implantable biomaterials for bone reconstruction and different ways to achieve osteogenesis, with a focus on injectable biomaterials for various applications in the orthopedic field. More specifically, bioceramics and polymeric materials, together with their applications in bone fracture healing, vertebral body augmentation, bone implant fixation, bone tumor therapy, and bone-defect-related infection treatment are reviewed in detail. Recent progress in injectable biomaterials with multiple functionalities and bioresponsive properties is also reviewed. Finally, we summarize the challenges in this field and future directions for clinical treatment.
Anterior cruciate ligament (ACL) injuries are frequently caused by sports injuries and trauma. In cases involving complete tears, ACL reconstruction (ACLR) surgery is the only way to restore the ligament’s integrity. When selecting a graft, both the potential complications and the mechanical properties and healing efficacies should be considered. Artificial ligaments have been widely applied in clinical ACLR, and most have exhibited satisfactory biocompatibility and short-term follow-up results. Compared with autografts and allografts, however, the lack of bioactivity of currently available artificial ligaments is a major disadvantage. In addition, some long-term follow-up results have revealed other drawbacks of artificial ligaments, such as graft failure and other complications. Here, we summarize attempts to enhance the bioactive performance of artificial ligaments, as such modifications may have good potential for clinical translation and could improve the long-term outcomes of existing products.
Tissue engineering and regenerative medicine is a new interdisciplinary subject integrating life science, material science, engineering technology, and clinical medicine. Over the last ten years, significant advancements have been achieved in the study of biomaterials and tissue engineering. Progress in the field of tissue engineering and regenerative medicine can result in optimal tissue regeneration and effective functional reconstruction. Spinal cord injury (SCI) is the most severe complication of spinal trauma and frequently results in significant functional impairments in the lower extremities of the affected segment. Repair of SCI is a medical challenge worldwide. Advancements in tissue engineering theory and technology offer fresh opportunities for addressing SCI, as well as providing new strategies and methodologies to tackle the challenges associated with repairing and reconstructing spinal cord function. This article provides an overview of the latest developments in tissue engineering and SCI repair, focusing on biomaterials, cells, and active factors. It also introduces nine key components related to SCI and proposes innovative approaches for repairing and functionally reconstructing the injured spinal cord.
The potential of regenerative medicine in the clinical space is vast, given its ability to repair and replace damaged tissues, restore lost functions due to age or disease, and transform personalized therapy. Traditional regenerative medicine and tissue engineering strategies have created specialized tissues using progenitor cells and various biological stimuli. To date, there are many US Food and Drug Administration (FDA)-approved regenerative medicine therapies, such as those for wound healing and orthopedic injuries. Nonetheless, these therapies face challenges, including off-target effects, a lack of precision, and failure to target the disease or injury at its origin. In search of novel, precise, and efficient alternatives, the regenerative medicine landscape is shifting towards genome engineering technologies, particularly gene editing. Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing systems enable precise knock-ins, knockouts, transcriptional activation and repression, as well as specific base conversions. This advancement has allowed researchers to treat genetic and degenerative diseases, control cell fate for highly regulated tissue repair, and enhance tissue functions. In this review, we explore the progress and future prospects of CRISPR technologies in regenerative medicine, focusing on how gene editing has led to advanced therapeutic applications and served as a versatile research tool for understanding tissue development and disease progression.
Ultrasound-enabled nanomedicine leverages ultrasound to amplify the capabilities of engineered nanosystems, paving the way for innovative diagnostic and therapeutic breakthroughs in conventional nanomedicine. As a burgeoning discipline, past overviews have sometimes offered a fragmented perspective, lacking a comprehensive view. This review presents a systematic exploration of the latest advancements in ultrasound-enabled nanomedicine, with a particular emphasis on oncology. Covered topics include molecular imaging of tumors, separation of tumor markers, penetration through physiological barriers, perforation of cell membranes, targeted drug release and activation strategies, and an array of sonotherapies for oncological treatments. We delve into the research framework of each topic, the foundational design of the nanosystems, and their associated ultrasound activation mechanisms. Moreover, we highlight recent pivotal research aimed at deepening the reader's understanding of this intricate domain. This review underscores the integration of design and foundational theories within ultrasound-enabled nanomedicine, aspiring to ignite advanced theoretical insights and introduce innovative design paradigms. In conclusion, we outline current challenges and prospective research directions. An enhanced focus on these areas will expedite the advancement of ultrasound-enabled nanomedicine.
The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis, cellular analysis, and life science research. Barcode biochip technology, which is integrated with microfluidics, typically comprises barcode array, sample loading, and reaction unit array chips. Here, we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells, including protein biomarkers, microRNA (miRNA), circulating tumor DNA (ctDNA), single-cell secreted proteins, single-cell exosomes, and cell interactions. We begin with an overview of current high-throughput detection and analysis approaches. Following this, we outline recent improvements in microfluidic devices for biomolecule and single-cell detection, highlighting the benefits and limitations of these devices. This paper focuses on the research and development of microfluidic barcode biochips, covering their self-assembly substrate materials and their specific applications with biomolecules and single cells. Looking forward, we explore the prospects and challenges of this technology, with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies, and their large-scale commercialization.
Given its excellent biological properties and the matching of its elastic modulus with that of human bone tissue, medical polyetheretherketone (PEEK) is considered a desirable candidate for bone-implant materials. However, its poor osseointegrative and antibacterial properties greatly limit its clinical application. To address these concerns, a functional PEEK implant is needed. Herein, a novel photo-responsive multifunctional PEEK-based implant material (sPEEK/BP/E7) with both effective osteogenesis and good disinfection properties was constructed via the self-assembly of black phosphorus (BP) nanosheets, mussel-inspired polydopamine (PDA), and bioactive short peptide E7 on sulfonated PEEK (sPEEK). The versatile micro-/nano-structured PEEK surface provides superior hydrophilicity, a favorable osteogenic microenvironment, and excellent photothermal effects under near-infrared (NIR) irradiation. The in vitro results showed that sPEEK/BP/E7 displays enhanced cytocompatibility and osteogenicity in terms of cell adhesion, proliferation, alkaline phosphatase (ALP) activity, matrix mineralization, and osteogenesis-related gene expression, superior to those of the sPEEK and sPEEK/BP samples. In addition to osteogenesis, the multifunctional coating exhibited strong antibacterial activity against both Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Furthermore, it was confirmed in a rat femoral infection model that sPEEK/BP/E7 effectively resisted infection caused by S. aureus under NIR light irradiation and promoted osseointegration in vivo. Thus, this work presents a facile strategy to realize improvement of the “functional integration” of new polymer bone–implant materials and provide new ideas for their clinical application.
Traditional Chinese medicine (TCM) can help prevent or treat diseases; however, there are few studies on the active substances of TCM. For example, Lycium barbarum L. has been proven to be effective in treating osteoporosis for thousands of years, but its active substance remains to be unknown. Prompted by the efforts to modernize TCM, the present study focused on the novel active substance of Lycium barbarum L. to reinforce kidney essence to produce bone marrow. Illumina deep sequencing analysis and stem-loop polymerase chain reaction (PCR) assay revealed that miR162a, a Lycium barbarum L.-derived microRNA, can pass through the gastrointestinal tract to target the bone marrow in mice. Immunofluorescence staining showed that miR162a was absorbed through systemic RNA interference defective transmembrane family member 1 (SIDT1) in the stomach. Bioinformatics prediction and luciferase reporter assay identified that miR162a targeted nuclear receptor corepressor (NcoR). Alizarin red staining and micro-computed tomography (microCT) confirmed that miR162a promoted osteogenic differentiation in bone marrow mesenchymal stem cells, zebrafish, and a mouse model of osteoporosis. In addition, transgenic Nicotiana benthamiana (N. benthamiana) leaves overexpressing miR162a were developed by agrobacterium infiltration method. microCT and tartrate-resistant acid phosphatase staining confirmed that transgenic N. benthamiana leaves effectively protected against osteoporosis in mice. Our study mechanistically explains how Lycium barbarum L. improves osteoporosis and supports that Lycium barbarum L. reinforces kidney essence, thereby strengthening the bone. miR162a expressed by transgenic plants may represent a novel and safe treatment for human osteoporosis.
It has been over a decade since the first coded aperture video compressive sensing (CS) system was reported. The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time. The superimposed image captured in this manner is modulated and compressed, since multiple modulation patterns are imposed. Following this, reconstruction algorithms are utilized to recover the desired high-speed scene. One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video, thereby enabling a low-speed camera to capture high-speed scenes. Inspired by this, a number of variants of video CS systems have been built, mainly using different modulation devices. Meanwhile, in order to obtain high-quality reconstruction videos, many algorithms have been developed, from optimization-based iterative algorithms to deep-learning-based ones. Recently, emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction, highlighting the possibility of deploying video CS in practical applications. Toward this end, this paper reviews the progress that has been achieved in video CS during the past decade. We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications. Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.
Artificial intelligence (AI) has taken breathtaking leaps forward in recent years, evolving into a strategic technology for pioneering the future. The growing demand for computing power—especially in demanding inference tasks, exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing systems. Advances in photonics technology have ignited interest in investigating photonic computing as a promising AI computing modality. Through the profound fusion of AI and photonics technologies, intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical applications. Deep learning, as a subset of AI, presents efficient avenues for optimizing photonic design, developing intelligent optical systems, and performing optical data processing and analysis. Employing AI in photonics can empower applications such as smartphone cameras, biomedical microscopy, and virtual and augmented reality displays. Conversely, leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy consumption. Applying photonics technology in AI computing is expected to have a transformative impact on diverse fields, including optical communications, automatic driving, and astronomical observation. Here, recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics, holography, and quantum photonics. This review also spotlights relevant applications and offers insights into challenges and prospects.
Designing refractory high-entropy alloys (RHEAs) for high-temperature (HT) applications is an outstanding challenge given the vast possible composition space, which contains billions of candidates, and the need to optimize across multiple objectives. Here, we present an approach that accelerates the discovery of RHEA compositions with superior strength and ductility by integrating machine learning (ML), genetic search, cluster analysis, and experimental design. We iteratively synthesize and characterize 24 predicted compositions after six feedback loops. Four compositions show outstanding combinations of HT yield strength and room-temperature (RT) ductility spanning the ranges of 714–1061 MPa and 17.2%–50.0% fracture strain, respectively. We identify an attractive alloy system, ZrNbMoHfTa, particularly the composition Zr0.13Nb0.27Mo0.26Hf0.13Ta0.21, which demonstrates a yield approaching 940 MPa at 1200 °C and favorable RT ductility with 17.2% fracture strain. The high yield strength at 1200 °C exceeds that reported for RHEAs, with 1200 °C exceeding the service temperature limit for nickel (Ni)-based superalloys. Our ML-based approach makes it possible to rapidly optimize multiple properties for materials design, thus overcoming the common problems of limited data and a vast composition space in complex materials systems while satisfying multiple objectives.
Immiscible bimetal systems, of which tungsten–copper (W–Cu) is a typical representative, have crucial applications in fields requiring both mechanical and physical properties. Nevertheless, it is a major challenge to determine how to give full play to the advantages of the two phases of the bimetal and achieve outstanding comprehensive properties. In this study, an ultrafine-grained W–Cu bimetal with spatially connected Cu and specific W islands was fabricated through a designed powder-mixing process and subsequent rapid low-temperature sintering. The prepared bimetal concurrently has a high yield strength, large plastic strain, and high electrical conductivity. The stress distribution and strain response of individual phases in different types of W–Cu bimetals under loading were quantified by means of a simulation. The high yield strength of the reported bimetal results from the microstructure refinement and high contiguity of the grains in the W islands, which enhance the contribution of W to the total plastic deformation of the bimetal. The high electrical conductivity is attributed to the increased mean free path of the Cu and the reduced proportion of phase boundaries due to the specific phase combination of W islands and Cu. This work provides new insight into modulating phase configuration in immiscible metallic composites to achieve high-level multi-objective properties.
Thermal metamaterial represents a groundbreaking approach to control heat conduction, and, as a crucial component, thermal invisibility is of utmost importance for heat management. Despite the flourishing development of thermal invisibility schemes, they still face two limitations in practical applications. First, objects are typically completely enclosed in traditional cloaks, making them difficult to use and unsuitable for objects with heat sources. Second, although some theoretical proposals have been put forth to change the thermal conductivity of materials to achieve dynamic invisibility, their designs are complex and rigid, making them unsuitable for large-scale use in real three-dimensional (3D) spaces. Here, we propose a concept of a thermal dome to achieve 3D invisibility. Our scheme includes an open functional area, greatly enhancing its usability and applicability. It features a reconfigurable structure, constructed with simple isotropic natural materials, making it suitable for dynamic requirements. The performance of our reconfigurable thermal dome has been confirmed through simulations and experiments, consistent with the theory. The introduction of this concept can greatly advance the development of thermal invisibility technology from theory to engineering and provide inspiration for other physical domains, such as direct current electric fields and magnetic fields.
Wastewater treatment significantly contributes to greenhouse gas emissions, which are further exacerbated by the environmental impact of external chemical additions. In response, microbial electrochemical wastewater refining has gained prominence at the interdisciplinary frontier of wastewater resource recovery and green bio-manufacturing. Significant progress has been made in utilizing active electrodes to stimulate CO2 fixation rates, applying “binary electron donors” to produce high-value-added chemicals, and developing novel processes and equipment. This review explores various aspects of microbial electrochemical wastewater refining, including microbial electrochemical monitoring of water quality, chemical synthesis from diverse carbon sources, and the deployment of pilot-scale systems for generating electricity, hydrogen, and methane, as well as for in-situ remediation. Additionally, it discusses the challenges and future directions, highlighting the importance of understanding mechanisms, advancing electrocatalyst and microbial engineering, and innovating hybrid processes. In conclusion, the widespread adoption of microbial electrochemical wastewater refining is emphasized for resource recovery and sustainable chemical production, ultimately reducing environmental impact.
Microbial vanadate (V(V)) reduction is a key process for environmental geochemistry and detoxification of vanadium (V). However, the electron transfer pathways and V isotope fractionation involved in this process are not yet fully understood. In this study, the V(V) reduction mechanisms with concomitant V isotope fractionation by the Gram-positive bacterium Bacillus subtilis (B. subtilis) and the Gram-negative bacterium Thauera humireducens (T. humireducens) were investigated. Both strains could effectively reduce V(V), removing (90.5% ± 1.6%) and (93.0% ± 1.8%) of V(V) respectively from an initial concentration of 50 mg·L−1 during a 10-day incubation period. V(V) was bioreduced to insoluble vanadium (IV), which was distributed both inside and outside the cells. Electron transfer via cytochrome C, nicotinamide adenine dinucleotide, and glutathione played critical roles in V(V) reduction. Metabolomic analysis showed that differentially enriched metabolites (quinone, biotin, and riboflavin) mediated electron transfer in both strains. The aqueous V in the remaining solution became isotopically heavier as V(V) bioreduction proceeded. The obtained V isotope composition dynamics followed a Rayleigh fractionation model, and the isotope enrichment factor (ε) was (–0.54‰ ± 0.04‰) for B. subtilis and (–0.32‰ ± 0.03‰) for T. humireducens, with an insignificant difference. This study provides molecular insights into electron transfer for V(V) bioreduction and reveals V isotope fractionation during this bioprocess, which is helpful for understanding V biogeochemistry and developing novel strategies for V remediation.
Mechanical energy produced by human motion is ubiquitous, continuous, and usually not utilized, making it an attractive target for sustainable electricity-harvesting applications. In this study, flexible magnetic-Juncus effusus (M-JE) fibers were prepared from plant-extracted three-dimensional porous Juncus effusus (JE) fibers decorated with polyurethane and magnetic particles. The M-JE fibers were woven into fabrics and used for mechanical energy harvesting through electromagnetic induction. The M-JE fabric and induction coil, attached to the human wrist and waist, yielded continuous and stable voltage (2 V) and current (3 mA) during swinging. The proposed M-JE fabric energy harvester exhibited good energy harvesting potential and was capable of quickly charging commercial capacitors to power small electronic devices. The proposed M-JE fabric exhibited good mechanical energy harvesting performance, paving the way for the use of natural plant fibers in energy-harvesting fabrics.
This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem (DHHBFSP) designed to minimize the total tardiness and total energy consumption simultaneously, and proposes an improved proximal policy optimization (IPPO) method to make real-time decisions for the DHHBFSP. A multi-objective Markov decision process is modeled for the DHHBFSP, where the reward function is represented by a vector with dynamic weights instead of the common objective-related scalar value. A factory agent (FA) is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality. Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop. A two-stage training strategy is introduced in the IPPO, which learns from both single- and dual-policy data for better data utilization. The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization (PPO), dispatch rules, multi-objective metaheuristics, and multi-agent reinforcement learning methods. Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO, and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
Aeroengines, often regarded as the heart of aircraft, are crucial for flight safety and performance. Comprehensive performance evaluation of aeroengines supports Prognostics and Health Management (PHM) and aeroengine digital engineering. Due to their highly integrated nature, aeroengines present challenges in performance evaluation because their test-run data are high-dimensional, large-scale, and exhibit strong nonlinear correlations among test indicators. To solve this problem, this study proposes a unified framework of the comprehensive performance evaluation of aeroengines to assess performance objectively and globally. Specifically, the network model and the dynamics model of aeroengine performance are constructed driven by test-run data, which can explain the patterns of system state changes and the internal relationship, and depict the system accurately. Based on that, three perturbations in the model are used to simulate three fault modes of aeroengines. Moreover, the comprehensive performance evaluation indexes of aeroengines are proposed to evaluate the performance dynamically from two dimensions, the coupling performance and the activity performance. Thirteen test-run qualified and four test-run failed aeroengines are used to validate and establish the qualified ranges. The results demonstrate that the comprehensive evaluation indexes can distinguish test-run qualified and test-run failed aeroengines. By changing the dynamic parameters, the comprehensive performance under any thrust and inlet guide vanes (IGV) angle can be estimated, broadening the test-run scenarios beyond a few typical states. This novel approach offers significant advancements for the comprehensive performance evaluation and management of aeroengines, paving the way for future PHM and aeroengine digital engineering developments.
Governments worldwide have implemented non-pharmaceutical interventions (NPIs) to control the spread of coronavirus disease 2019 (COVID-19), and it is crucial to accurately assess the effectiveness of such measures. Many studies have quantified the risk of infection transmission and used simulations to compare the risk before and after the implementation of NPIs to judge policies’ effectiveness. However, the choice of metric used to quantify the risk can lead to different conclusions regarding the effectiveness of a policy. In this study, we analyze the correlation between different transmission-risk metrics, pedestrian environments, and types of infectious diseases using simulation-generated data. Our findings reveal conflicting results among five different metric types in specific environments. More specifically, we observe that, when the randomness of pedestrian trajectories in indoor spaces is low, the closeness centrality exhibits a higher correlation coefficient with infection-based metrics than with contact-based metrics. Furthermore, even within the same pedestrian environment, the likelihood of discrepancies between infection-based metrics and other metrics increases for infectious diseases with low transmission rates. These results highlight the variability in the measured effectiveness of NPIs depending on the chosen metric. To evaluate NPIs accurately, facility managers should consider the type of facility and infectious disease and not solely rely on a single metric. This study provides a simulation model as a tool for future research and improves the reliability of pedestrian-simulation-based NPI effectiveness analysis methods.
Vehicle electrification, an important method for reducing carbon emissions from road transport, has been promoted globally. In this study, we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure. Considering the varying travel costs associated with electric and fuel vehicles, we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city. A spatial equilibrium is developed to model the interactions between urban density, vehicle age and vehicle type. An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types, considering vehicle ages and competition for public charging piles. Key findings from our proposed models show that the proportion of electric vehicles (EVs) peaks at over 50% by the end of the first scrappage period, accompanied by more than a 40% increase in commuting distance and time compared to the scenario with only fuel vehicles. Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion. Furthermore, households with EVs tend to be located farther from the city center, and an increase in EV ownership contributes to urban expansion. Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes. It offers a novel perspective on the dynamic interactions between EV adoption and urban development.
Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks, while lowering the industrial parks’ carbon emissions and accommodating diverse load demands from users. However, most optimization research on hybrid energy storage has adopted rule-based passive-control principles, failing to fully leverage the advantages of active energy storage. To address this gap in the literature, this study develops a detailed model for an industrial park energy system with hybrid energy storage (IPES-HES), taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks. An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day. An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm II (NSGA-II). A method using the improved NSGA-II is developed for day-ahead nonlinear scheduling, based on configuration optimization. The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD (5.5%) and 1600.3 kW (43.7%), respectively, compared with an operation strategy based on proportional electricity storage on a typical summer day. Overall, the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.
The promotion of deep decarbonization in the cement industry is crucial for mitigating global climate change, a key component of which is carbon capture, utilization, and storage (CCUS) technology. Despite its importance, there is a lack of empirical assessments of early opportunities for CCUS implementation in the cement sector. In this study, a comprehensive onshore and offshore source–sink matching optimization assessment framework for CCUS retrofitting in the cement industry, called the SSM-Cement framework, is proposed. The framework comprises four main modules: the cement plant suitability screening module, the storage site assessment module, the source–sink matching optimization model module, and the economic assessment module. By applying this framework to China, 919 candidates are initially screened from 1132 existing cement plants. Further, 603 CCUS-ready cement plants are identified, and are found to achieve a cumulative emission reduction of 18.5 Gt CO2 from 2030 to 2060 by meeting the CCUS feasibility conditions for constructing both onshore and offshore CO2 transportation routes. The levelized cost of cement (LCOC) is found to range from 30 to 96 (mean 73) USD·(t cement)−1, while the levelized carbon avoidance cost (LCAC) ranges from −5 to 140 (mean 88) USD·(t CO2)−1. The northeastern and northwestern regions of China are considered priority areas for CCUS implementation, with the LCAC concentrated in the range of 35 to 70 USD·(t CO2)−1. In addition to onshore storage of 15.8 Gt CO2 from 2030 to 2060, offshore storage would contribute 2.7 Gt of decarbonization for coastal cement plants, with comparable LCACs around 90 USD·(t CO2)−1.
With the development of offshore oil and gas resources, hydrates pose a significant challenge to flow assurance. Hydrates can form, accumulate, and settle in pipelines, causing blockages, reducing transport capacity, and leading to significant economic losses and fatalities. As oil and gas exploration moves deeper into the ocean, the issue of hydrate blockages has become more severe. It is essential to take adequate measures promptly to mitigate the hazards of hydrate blockages after they form. However, a prerequisite for effective mitigation is accurately detecting the location and amount of hydrate formation. This article summarizes the temperature–pressure, acoustic, electrical, instrumental–response, and flow characteristics of hydrate formation and blocking under various conditions. It also analyzes the principles, limitations, and applicability of various blockage detection methods, including acoustic, transient, and fiber-optic-based methods. Finally, it lists the results of field experiments and commercially used products. Given their advantages of accuracy and a wide detection range, acoustic pulse reflectometry and transient-based methods are considered effective for detecting hydrate blockages in future underwater pipelines. Using strict backpressure warnings combined with accurate detection via acoustic pulse reflectometry or transient-based methods, efficient and timely diagnosis of hydrate blockages can be achieved. The use of a hydrate model combined with fiber optics could prove to be an effective method for detecting blockages in newly laid pipelines in the future.
