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At least 235 records · Page 13

Land-cover Change in the Caucasus Mountains Since 1987 Based on the Topographic Correction of Multi-temporal Landsat Composites

Mountainous regions are changing rapidly across the world due to both land-use change and climate change. Given the importance of mountainous regions for ecosystem services and endemic biodiversity, monitoring these changes is essential. Satellite data provide a great resource to map land-cover change in mountainous regions, however mapping is especially challenging there because topographic complexity affects reflectance. The so called ‘topographic effect’ has been successfully corrected for in case studies of small areas, but a comparison of large-area classifications and land-cover change analyses with and without topographic correction is missing. Here, we performed a long-term land-cover change assessment for a large mountainous region, i.e., the Caucasus Mountains with topographic correction. Our two goals were 1) to examine the effect of topographic correction on land-cover classification for a large mountainous region, and 2) to assess land-cover changes since 1987 across the Caucasus based on the full Landsat archive. Both the complex topography and the history of land-use changes, especially after the collapse of the Soviet Union in 1991, make the Caucasus Mountains an ideal study area to understand topographic effects on large-area land-cover mapping for the last three decades. First, we compared a non-topographically-corrected Landsat classification for 2015 with a classification that was topographically-corrected with an enhanced C-correction for the same year and assessed the accuracy of both. Second, we derived topographically-corrected Landsat classifications for six dates to assess changes in cropland and forest from 1987 to 2015, based on class probabilities and post-classification comparisons. In regard to our first goal, topographic correction improved the overall accuracy of the classification only by 2% (from 79 to 81%), but disagreement rates were as high as 100% in mountainous regions, especially among forest types. In regard to our second goal, we found that cropland loss was the most prevalent change process since 1987. Cropland loss was particularly widespread in Georgia and Armenia until 2000, and in Azerbaijan until 2005. The North Caucasus (the Russian Federation) had more stable cropland over time, most likely due to different land reforms after the collapse of the Soviet Union, and the prevalence of flat landscapes and very fertile soils, which make cultivation easier than in the South Caucasus. Rates of forest change throughout the Caucasus Mountains were surprisingly low, with forest loss and forest gain was most likely due to cropland abandonment and less grazing pressure. Our results highlight both the importance and the feasibility of topographic correction for accurate large-area land-cover classifications in steep terrain.

Large-area mapping↗

Identifying Common Coordination Procedures across Extensible Traffic Management (xTM) to Integrate xTM Operations into the National Airspace System

New categories of missions and vehicle types, such as drone delivery services, on-demand air taxi, and high-altitude long-endurance (HALE) vehicles are being proposed to operate using a novel, highly automated information exchange infrastructure and a community-based, cooperative traffic management concept. Collectively, these new operations are called Extensible Traffic Management (xTM). As these xTM vehicles become more prevalent, their operations will increasingly overlap with existing conventional aircraft and with each other. In order to seamlessly co-exist with current conventional aircraft operations, new coordination procedures, tools and services will be needed to integrate xTM into the future National Airspace System (NAS). In our prior work, we have identified a set of use cases for xTM interactions with air traffic control (ATC), categorized across different xTM operations based on trigger events. Events consisted of ones such as nominal xTM vehicle transition into the ATC environment or an off-nominal emergency landing situation. In this paper, we have extended the prior work to identify commonalities in the coordination procedures across xTM, as well as differences that are specific to the individual xTM operations. The overall results showed that two types of xTM-ATC interactions were prevalent: 1) xTM vehicles transitioning between xTM and ATC operational environments; 2) xTM vehicles being allowed to continue xTM operations in areas that are normally controlled by ATC. The results also suggested that emergency and rare off-nominal events may need specialized procedures for each vehicle type. The overall results suggest that there is a pathway to define a common method of handling and integrating diverse xTM operations in the future NAS, but there need to be procedures for individualized handling of xTM vehicles in infrequent, safety-critical events.

Extensible Traffic Management (xTM)↗

CyanoSCape: Freshwater Phytoplankton and Floating Aquatic Vegetation Biodiversity

In Southern Africa, the impacts of anthropogenic activities on biodiversity and ecosystem services are exacerbated by the climate crisis. Rapid land use change and the lack of emphasis on environmentally sustainable agricultural practices has hindered hydrological processes and compromised riverine and aquatic ecosystems. This poses obvious risks to natural/indigenous aquatic biodiversity and long-term ecosystem sustainability. Phytoplankton serve as the foundation of the freshwater food web with zooplankton as consumers, which feed fish, invertebrates, and so on up the food chain that comprises the biodiversity of the freshwater system that serves as habitat for biodiversity as well. The diversity of phytoplankton (microscopic organisms) includes photosynthesizing bacteria (cyanobacteria), plant-like diatoms, dinoflagellates, and green algae. Nutrient run-off from agricultural fertilizers and urban overflows, warm temperatures, abundant light availability and compromised hydrological systems provide an ideal environment for cyanobacteria to flourish. Increased prevalence of cyanobacteria, due to both natural and anthropogenic causes, can incur significant effects on the biodiversity of the overall phytoplankton assemblage. These bloom forming algae can significantly outcompete other phytoplankton classes in warmer and eutrophic waters where they are quick to dominate the freshwater system. Eutrophication and toxic cyanobacteria blooms (cyanoHABs) in the inland waters of the Greater Cape Floristic Region (GCFR) incur significant effects on the biodiversity of the overall phytoplankton assemblage and provide a favorable environment forthe overgrowth of floating aquatic vegetation (FAV), which is often invasive and associated with reduced aquatic biodiversity. The algal biodiversity of the GCFR’s freshwater systems is not well characterized. Hyperspectral optical observations are expected to facilitate the improvement of current phytoplankton functional type retrievals significantly, as the sensitivity is sufficient that the distinctive, fine spectral features of different phytoplankton groups can be detected. This will enable testing emerging algorithms and inform the development of new algorithms for use with upcoming hyperspectral satellite missions in this decade. Innovations in optical sensor sensitivity and next generation machine learning capabilities considerably enhance the potential for accurate and rapid detection of phytoplankton, namely the presence, extent, and diversity of cyanobacteria present in cyanoHABs and additionally, invasive FAV. Upcoming hyperspectral satellite missions such as NASA’s Surface Biology and Geology (SBG), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and the European Space Agency’s Copernicus Hyperspectral Imaging Mission (CHIME) will provide imagery with unprecedented spectral and spatial resolution which will further enable the discovery of linkages between the seasonality and dynamics of HABs and FAV. The overarching goal of this project is to utilize hyperspectral data, with recently developed and next-generation algorithms, to determine the biodiversity of freshwater systems phytoplankton assemblage with emphasis on genus level distinction, as well as monitor the prevalence and diversity of FAV.

CyanoSCape↗

Direct observation of 3D atomic packing in monatomic amorphous materials

Liquids and solids are two fundamental states of matter. Although the structure of crystalline solids has long been solved by crystallography, our understanding of the 3D atomic structure of liquids and amorphous materials remained speculative. In 1952, Frank hypothesized that icosahedral order is the prevalent atomic motif in monatomic liquids. Over the past six decades, there have been a great deal of experimental, computational and theoretical studies to understand the structure of liquids and amorphous materials. A polytetrahedral packing model was proposed to explain the 3D atomic structure of monatomic liquids and amorphous materials, in which icosahedral order is a key feature. The icosahedral order has also been found to play an important role in the structure of metallic glasses and quasicrystals. Here we advance atomic electron tomography to determine for the first time the 3D atomic positions in monatomic amorphous materials, including a Ta thin film and two Pd nanoparticles. Despite the different chemical composition and synthesis methods, we observe that pentagonal bipyramids are the most abundant atomic motifs in these amorphous materials. Contrary to the traditional understanding, the pentagonal bipyramids do not assemble full icosahedra, but are closely connected with each other to form networks that extend to medium-range scale. Our molecular dynamic simulations, coupled with experimental results, further reveal that the 3D atomic structure of monatomic liquids is similar to the experimental amorphous material. Moreover, we find that the pentagonal bipyramid networks, prevalent in monatomic liquids, rapidly grow in size and form a fraction of full icosahedra during the quench from a liquid to a metallic glass state, providing a possible new picture of structural evolution through the glass transition.

Yuan, Yakun↗

Controlled Environment Agriculture - Farming and Food Access for Healthy Diets

Fruits and vegetables are critical for a healthy diet and USDA guidelines recommend increasing fruit and vegetable consumption for almost all Americans. However, most food system lifecycle assessments do not capture the importance of these food groups in the food system. Moreover, long supply chains and limited access to fresh produce are particularly prevalent in disadvantaged neighborhoods. Many of these communities are food deserts or food swamps characterized by a lack of full-scale groceries, and a prevalence of convenience stores and fast-food restaurants. Stocking of fresh produce is difficult and often unprofitable for small stores. Convenience stores are usually space-limited, unable to achieve economies-of-scale in purchasing and have difficulty managing the short shelf-life of fresh produce. Controlled-Environment (CE) farming, including greenhouses and fully indoor vertical farms, have the potential to address persistent food access issues for disadvantaged communities. CE farming offers several potential advantages for food desert and food swamp neighborhoods, including shorter or non-existent transport distances and fewer middlemen, which could reduce costs. CE farming can be integrated into existing buildings at virtually any scale, providing local jobs and revitalization of community hubs such as shopping malls, or larger-scale farms located in close-in warehouse or industrial areas. Rainwater harvesting and recovery of nutrients from wastewater could further improve food system resilience and circularity. Geospatial modeling of CE farming in case study cities of various sizes and densities will help to quantify the benefits of CE farming. Geospatial modeling can help to answer questions such as whether sufficient collection area is available for rainwater harvesting for a CE farm, and what size of farm would be required to serve the community living within walking distance of the farm. This paper reports on progress in geospatial modeling of CE farming integration into several case study cities.

controlled environment agriculture↗

Probing the Isospin Composition of Short-Range Correlated Pairs at Jefferson Lab Hall B

Nucleons in short-range correlated (SRC) pairs, due to their close proximity and high relative momentum, can provide insight into the short-range part of the strong nuclear interaction. In particular, the prevalence of np pairs is due to the dominance of a tensor term for correlated nucleons with momenta of approximately 400?600 MeV/c. This dissertation comprises two studies advancing the community?s understanding of the isospin composition of SRC pairs. First, I performed a study of proton and neutron knockout from initially low-momentum and high-momentum states in 3He. Previous work has shown that protons are disproportionately represented in high-momentum states in neutron-rich nuclei. I demonstrate that spectral functions for the proton-rich nucleus 3He predict, in agreement with data, that neutrons are disproportionately represented in high-momentum states, but that 3He does not display the same strong prevalence of np pairs that is observed in larger nuclei. Second, Generalized Contact Formalism (GCF), a well-supported theory for predicting SRC behavior, predicts the transition from an isospin-dependent, tensor-dominant interaction at intermediate distances to a scalar-dominant, isospin-independent interaction at very short distances. This dissertation uses data from the CLAS12 Nuclear Targets Experiments in Hall B at Jefferson Lab to measure the relative abundances of pp and pn pairs for increasing relative momentum and decreasing separation. I provide an independent confirmation of the previously-observed increase in pp pairs at increasing momentum of the struck nucleon. I also contribute to the application of the new CLAS12 Central Neutron Detector by precisely measuring the neutron detection efficiency and developing a machine learning model for rejecting charged particle background.

Seroka, Erin↗

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. Here in this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Global drivers of avian haemosporidian infections vary across zoogeographical regions

Macroecological analyses provide valuable insights into factors that influence how parasites are distributed across space and among hosts. Amid large uncertainties that arise when generalizing from local and regional findings, hierarchical approaches applied to global datasets are required to determine whether drivers of parasite infection patterns vary across scales. Here we assessed global patterns of haemosporidian infections across a broad diversity of avian host clades and zoogeographical realms to depict hotspots of prevalence and to identify possible underlying drivers.

haemosporidian prevalence↗

Human and behavioral factors contributing to spine-based neurological cockpit injuries in pilots of high-performance aircraft: recommendations for management and prevention

In high-performance aircraft, the need for total environmental awareness coupled with high-g loading (often with abrupt onset) creates a predilection for cervical spine injury while the pilot is performing routine movements within the cockpit. In this study, the prevalence and severity of cervical spine injury are assessed via a modified cross-sectional survey of pilots of multiple aircraft types (T-38 and F-14, F-16, and F/A-18 fighters). Ninety-five surveys were administered, with 58 full responses. Fifty percent of all pilots reported in-flight or immediate post-flight spine-based pain, and 90% of fighter pilots reported at least one event, most commonly (> 90%) occurring during high-g (> 5 g) turns of the aircraft with the head deviated from the anatomical neutral position. Pre-flight stretching was not associated with a statistically significant reduction in neck pain episodes in this evaluation, whereas a regular weight training program in the F/A-18 group approached a significant reduction (mean = 2.492; p < 0.064). Different cockpit ergonomics may vary the predisposition to cervical injury from airframe to airframe. Several strategies for prevention are possible from both an aircraft design and a preventive medicine standpoint. Countermeasure strategies against spine injury in pilots of high-performance aircraft require additional research, so that future aircraft will not be limited by the human in control.

Case Reports↗

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗

Fundamental Effects of Al and Ta on Microstructure and Phase Transformations in the Al–Cr–Mo–Ta–Ti Refractory Complex Concentrated Alloy System

The effect of aluminum and tantalum concentrations on a refractory metal complex concentrated alloy is reported, particularly with respect to their effect on microstructure and phase composition of the alloy in cast and annealed form. Alloys with an equiatomic composition, (AlCrMoTaTi), an aluminum-lean composition (Al 0.75 CrMoTaTi), and a tantalum-lean composition (AlCrMoTa 0.75 Ti) are produced via arc melting. The alloys exhibit multiphase structures, confirmed by X-ray diffraction, microstructural characterization, and thermal analysis. The minor off-equiatomic adjustments of aluminum and tantalum in this alloy system did not drastically alter the prevalence of the Cr–Ta-based Laves phase. Correlations between thermodynamic predictions and observed phase transformations via thermal analysis are improved upon refinement of calculations removing impractical intermediate phases. As a result, experimental findings provide information for the refinement of thermodynamic modeling and deliver additional insight into the optimization of alloy compositions within this five-component system.

36 MATERIALS SCIENCE↗

Multiple Twinning in Nacre and Aragonite

Twinning occurs when two crystals share a coherent interface along which their lattices are mirror symmetric. Here, twinning is investigated in biogenic and geologic aragonite (CaCO 3 ) in columnar nacre from the red abalone Haliotis rufescens, sheet nacre from the black-lip pearl oyster Pinctada margaritifera, and geologic aragonite. All samples exhibit the expected single (110) twins, characterized by a 116.2° rotation of the aragonite a-axis. Surprisingly, multiple twins—double, triple, quadruple—are also observed, each involving successive 116.2° rotations of the a-axis around the c-axis. Multiple twinning is concomitant but distinct from the well-known cyclic twinning of aragonite. Multiple twinning is most prevalent in columnar nacre, reaching up to quadruple twins, most frequently occurring between nacre tablets in the same column, whereas sheet nacre and geologic aragonite exhibit up to triple twins. The frequency of twins decreases with increasing twin multiple, consistent with a physical rather than biological origin. This interpretation is supported by observations in geologic aragonite and by simulations of columnar nacre growth, which incorporate only nucleation rate, growth rate, and basic geometric constraints, yet they reproduce the observed twinning behavior in columnar nacre, reinforcing the conclusion that multiple twinning arises from physical growth mechanisms.

Schmidt, Connor A. [Univ. of Wisconsin, Madison, W↗

Unveiling Atomistic Mechanisms Governing Additive Manufacturing Processability and Mechanical Behavior of a Refractory Complex Concentrated Alloy

Extending the concept of complex concentrated alloys (CCAs) to the refractory alloys (solidus temperature over 2000 °C) space potentially facilitates the design of lightweight structural alloys with service temperatures that exceed those of Ni and Co‐based alloys. However, the room and elevated temperature tensile properties of the current refractory‐CCAs (R‐CCAs) are inferior to those of the Ni/Co‐based alloys. Furthermore, the manufacturing scalability of R‐CCAs remains challenging, in that cracks are prevalent in all R‐CCAs when processed using near‐net shape manufacturing processes, such as fusion‐based additive manufacturing (F‐BAM). Still, mechanisms governing the poor F‐BAM processability of R‐CCAs remain unexplored. Here, to this end, this work unveils the atomistic mechanisms underlying F‐BAM process‐induced cracking in a NbTiTaMoHfZrC R‐CCA. The implications of light elements’ presence for intrinsic ductility and grain boundary cohesion, and subsequently for F‐BAM processability and mechanical behavior, are revealed. Leveraging the insights, we accomplish what is, to the best of the knowledge, the first instance of crack‐free F‐BAM processing of any R‐CCA. Additionally, the R‐CCA exhibits over 20% tensile ductility and ≈160 MPa tensile yield strength at 1200 °C. In addition to facilitating the design of lightweight R‐CCAs, findings enable scalable manufacturing of these ultra‐high temperature alloys for structural applications.

Refractory alloys↗

A Pseudo‐Surfactant Chemical Permeation Enhancer to Treat Otitis Media via Sustained Transtympanic Delivery of Antibiotics

Abstract Chemical permeation enhancers (CPEs) represent a prevalent and safe strategy to enable noninvasive drug delivery across skin‐like biological barriers such as the tympanic membrane (TM). While most existing CPEs interact strongly with the lipid bilayers in the stratum corneum to create defects as diffusion paths, their interactions with the delivery system, such as polymers forming a hydrogel, can compromise gelation, formulation stability, and drug diffusion. To overcome this challenge, differing interactions between CPEs and the hydrogel system are explored, especially those with sodium dodecyl sulfate (SDS), an ionic surfactant and a common CPE, and those with methyl laurate (ML), a nonionic counterpart with a similar length alkyl chain. Notably, the use of ML effectively decouples permeation enhancement from gelation, enabling sustained delivery across TMs to treat acute otitis media (AOM), which is not possible with the use of SDS. Ciprofloxacin and ML are shown to form a pseudo‐surfactant that significantly boosts transtympanic permeation. The middle ear ciprofloxacin concentration is increased by 70‐fold in vivo in a chinchilla AOM model, yielding superior efficacy and biocompatibility than the previous highest‐performing formulation. Beyond improved efficacy and biocompatibility, this single‐CPE formulation significantly accelerates its progression toward clinical deployment.

Engineering↗

A Morphologically Stable Li/Electrolyte Interface for All-Solid-State Batteries Enabled by 3D-Micropatterned Garnet

Morphological degradation at the Li/solid-state electrolyte (SSE) interface is a prevalent issue causing performance fading of all-solid-state batteries (ASSBs). To maintain the interfacial integrity, most ASSBs are operated under low current density with considerable stack pressure, which significantly limits their widespread usage. In this work, a novel 3D-micropatterned SSE (3D-SSE) that can stabilize the morphology of the Li/SSE interface even under relatively high current density and limited stack pressure is reported. Under the pressure of 1.0 MPa, the Li symmetric cell using a garnet-type 3D-SSE fabricated by laser machining shows a high critical current density of 0.7 mA cm –2 and stable cycling over 500 h under 0.5 mA cm –2 . This excellent performance is attributed to the reduced local current density and amplified mechanical stress at the Li/3D-SSE interface. These two effects can benefit the flux balance between Li stripping and creep at the interface, thereby pre-venting interfacial degradation such as void formation and dendrite growth.

3D-micropatterned garnet↗

The Influence of Ozone: Superstoichiometric Oxygen in Atomic Layer Deposition of Fe 2 O 3 Using tert -Butylferrocene and O 3

Understanding the chemical mechanisms at play in atomic layer deposition (ALD) is critical for effective process development and expansion of ALD into more complex classes of materials. In this work, a mechanistic study of iron oxide deposited by ALD using tert-butylferrocene and ozone as reactants is performed. Iron oxide ALD using ozone is a useful model system for mechanistic studies due to the prevalence of ozone-based ALD processes and the uses of iron oxide in ternary and quaternary metal oxides. Results show that saturation conditions require significantly greater exposures of both reactants than is typically reported in the literature, and growths per cycle of greater than one monolayer of Fe 2 O 3 per cycle are observed and explained. A growth mechanism is proposed whereby increased ozone exposure results in uptake of superstoichiometric oxygen into the film. X-ray characterizations reveal the presence of excess oxygen stored near the surface of films deposited with larger ozone exposures and show that increased ozone exposures cause crystalline domain rearrangement and conversion of the film from the γ-maghemite phase to the α-hematite phase. The mechanism described here has implications for the wider class of ozone-based ALD processes, and potential applications of this growth phenomenon are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Novel Mito-Nuclear Combinations Facilitate the Global Invasion of a Major Agricultural Crop Pest

A fundamental understanding of the underlying mechanisms involved in biological invasions is crucial to developing effective risk assessment and control measures against invasive species. The fall armyworm (FAW), Spodoptera frugiperda, is a highly invasive pest that has rapidly spread from its native Americas into much of the Eastern Hemisphere, with a highly homogeneous nuclear genetic background. However, the exact mechanism behind its rapid introduction and propagation remains unclear. Here, a systematic investigation is conducted into the population dynamics of FAW in China from 2019 to 2021 and found that FAW individuals carrying “rice” mitochondria (FAW-mR) are more prevalent (>98%) than that with “corn” mitochondria (FAW-mC) at the initial stage of the invasion and in newly-occupied non-overwintering areas. Further fitness experiments show that the two hybrid-strains of FAW exhibit different adaptions in the new environment in China, and this may have been facilitated by amino acid changes in mitochondrial-encoded proteins. FAW-mR used increases energy metabolism, faster wing-beat frequencies, and lower wing loadings to drive greater flight performance and subsequent rapid colonization of new habitats. In contrast, FAW-mC individuals adapt with more relaxed mitochondria and shuttle energetics into maternal investment, observed as faster development rate and higher fecundity. The presence of two different mitochondria types within FAW has the potential to significantly expand the range of damage and enhance competitive advantage. Overall, the study describes a novel invasion mechanism displayed by the FAW population that facilitates its expansion and establishment in new environments.

60 APPLIED LIFE SCIENCES↗

Facet-Dependent Cold Welding of Au Nanorods Revealed by Liquid Cell Transmission Electron Microscopy

Cold welding of metals at the nanoscale has been demonstrated to play a significant role in bottom-up manufacturing and self-healing processes of nanostructures and nanodevices. However, the welding mechanism at the nanoscale is not well understood. In this study, a comprehensive demonstration of the cold welding process of gold nanorods with different modes is presented through in situ liquid cell transmission electron microscopy. The experimental results and molecular dynamics simulations reveal that the nanorods are welded through the facet-dependent atomic surface diffusion and rearrangement along {100} facets. The density functional theory calculations indicate that the preferred coalescence of two {100} surfaces is thermodynamically favorable. Unlike the prevalent “oriented attachment” in the nanoparticle coalescence, the misalignment of nanorod orientations and local stresses can induce grain boundaries and stacking faults in the welded interface.

42 ENGINEERING↗