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This review article centers around structured liquids and their underlying mechanisms. The focus is on the chemistry, applications, characterization, reconfigurability, and future prospects of this class of soft materials.
SARS-CoV-2 poses an ongoing threat to human health as variants continue to emerge. Several effective vaccines are available, but a diminishing number of Americans receive the updated vaccines (only 22% received the 2023 update). Public hesitancy towards vaccines and common occurrence of “breakthrough” infections (i.e., infections of vaccinated individuals) highlight the need for alternative methods to reduce viral transmission. SARS-CoV-2 enters cells by fusing its envelope with the target cell membrane in a process mediated by the viral spike protein, S. The S protein operates via a Class I fusion mechanism in which fusion between the viral envelope and host cell membrane is mediated by structural rearrangements of the S trimer. We previously reported lipopeptides derived from the C-terminal heptad repeat (HRC) domain of SARS-CoV-2 S that potently inhibit fusion by SARS-CoV-2, both in vitro and in vivo. These lipopeptides bear an attached cholesterol unit to anchor them in the membrane. Here, to improve prospects for experimental development and future clinical utility, we employed structure-guided design to incorporate charged residues at specific sites in the peptide to enhance aqueous solubility. This effort resulted in two new, potent lipopeptide inhibitors.
The following sections are included: Introduction, Historical to Present Day, Projecting the Future, Ambitious Goals, Lagging Implementation, Sustainable Urban Energy Transitions, and References.
CCUS technologies are crucial solutions for mitigating climate change by reducing CO2 emissions from industrial operations and energy sectors. This review critically examines the current state of CCUS technologies, and highlights advancements, challenges, regulatory frameworks, and future directions. It comprehensively analyzes carbon capture methods, such as pre-combustion, post-combustion, and oxy-fuel combustion capture, while comparing their efficiencies and limitations. The review also explores carbon utilization techniques, such as direct and indirect utilization, emphasizing their potential applications and technological constraints. Additionally, it assesses various carbon storage methods, focusing on geological, ocean, and mineralization storage, and discusses their capacity, feasibility, and environmental implications. The study reviews the policy and regulatory frameworks, economic viability, market trends, and environmental sustainability of CCUS. By identifying research gaps and recommending future research priorities, this review aims to guide the development of more efficient/effective, and cost-effective CCUS technology, ensuring their role in a sustaining low-carbon future. This review provides a forward-looking perspective, a critical and interdisciplinary analysis that assesses the current state of CCUS technologies, and further provides a roadmap for future development.
Distributed optical fibre sensing (DOFS) is a technology that enables continuous, real-time measurements of a range of environmental parameters along the length of a fibre optic cable. In this article, we review the recently emerged applications of DOFS techniques in physical oceanography and offer a perspective on the technology's potential for future growth within the field. The introduction of DOFS to physical oceanography is contextualised with a brief history of the technology, which spun off primarily from the use of seafloor-laid optical fibres for telecommunications purposes. The key components and underpinning physics of a DOFS system are outlined and, on their basis, the suite of variables that are observable with DOFS are discussed. The implementation factors of DOFS, which include trade-offs between measurement accuracy and spatio-temporal resolutions and ranges, are also examined. The physical oceanographic applications of DOFS to date are then illustrated with case examples of four distinct DOFS techniques: distributed temperature sensing (DTS), which can provide ocean temperature observations; distributed static strain sensing (DSS) and distributed acoustic sensing (DAS), which are sensitive to temperature, cable strain and strain-associated variables, such as pressure and ocean velocity; and ultra-long-range observations of ocean currents with optical interferometry. The forthcoming prospects of DOFS in physical oceanography are considered, and are proposed to include new fibre optic-based approaches to sense ocean salinity and measure through the water column. We conclude with reflections on the future role of DOFS within the Global Ocean Observing System, and highlight the opportunities provided by the existing world-wide network of seafloor-laid optical fibres.
This paper provides a comprehensive review on the application of proppants to maintain fracture permeability over the lifetime of a well based on published observations from experiments and modeling. The review identifies and describes important processes occurring during proppant embedment, during hydraulic fracturing, laboratory testing of fracture conductivity, proppant embedment and modeling of proppant embedment. Lastly, this paper identifies the challenges and knowledge gaps that also provide future avenues of research and opportunities for collaborative technological development which requires an interdisciplinary approach of science, engineering in academia, government, and private sector.
The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino program designed to address fundamental questions in neutrino and astroparticle physics. ProtoDUNE, operating at the CERN Neutrino Platform, serves as a full-scale prototype for the DUNE Far Detector. In particular, the ProtoDUNE Vertical Drift (ProtoDUNE-VD) detector provides a powerful testbed for validating reconstruction and event selection techniques for future DUNE operations. In addition to detector R&D, ProtoDUNE enables a novel parasitic beam-dump search for beyond-the-Standard-Model (BSM) particles. However, it faces several challenges. Most notably, the ProtoDUNE-VD modules operate on the surface and are consequently exposed to an intense flux of cosmic rays, which requires a dedicated trigger. In addition, standard neutrinos are also produced in the T2 target area from the decay of unstable mesons, constituting a relevant background, which needs to be well understood and characterized a priori. We present the first studies based on 2025 data taken with a trigger designed to identify neutrino candidates at ProtoDUNE-VD. ProtoDUNE-VD’s high-resolution LArTPC imaging allows detailed reconstruction of decay and scattering signatures. This work demonstrates the complementarity of traditional tools such as Pandora and modern machine-learning approaches, providing key input for atmospheric neutrino and rare-event searches in the DUNE Vertical Drift program.
NOvA is a two detector, long baseline, neutrino oscillation experiment utilizing the NuMI beamline at Fermilab to study neutrino oscillations through muon (anti)neutrino disappearance and electron (anti)neutrino appearance. NOvA's primary goals are to make determinations of the neutrino mass hierarchy and the octant of ${\theta}_{23}$, and to measure possible CP violation. Along with standard three flavor neutrino oscillations, NOvA data is used to study cross sections in the near detector, neutrino oscillations beyond the three flavor framework such as sterile neutrinos and non-standard interactions, along with other exotic neutrino interactions from astrophysical sources including supernovae. I will provide an overview of the NOvA detectors, share some current results from the experiment, and discuss the future of NOvA operations and analyses.
Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.
The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.
Lithium ion batteries (LIBs) are encouraging electrochemical devices with remarkable properties including a high energy/power density, fast charging capability, and low self-discharge rate. Further increase in energy density as well as safe usage is needed for next-generation LIBs in electric transportation vehicles. Solid-state electrolytes (SSEs) are very promising for high-performance LIBs since they enable improved safety along with increased energy density compared to flammable liquid organic electrolytes. However, utilizing SSEs with a Li metal anode is very challenging due to the possibility of undesired side reactions and the formation of an unstable solid-electrolyte interphase. Therefore, it is critical to enhance the stability of SSEs against the Li anode. One feasible approach is to form a thin and conductive interlayer between the Li anode and solid-state electrolyte. Atomic layer deposition (ALD) is a unique technique for conformal coating of complex 3D structures with finely controlled film thickness (at the atomic scale). ALD coating on the surface of SSEs can be adopted for engineering solid-electrolyte interfaces with desired attributes and improved stability. In this review paper, we have discussed recent progress in implementing the ALD technique for depositing thin layers on various SSE configurations including lithium phosphorus oxynitride (LiPON), garnets, oxides, perovskites, sulphides, Li 3 BO 3 –Li 2 CO 3 (LBCO), and sodium super ionic conductors (NASICON). We have also highlighted the major areas for future research and development in the field. We believe that this review will be very helpful for directing future research on implementing ALD for synthesizing stable and high-performance SSEs with an engineered solid-electrolyte interface for next-generation electrochemical devices (e.g., Li-ion batteries, supercapacitors, and flow batteries).
Colloidally grown nanosized semiconductors yield extremely high-quality optoelectronic materials. Many examples have pointed to near perfect photoluminescence quantum yields, meaning non-radiative optical processes (losses) can be completely suppressed allowing for technology-leading materials as high purity color centers in display technology and emitters in LEDs. Furthermore, because of high chemical yield, and improved understanding of the surfaces, these materials, particularly colloidal quantum dots (QDs) can also be ideal candidates for other optoelectronic applications. Given the strong drive toward carbon neutrality and limiting climate change, electricity from solar photovoltaics will play a large role in the power generation sector. QDs have been developed and shown dramatic improvements over the past 15 years as photoactive materials in photovoltaics and have unique potential with various innovative deposition properties which could lead to exceptionally low-cost and high-performance devices. Once the key issues related to charge transport in optically thick arrays are addressed, QD-based photovoltaic technology could become a better candidate for practical application. In this article, w e show how the possibilities of different deposition techniques could bring QD-based solar cells to the industrial level and discuss the challenges for perovskite QD solar cells in particular, to achieve large-area fabrication for further advancing technology to solve pivotal energy and environmental issues.
The electric power grid is undergoing a significant transformation, shifting from traditional synchronous generators to inverter-based resources (IBRs) such as solar photovoltaics, wind turbines, and energy storage systems. This evolution leads to a reduction in system inertia, a critical attribute for maintaining frequency stability in response to disturbances. Consequently, the ability to monitor and estimate system inertia has become increasingly essential. This paper provides a comprehensive review of existing inertia estimation methodologies, analyzing them from multiple perspectives, including the types of data utilized, underlying estimation principles, operational modes, and system-wide applicability. A comparative summary table is included to distill commonalities and key characteristics across various studies. In addition, the paper examines practical implementations of inertia estimation across several major power systems worldwide, including the U.S. interconnections, the Nordic power system, and the U.K. grid. Key challenges are identified, particularly in estimating contributions from virtual inertia sources and load-induced inertia in increasingly converter-dominated networks. To address these emerging challenges, the paper proposes an integrated framework for real-time inertia estimation and monitoring. This framework encompasses critical components such as data acquisition, inertia estimation from both synchronous and non-synchronous sources, load-induced effects, optimization techniques, forecasting, and virtual inertia scheduling. Collectively, these elements enable dynamic, system-wide monitoring and adaptive control of grid inertia.
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Solid desiccant dehumidification is a promising alternative to vapor compression-based air-dehumidification to reduce energy consumption and improve air quality. Desiccant coated heat exchanger (DCHE), as one type of solid desiccant dehumidification system, can improve system performance and efficiency. The thermal performance and moisture removal capability of the DCHE greatly influence its dehumidification performance.The present work aims to critically analyze the designs, materials, and manufacturing methods of heat exchangers used for solid desiccant coating and look into recent developments in regular heat exchangers, potentially deployed as a substrate for DCHE applications. A comprehensive literature review of publications regarding solid desiccant materials, heat exchangers, manufacturing and coating methods, binder materials, and the performance of heat exchangers has been developed. Alternative heat exchangers are described as better options than fin and tube heat exchangers that have been considered thus far for DCHE applications. Additionally, the heat exchanger's manufacturing process and materials over their thermal and mass exchange performance have been analyzed. Despite the widespread use of fin and tube heat exchangers for solid desiccant coating, recent developments in heat exchanger design, and related fields such as manufacturing methods and materials, open the possibilities for their application in solid desiccant dehumidification systems. Lastly, the authors provide their outlook on possible developments of DCHE technology, aiming to increase systems' energy and dehumidification performance.