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We present a techno-economic analysis that evaluates hydrogen technology value chains, incorporating economic, environmental, and societal considerations for 824 supply chain variations.
The BiLateral Operational Storm-Scale Observation and Modeling (BLOSSOM) project was initiated to establish a long-term supersite to improve understanding of cloud physical states and processes as well as to support satellite and climate model programs over the WFF site via a bilateral approach of storm-scale observations and process modeling. This study highlights a noble systematic validation framework of the BLOSSOM ensemble cloud-process simulations through mixed-phase, light-rain, and deep-convective precipitation cases. The framework consists of creating a domain-shifted ensemble of large-scale forcing datasets, and configuring and performing cloud-process simulations with three different bulk microphysics schemes. Validation uses NASA S-band dual-POLarimetric radar (NPOL) observations in the form of statistical composites and skill scores via a polarimetric radar simulator and newly developed CfRad Data tool (CfRAD). While the simulations capture the overall structures of the reflectivity composites, polarimetric signals are still poorly simulated, mainly due to a lack of representation of ice microphysics diversity in shapes, orientation distributions, and their complex mixtures. Despite the limitation, this new ensemble-based validation framework demonstrates that 1) no particular forcing or microphysics scheme outperforms the rest and 2) the skill scores of coarse- and fine-resolution ensemble simulations with different domain-shifted forcing and microphysics schemes are highly correlated with each other with no clear improvement. On the other hand, this suggests that coarse-resolution ensemble simulations are relevant for selecting the best meteorological forcing and microphysics scheme before conducting computationally demanding large eddy simulations (LESs) in support of aircraft and satellite instrument development as well as cloud-precipitation-convection parameterizations.
Agrivoltaic systems are mixed systems of solar photovoltaic (PV) panels and agricultural production, where shade from the panels can result in lower evapotranspiration for crops, which is of particular interest for dryland agriculture. Dry-farmed tomato (Solanum lycopersicum) production in the Willamette Valley of Oregon has lower total yields and higher rates of blossom-end rot (BER) than irrigated tomato production, resulting in reduced marketable yields. To determine how dry-farmed ‘Early Girl’ tomato performed in an agrivoltaics system, a trial was conducted at the Valley Creek Solar Project (Salem, OR, USA) in 2020, using three different amendment treatments and three levels of shading from the panels. Amendment treatments were 0N (receiving no fertilizer), 84N (receiving 84 kg·ha −1 N), and 168N (receiving 168 kg·ha −1 N), applied as processed chicken manure. Plants were estimated to receive an irradiance factor of 30%, 76%, and 89% for full-shade, partial-shade, and full-sun treatments respectively. There was an interaction between amendment treatments and shading treatments in their effects on unblemished yield (yield of fruit without BER or sunscald). The optimum fertilizer application for full-shade and partial-shade rows was 84N, the optimum for full-sun rows was 0N. Fertilizing these rows at these rates resulted in an unblemished yield for the aisle of 11.1 t·ha −1 , which was lower than unblemished yields reported in previous experiments and trials in open fields. However, these results are from a single location and a single year, and other solar sites may behave more similar to open-field conditions. Shading from the panels increased average fruit weight and decreased incidence of BER and sunscald, suggesting that crops were less drought stressed. This resulted in similar unblemished yields for the full-shade and full-sun plots at 84N and 168N. Applying fertilizer resulted in higher total yields, smaller average fruit weight, increased BER incidence, and decreased sunscald incidence. The results suggest a possible synergy between dry-farmed tomato production and agrivoltaics, although several concerns remain, including difficulties managing the vegetation under panels, rules restricting PVs on high-value agricultural soils, and the possibility of soil compaction during PV installation.
Abstract The blossoming of organic solar cells (OSCs) has triggered enormous commercial applications, due to their high‐efficiency, light weight, and flexibility. However, the lab‐to‐manufacturing translation of the praisable performance from lab‐scale devices to industrial‐scale modules is still the Achilles' heel of OSCs. In fact, it is urgent to explore the mechanism of morphological evolution in the bulk heterojunction (BHJ) with different coating/printing methods. Here, a general approach to upscale flexible organic photovoltaics to module scale without obvious efficiency loss is demonstrated. The shear impulse during the coating/printing process is first applied to control the morphology evolution of the BHJ layer for both fullerene and nonfullerene acceptor systems. A quantitative transformation factor of shear impulse between slot‐die printing and spin‐coating is detected. Compelling results of morphological evolution, molecular stacking, and coarse‐grained molecular simulation verify the validity of the impulse translation. Accordingly, the efficiency of flexible devices via slot‐die printing achieves 9.10% for PTB7‐Th:PC 71 BM and 9.77% for PBDB‐T:ITIC based on 1.04 cm 2 . Furthermore, 15 cm 2 flexible modules with effective efficiency up to 7.58% (PTB7‐Th:PC 71 BM) and 8.90% (PBDB‐T:ITIC) are demonstrated with satisfying mechanical flexibility and operating stability. More importantly, this work outlines the shear impulse translation for organic printing electronics.
Carbon is the central element driving the evolution of our human society towards prosperity over several historical stages. As for now, we are in a stage of blossoming sciences and technologies related to carbon materials, as a result of which our ever-growing energy demand has been largely satisfied. Yet, the expected rise of carbon energy consumption and the emerging environmental concerns have prevented us from being optimistic. To build a sufficiently powered future, we have been revolutionizing our ways of carbon energy utilization by discovering and designing new carbon structures, exploring and enhancing their unique physicochemical properties, and pursuing environmentally friendly strategies. Emerging structures such as graphene and sp-bonded C 18 have allowed us to discover carbon’s promising properties such as energy storage and superconductivity, while green energy solutions such as fuel cells and CO 2 reduction are working synergistically to purify the ecospheric carbon cycle. Therefore, this essay timely discusses related carbon sciences and technologies that have been the milestones shaping our energy consumption, based on which our energy future can be envisioned to be green and prosperous.
Access to a blossoming library of colloidal nanomaterials provides building blocks for complex assembled materials. The journey to bring these prospects to fruition stands to benefit from the application of advanced processing methods. Epitaxially connected nanocrystal (or quantum dot) superlattices present a captivating model system for mesocrystals with intriguing emergent properties. The conventional processing approach to creating these materials involves assembling and attaching the constituent nanocrystals at the interface between two immiscible fluids. Processing small liquid volumes of the colloidal nanocrystal solution involves several complexities arising from the concurrent spreading, evaporation, assembly, and attachment. The ability of inkjet printers to deliver small (typically picoliter) liquid volumes with precise positioning is attractive to advance fundamental insights into the processing science, and thereby potentially enable new routes to incorporate the epitaxially connected superlattices into technology platforms. In this study, we identified the processing window of opportunity, including nanocrystal ink formulation and printing approach to enable delivery of colloidal nanocrystals from an inkjet nozzle onto the surface of a sessile droplet of the immiscible subphase. We demonstrate how inkjet printing can be scaled-down to enable the fabrication of epitaxially connected superlattices on patterned sub-millimeter droplets. Furthermore, we anticipate that insights from this work will spur on future advances to enable more mechanistic insights into the assembly processes and new avenues to create high-fidelity superlattices.
Cation and anion recognition have both played central roles in the development of supramolecular chemistry. Additionally, much of the associated research has focused on the development of receptors for individual cations or anions, as well as their applications in different areas. Rarely is complexation of the counterions considered. In contrast, ion pair recognition chemistry, emerging from cation and anion coordination chemistry, is a specific research field where co-complexation of both anions and cations, so-called ion pairs, is the center of focus. Systems used for the purpose, known as ion pair receptors, are typically di- or polytopic hosts that contain recognition sites for both cations and anions and which permit the concurrent binding of multiple ions. The field of ion pair recognition has blossomed during the past decades. Several smaller reviews on the topic were published roughly 5 years ago. They provided a summary of synthetic progress and detailed the various limiting ion recognition modes displayed by both acyclic and macrocyclic ion pair receptors known at the time. The present review is designed to provide a comprehensive and up-to-date overview of the chemistry of macrocycle-based ion pair receptors. Finally, we specifically focus on the relationship between structure and ion pair recognition, as well as applications of ion pair receptors in sensor development, cation and anion extraction, ion transport, and logic gate construction.
Current management of fire blight, caused by Erwinia amylovora , relies heavily on streptomycin a practice that has contributed to the emergence of antibiotic-resistant strains and raised environmental and regulatory concerns. Enzyme-based biocontrol agents offer a promising antibiotic-free approach that combines target specificity with environmental compatibility. This study evaluates CAase, a bacteriophage-derived glycosyl hydrolase, for its ability to disrupt E. amylovora biofilms and reduce disease severity. Biochemical assays and scanning electron microscopy confirmed that CAase efficiently degraded the extracellular polysaccharide (EPS) matrix, releasing cells from biofilms. Gas chromatography–mass spectrometry (GC–MS) linkage analysis of EPS isolated from two Erwinia amylovora strains demonstrated that CAase preferentially cleaves galactan-rich amylovoran produced by strain EA273, while exhibiting only limited activity toward the levan-rich EPS from strain EA1430. Functional assays revealed that CAase reduced bacterial viability by nearly 2 orders of magnitude at higher enzyme concentrations, strongly suppressed surface motility, and induced ultrastructural damage visible by transmission electron microscopy. Importantly, field trials showed that CAase significantly lowered blossom and shoot blight incidence under orchard conditions. These results highlight CAase as a potent enzyme-based strategy for reducing the virulence of E. amylovora and demonstrate its potential as a sustainable alternative to antibiotics in fire blight management.
Magnetism in two-dimensional (2D) van der Waals (vdW) materials has recently emerged as one of the most promising areas in condensed matter research, with many exciting emerging properties and significant potential for applications ranging from topological magnonics to low-power spintronics, quantum computing, and optical communications. In the brief time after their discovery, 2D magnets have blossomed into a rich area for investigation, where fundamental concepts in magnetism are challenged by the behavior of spins that can develop at the single layer limit. However, much effort is still needed in multiple fronts before 2D magnets can be routinely used for practical implementations. In this comprehensive review, prominent authors with expertise in complementary fields of 2D magnetism (i.e., synthesis, device engineering, magneto-optics, imaging, transport, mechanics, spin excitations, and theory and simulations) have joined together to provide a genome of current knowledge and a guideline for future developments in 2D magnetic materials research.
The classical way of studying the rainfall‐runoff processes in the water cycle relies on conceptual or physically‐based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in the hydrology community for rainfall‐runoff simulations. However, the decades‐old Long Short‐Term Memory (LSTM) network remains the benchmark for this task, outperforming newer architectures like Transformers. In this work, we propose a State Space Model (SSM), specifically the Frequency Tuned Diagonal State Space Sequence (S4D‐FT) model, for rainfall‐runoff simulations. The proposed S4D‐FT is benchmarked against the established LSTM and a physically‐based Sacramento Soil Moisture Accounting model under in‐sample and out‐of‐sample simulation setups across 531 watersheds in the contiguous United States (CONUS). Results show that S4D‐FT is able to outperform the LSTM model across diverse regions under both simulation setups, especially for regions that feature snowmelt‐driven or intermittent flow regimes. In contrast, S4D‐FT tends to underperform in flashier, high‐magnitude flow regimes, likely due to its global state‐space convolution computation that emphasizes slow, storage‐driven dynamics, which makes it less effective at picking up short bursts and noisy spikes in the data. In summary, our pioneering introduction of the S4D‐FT for rainfall‐runoff simulations challenges the dominance of LSTM in the hydrology community and expands the arsenal of DL tools available for hydrological modeling.
We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.
Biocatalysis, the application of enzymes to solve synthetic problems of human import, has blossomed into a powerful technology for chemical innovation. In the past decade, a threefold partnership, where nature provides blueprints for enzymatic catalysis, chemists introduce innovative activity modes with abiological substrates, and protein engineers develop new tools and algorithms to tune and improve enzymatic function, has unveiled the frontier of new-to-nature enzyme catalysis. In this Perspective, we highlight examples of interdisciplinary studies, which have helped to expand the scope of biocatalysis, including concepts of enzymatic versatility explored through the lens of biomimicry, to achieve activities and selectivities beyond those currently possible with chemocatalysis. We indicate how modern tools, such as directed evolution, computational protein design and machine learning-based protein engineering methods, have already impacted and will continue to influence enzyme engineering for new abiological transformations. As a result, a sustained collaborative effort across disciplines is anticipated to spur further advances in biocatalysis in the coming years.
In the past two decades, ionic liquids (ILs) have blossomed as versatile task-specific materials with a unique combination of properties, which can be beneficial for a plethora of different applications. The additional need of incorporating ILs into solid devices led to the development of a new class of ionic soft-solid materials, named here iongels. Nowadays, iongels cover a wide range of materials mostly composed of an IL component immobilized within different matrices such as polymers, inorganic networks, biopolymers or inorganic nanoparticles. Here this review aims at presenting an integrated perspective on the recent progress and advances in this emerging type of material. We provide an analysis of the main families of iongels and highlight the emerging types of these ionic soft materials offering additional properties, such as thermoresponsiveness, self-healing, mixed ionic/electronic properties, and (photo)luminescence, among others. Next, recent trends in additive manufacturing (3D printing) of iongels are presented. Finally, their new applications in the areas of energy, gas separation and (bio)electronics are detailed and discussed in terms of performance, underpinning it to the structural features and processing of iongel materials.
Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an "on-demand" mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors' experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.
With ground-based interferometers detecting hundreds of gravitational-wave (GW) events, GW astronomy has continued to blossom. U.S. and European scientists are developing plans to construct third-generation ground-based interferometers. ESA has proceeded through the mission formulation phase of space-based interferometer in a lower-frequency band, 10 –4 –0.1 Hz. Despite all these exciting developments, there is still a missing frequency band, 0.1–10 Hz. This mid-frequency band is rich with interesting astrophysical events. Coalescence and merger of intermediate-mass black holes (IMBHs) will occur in this frequency band. Coalescing stellar-mass BHs will pass through this frequency band days before they reach the frequency band of LIGO and Virgo. Detection of such signals would enable a mid-frequency detector to issue an advance notice to the high-frequency GW detectors, as well as to optical, x-ray and γ-ray telescopes. We propose Lunar Accelerometer Network Gravitational Observatory (LANGO) to detect GWs in this frequency band. In the first phase (LANGO 1), we propose to deploy four ambient-temperature (250 K) accelerometers in the tetrahedral or in a square configuration on the hemisphere facing the Earth. After successful operation at 250 K, LANGO would be upgraded to a cryogenic (4 K) version with over two orders of magnitude increased sensitivity (LANGO 2), with coherent rejection of seismic noise implemented. LANGO is a full-tensor detector, capable of determining the source direction and wave polarization. Each test mass (TM) is suspended as a pendulum with resonance frequency ∼ 0.01 Hz, thus is only weakly coupled to the lunar surface horizontally. LANGO is designed to detect the relative motion of globally separated, nearly free, TMs by using the Moon as a large quiet platform. LANGO 1 and 2 accelerometers aim at sensitivities ⩽ 10 –11 m s –2 Hz –1/2 and ⩽ 10 –13 m s –2 Hz –1/2 in the horizontal axes over the frequency band of 1 mHz–10 Hz, which yield GW sensitivities 1.1 x 10 -21 Hz -1/2 and 3.8 x 10 -24 Hz -1/2 at 1 Hz, respectively. The LANGO accelerometers will be 10 3 –10 5 times more sensitive than Apollo seismometers. With such sensitivity, LANGO will also make great contribution to the advancement of lunar geophysics.
The early 1990s saw the first useful application of pulsed neutron spectroscopy to the study of excitations in low dimensional magnetic systems, with Roger Cowley as a key participant in important early experiments. Since that time the technique has blossomed as a powerful tool utilizing vastly improved neutron instrumentation coupled with more powerful pulsed sources. In this work, we review representative experiments illustrating some of the fascinating physics that has been revealed in quasi-one and two dimensional systems.
Abstract Eleven years (by publication) years after the development and application of the control banding (CB) Nanotool for the qualitative assessment and control of engineered nanoparticles (ENP), there remains no quantitative gold standard to serve as an alternative to the qualitative assessment. Many CB models have been developed during the years subsequent to the initial development of the CB Nanotool and the literature continues to blossom with comparisons and applications of these various tools; however, these developments have hitherto been made in the absence of validating and verifying their effectiveness using existing, albeit limited, quantitative methods. This paper reviews the existing literature on the CB Nanotool to evaluate its effectiveness in a variety of settings and presents a summary of qualitative and quantitative information from its application in a broad range of ENP handling activities performed in two different research institutions. A total of 28 ENP activities were assessed using the CB Nanotool (Version 2.0). Due to the lack of guidance on a single exposure assessment methodology, a combination of real-time monitoring, filter analysis, and microscopic analysis was used to assess various quantitative metrics, including mass concentration, particle number concentration, and particle speciation. All the results indicated that the control outcomes from the CB Nanotool qualitative assessment were sufficient to prevent workers from being exposed to ENP at levels beyond established exposure limits or background levels. These data represent an independent quantitative validation of CB Nanotool risk level outcomes and give further credence to the use of the CB Nanotool to effectively control worker exposures in the absence of quantitative air monitoring results.