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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enhancing carbon storage through proactively managing fire-prone coniferous mountain forests

Mountain ecosystems typically serve as carbon (C) sinks. However, studies also suggest that they could be C sources due to climate warming, drought and insect-related mortality, wildfires, and management actions. We applied the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), a process-based dynamic vegetation-ecosystem model, to investigate the role of ecosystem management in C storage under Mediterranean climate over the 21 st century. We modified LPJ-GUESS to include implementing mechanical thinning by vegetation size classes, components, and types along with a new mechanistic fire-occurrence model that accounts for wind speed and lightning ignition. Simulations show that mechanical thinning or prescribed fire performed 5-20 years in advance of a high-severity wildfire reduced direct wildfire C emissions by 38-66 %. Our results also show that long-term management actions repeated every 5-20 years, including thinning relatively small trees (diameters up to 7 inches or ∼178 mm), can maintain stable C levels in the forest and lower dead-fuel amounts. We found that, although prescribed fire mitigated wildfire severity, ecosystem C storage from reduced wildfire emissions can be outweighed by the added emissions from the prescribed fire themselves. Thinning plus removing and sequestering the thinned biomass can ensure that forests act as net C sinks through the end of the 21 st century. However, addition of prescribed fire is needed to reduce understory and lower the projected extent of high-severity wildfire. Achieving the competing goals of reducing wildfire and making the Sierra Nevada long-term C sink can be advanced through carefully coordinated thinning, sequestration of thinned biomass, and prescribed fire.

Climate solutions↗
DOE OSTI2021

Modeling the Size Distribution and Chemical Composition of Secondary Organic Aerosols during the Reactive Uptake of Isoprene-Derived Epoxydiols under Low-Humidity Condition

Reactive uptake of isoprene epoxydiols (IEPOX), which are isoprene oxidation products, onto acidic sulfate aerosols is recognized to be an important mechanism for the formation of isoprene-derived secondary organic aerosol (SOA). While a mechanistic understanding of IEPOX-SOA formation exists, several processes affecting their formation remain uncertain. Evaluating mechanistic IEPOX-SOA models with controlled laboratory experiments under longer atmospherically relevant timescales is critical. Here, we implement our latest understanding of IEPOX-SOA formation within a box model to simulate the measured reactive uptake of IEPOX on polydisperse ammonium bisulfate seed aerosols within an environmental Teflon chamber. The model is evaluated with single-particle measurements of size distribution, volume, density, and composition of aerosols due to IEPOX-SOA formation at timescales of hours. Additionally, we find that the model can simulate the growth of particles due to IEPOX multiphase chemistry, as reflected in increases of the mean particle size and volume concentrations, and a shift of number size distribution to larger sizes. The model also predicts the observed evolution of particle number mean diameter and total volume concentrations at the end of the experiment. We show that in addition to self-limiting effects of IEPOX-SOA coatings, accounting for the molar balance between inorganic and organic sulfate and mass accommodation coefficient of IEPOX are important parameters governing modeling of IEPOX-SOA formation. Thus, models which do not account for the molar sulfate balance and/or diffusion limitations within IEPOX-SOA coatings are likely to predict too high IEPOX-SOA formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Attractive Noncovalent Interactions versus Steric Confinement in Asymmetric Supramolecular Catalysis

The remarkable catalytic performance of enzymes stems from their ability to engage in precise noncovalent interactions (NCIs) within a sterically confined space. Supramolecular catalysis seeks to emulate and understand these strategies through the rational design of simple and controlled catalyst microenvironments. While both steric confinement and attractive interactions have been invoked as key to host activity, their relative contribution to rate enhancement and selectivity, as well as potential trade-offs, remains an outstanding question. Here, we address this question by systematically comparing two metal–organic supramolecular catalysts, which differ in the strength of their attractive noncovalent interactions and in their cavity volume. Our findings reveal that the catalyst with the larger cavity, and with stronger available NCIs, exhibits both significant rate acceleration (100-fold) and enhanced enantioselectivity (84% vs 14% ee) in a model ketone reduction compared to its smaller analogue. Mechanistic analysis, binding competition experiments, and computational modeling indicate that these differences predominantly stem from stabilizing noncovalent interactions in the larger catalyst, a result that challenges existing steric-based models of supramolecular stereoinduction. Understanding the governing factors of asymmetric induction and rate acceleration in supramolecular hosts will undoubtedly inform future catalyst design.

Catalysts↗
DOE OSTI2020

Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations

The soil environment is constantly changing due to shifts in soil moisture, nutrient availability and other conditions. To contend with these changes, soil microorganisms have evolved a variety of ways to adapt to environmental perturbations, including regulation of gene expression. However, it is challenging to untangle the complex phenotypic response of the soil to environmental change, partly due to the absence of predictive modeling frameworks that can mechanistically link molecular-level changes in soil microorganisms to a community’s functional phenotypes (or metaphenome). Towards filling this gap, we performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responded to changes in soil moisture and nutrient conditions and to determine which genes were expressed under a given condition. Our integrated modeling approach revealed previously unknown, but critically important aspects of the soil microbiomes’ response to environmental perturbations. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended conditions. A subsequent gene co-expression network analysis revealed that drought-associated genes occupied more central positions in a network model of the soil community, compared to the genes associated with wet, and glycine-amended conditions. These results indicate the occurrence of system-wide metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, the approach that we demonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which multi-omics data are available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Mechanistic mass transfer in hollow fiber membrane solvent extraction for bio-based isobutanol

Membrane solvent extraction (MSE) has emerged as a promising method for selectively recovering bioproducts from complex aqueous streams. Bio-isobutanol, a next-generation feedstock for biofuel, remains challenging to recover because of its low concentration and the presence of inhibitory substances. This study explores the potential of hollow fiber (HF) MSE for bio-isobutanol recovery and systematically examines the coupled effects of fiber packing, shell-side flow dynamics, and aqueous chemistry on performance. A resistance-in-series model is applied to understand mass transfer in the HF MSE modules, quantify local resistances, and validate overall performance. The results show that increasing the fiber packing provides a larger interfacial area but induces poor flow distribution and channeling, hindering effective isobutanol transport. Meanwhile, increasing the shell-side velocity improves isobutanol recovery due to reductions in the boundary layer thickness. The presence of salts, added to mimic fermentation broth, increases the partition coefficient through salting-out effects, further improving isobutanol flux. A modified correlation for the shell-side mass transfer coefficient (k s,ϕ+v ), integrating geometric and hydrodynamic effects, was developed and validated. The proposed model achieves highly predictive accuracy (r 2 = 0.9808) across a wide range of conditions, outperforming previous models. The findings provide mechanistic insight into the interaction of geometric packing, hydrodynamics, and chemistry in governing mass transfer in HF MSE. Overall, this work demonstrates the potential of HF MSE for efficient bio-isobutanol recovery and also provides practical guidelines on critical factors (packing fraction, partition coefficient, and shell-side velocity), aiding in the design and scaling of MSE systems for resource recovery.

Aqueous chemistry↗
DOE OSTI2020

Microbial macroecology: In search of mechanisms governing microbial biogeographic patterns

Abstract Introduction Rapidly advancing technologies and accumulating information about microbial communities across the globe allow the quantification of microbial properties and functions at a macro‐scale. These emerging microbial biogeographic patterns call for a practical macroecological approach to investigate their underlying mechanisms. Aims The primary aims of this paper are to review the advancements of microbial macroecology in seeking mechanisms governing microbial biogeographic patterns, and to further lay out a roadmap for microbial macroecology in 10 years. Methods We reviewed the progress of microbial macroecology and demonstrated the application of the microbial macroecological approach to microbial biogeographic patterns with three case studies. Results Microbial macroecology provides a platform for understanding microbial abundance, community structure, and functioning across space, time, and taxonomic hierarchy. It emphasizes the integral effects of environmental filtering, microbial responses, diversification, dispersal, and local extinction that drive the microbial biogeographic patterns. The microbial macroecological approach emphasizes the last two stages of the four‐stage scientific method applied to microbial ecology: (a) describing microbial traits across scales to reveal patterns, (b) mathematically representing these patterns, (c) developing and testing conceptual models to build a mechanistic understanding of these patterns from a macroecological perspective, (d) plugging the new knowledge into the theoretical advancements. Three case studies were used to demonstrate the microbial macroecological approach for understanding the global patterns of microbial biomass carbon, microbial composition (fungi : bacteria ratio), and microbial carbon use efficiency. Conclusions Microbial macroecology offers a platform for understanding the mechanisms that drive biogeographic patterns of microbial abundance, diversity and functions. It is likely that these patterns and mechanisms will be increasingly incorporated into predictive models that link climate, carbon dynamics, and biogeochemical processes. A roadmap is outlined for the growing microbial macroecology field; we expect significant progress will be made in five research directions over the next 10 years.

Xu, Xiaofeng↗
DOE OSTI2021

An Integrative Model for Soil Biogeochemistry and Methane Processes. II: Warming and Elevated CO 2 Effects on Peatland CH 4 Emissions

Peatlands are one of the largest natural sources for atmospheric methane (CH 4 ), a potent greenhouse gas. Climate warming and elevated atmospheric carbon dioxide (CO 2 ) are two important environmental factors that have been confirmed to stimulate peatland CH 4 emissions; however, the mechanisms underlying enhanced emissions remain elusive. A data-model integration approach was applied to understand the CH 4 processes in a northern temperate peatland under a gradient of warming and doubled atmospheric CO 2 concentration. We found that warming and elevated CO 2 stimulated CH 4 emissions through different mechanisms. Warming initially stimulated but then suppressed vegetative productivity while stimulating soil organic matter (SOM) mineralization and dissolved organic carbon (DOC) fermentation, which led to higher acetate production and enhanced acetoclastic and hydrogenotrophic methanogenesis. Warming also enhanced surface CH 4 emissions, which combined with warming-caused decreases in CH 4 solubility led to slightly lower dissolved CH 4 concentrations through the soil profiles. Elevated CO 2 enhanced ecosystem productivity and SOM mineralization, resulting in higher DOC and acetate concentrations. Higher DOC and acetate concentrations increased acetoclastic and hydrogenotrophic methanogenesis and led to higher dissolved CH 4 concentrations and CH 4 emissions. Both warming and elevated CO 2 had minor impacts on CH 4 oxidation. A meta-analysis of warming and elevated CO 2 impacts on carbon cycling in wetlands agreed well with a majority of the modeled mechanisms. Finally, this mechanistic understanding of the stimulating impacts of warming and elevated CO 2 on peatland CH 4 emissions enhances our predictability on the climate-ecosystem feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Extracellular electron transfer across bio-nano interfaces for CO 2 electroreduction

Acetogenic bacteria represent a class of organisms capable of converting reducing equivalents and carbon dioxide into products with carbon–carbon bonds. Materials-based bio-electrochemical approaches are attractive for supplying biological organisms directly with grid-supplied electrons to convert carbon dioxide to value-added chemicals. Carbon nanotube-modified biocathodes have emerged as promising candidates for microbial electrosynthesis with high yields of carbon product formation, but a fundamental understanding of extracellular charge transfer at this electrode-biofilm interface is still lacking. Here, we utilize solid-state interfaces between semiconducting single-walled carbon nanotubes (s-SWCNT) and a model acetogenic bacterium for mechanistic studies of electro-catalytic CO 2 conversion to acetate. Studies of bacteria/s-SWCNT interactions in a transistor-based device suggest direct extracellular electron transfer (EET) at the bio-nano interface. Deuterium isotope labeling experiments confirmed that the availability of electrochemically produced H 2 as a redox mediator does not limit the efficiency of EET and CO 2 electro-reduction for C. ljungdahlii biofilms, suggesting the primary reducing equivalents are the electrons delivered across the electrode/bacterium interface or involvement of biological redox mediators. Additional isotope labeling studies demonstrate high Faradaic efficiency for CO 2 electro-reduction at the SWCNT/bacterium interface. These results provide important information about EET across the bacterium/material interface in a model biocathode.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Data for: Time to anoxia: Observations and predictions of oxygen drawdown following coastal flood events

This dataset contains data and code associated with the paper Patel et al. "Time to anoxia: Observations and predictions of oxygen drawdown following coastal flood events".The coastal terrestrial-aquatic interface (TAI) is a highly dynamic system characterized by strong physical, chemical, and biological gradients. In particular, shifting soil redox conditions and consumption of terminal electron acceptors, due in part to dynamic hydrologic conditions, is a strong driver of carbon availability and transformations across TAIs. However, while redox dynamics are well described, our ability to quantitatively forecast rates of oxic to anoxic shifts in soils with different characteristics and inundation regimes is limited. We integrated field measurements, laboratory incubations, and model simulations to improve mechanistic understanding of oxygen consumption dynamics in coastal soils. Continuous in situ monitoring unexpectedly revealed that flooding caused temporary spikes in subsurface dissolved oxygen followed by rapid consumption in the wetlands. To further investigate these mechanisms in a controlled setting, we performed laboratory incubations using surface and subsurface soils from a TAI gradient (defined here as upland forest to transitional forest to wetland) in Western Lake Erie to measure oxygen consumption rates in TAI soils during flood events. In our experiments, wetland soils reached anoxia the fastest, in ∼ 9 h on average, whereas upland soils turned anoxic in ∼ 18 h. Subsurface upland soils did not turn anoxic even after two weeks of saturation in the lab, and their oxygen consumption patterns suggested carbon and/or nutrient limitation. These results are consistent with in-situ groundwater redox and oxygen measurements in the field, where wetland soils exhibited the highest rates of oxygen consumption along the TAI. Model simulations of oxygen consumption suggested that oxygen consumption had stronger abiotic controls in wetland soils but stronger biotic controls in upland soils, providing a useful framework for future incubation experiments. Microbial activity is a strong driver of oxygen consumption in TAI soils, although it is constrained by the availability of dissolved carbon in subsurface soils.This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/COMPASS-DOE/cmps-time_to_anoxia (DOI: 10.5281/zenodo.10815374).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Polyethylene Terephthalate Deconstruction Catalyzed by a Carbon‐Supported Single‐Site Molybdenum‐Dioxo Complex

Abstract Polyethylene terephthalate (PET) is selectively depolymerized by a carbon‐supported single‐site molybdenum‐dioxo catalyst to terephthalic acid (PTA) and ethylene. The solventless reactions are most efficient under 1 atmosphere of H 2 . The catalyst exhibits high stability and can be recycled multiple times without loss of activity. Waste beverage bottle PET or a PET + polypropylene (PP) mixture (simulating the bottle + cap) proceeds at 260 °C with complete PET deconstruction and quantitative PTA isolation. Mechanistic studies with a model diester, 1,2‐ethanediol dibenzoate, suggest the reaction proceeds by initial retro‐hydroalkoxylation/β‐C−O scission and subsequent hydrogenolysis of the vinyl benzoate intermediate.

Kratish, Yosi↗
DOE OSTI2022

Isolation, Characterization, and Depolymerization of L -Cysteine Substituted Eucalyptus Lignin

Lignin condensation reactions are hard to avoid or control during separation, which is a deterrent to lignin isolation and post-conversation, especially for the full utilization of lignocelluloses. Selective protection of β-aryl ether linkages in the isolation process is crucial to lignin valorization. Herein, a two-step acid/alkali separation method assisted with l-cysteine for eucalyptus lignin separation is developed, and the isolated L -cysteine lignins (LCLs) are comprehensively characterized by 2D NMR, 31 P NMR, thioacidolysis, etc. Compared to the two-step control treatment, a much higher β-O-4 content is preserved without reducing the separation efficiency assisted by L -cysteine, which is also significantly higher than alkali lignin and kraft lignin. The results of hydrogenolysis show that LCLs generate a much higher monomer yield than that of control sample. Structural analysis of LCLs suggests that lignin condensation reaction, to some extent, is suppressed by adding L -cysteine during the two-step acid/alkali separation. Further, mechanistic studies using dimeric model compound reveals that L -cysteine may be the α-carbon protective agent in the two-step separation. The role of L -cysteine in the two-step lignin isolation method provides novel insights to the selective fractionation of lignin from biomass, especially for the full valorization of lignocellulosic biomass.

09 BIOMASS FUELS↗
DOE OSTI2023

Theoretical and Experimental Advances in High-Pressure Behaviors of Nanoparticles

Using compressive mechanical forces, such as pressure, to induce crystallographic phase transitions and mesostructural changes while modulating material properties in nanoparticles (NPs) is a unique way to discover new phase behaviors, create novel nanostructures, and study emerging properties that are difficult to achieve under conventional conditions. In recent decades, NPs of a plethora of chemical compositions, sizes, shapes, surface ligands, and self-assembled mesostructures have been studied under pressure by in-situ scattering and/or spectroscopy techniques. As a result, the fundamental knowledge of pressure–structure–property relationships has been significantly improved, leading to a better understanding of the design guidelines for nanomaterial synthesis. In the present review, we discuss experimental progress in NP high-pressure research conducted primarily over roughly the past four years on semiconductor NPs, metal and metal oxide NPs, and perovskite NPs. We focus on the pressure-induced behaviors of NPs at both the atomic- and mesoscales, inorganic NP property changes upon compression, and the structural and property transitions of perovskite NPs under pressure. We further discuss in depth progress on molecular modeling, including simulations of ligand behavior, phase-change chalcogenides, layered transition metal dichalcogenides, boron nitride, and inorganic and hybrid organic–inorganic perovskites NPs. These models now provide both mechanistic explanations of experimental observations and predictive guidelines for future experimental design. Here, we conclude with a summary and our insights on future directions for exploration of nanomaterial phase transition, coupling, growth, and nanoelectronic and photonic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Controlled Acidity Gradients Enable CO 2 Reduction to Formic Acid (Not Formate) by Molecular Electrocatalysts

Neutral or basic conditions are commonly required for the selective electrochemical reduction of CO 2 , leading to the accumulation of carbonate salts and the generation of formate rather than formic acid. A generalizable strategy for obtaining formic acid (not formate) in the electroreduction of CO 2 with molecular catalysts is introduced, based on controlling acidity gradients using a dual-electrolyte cell with a proton-exchange membrane. This approach uses anodic water oxidation as the source of protons and electrons for CO 2 reduction to formic acid, while mitigating H 2 evolution near the cathode and avoiding carbonate formation. Mechanistic studies, including systems modeling, provide insight into the origin of the formic acid selectivity and guide the broader implementation of this strategy in molecular electrocatalysis for CO 2 utilization.

Alcohols↗
DOE OSTI2025

Bulky Phosphine Ligands Promote Palladium-Catalyzed Protodeboronation

The Suzuki-Miyaura cross-coupling reaction is plagued by protodeboronation, an undesirable side reaction with water that consumes the boronic acid derivatives required for the cross-coupling reaction. Meticulous mechanistic studies have previously established protodeboronation to be highly sensitive to the nature of the boronic reagent and reaction conditions. Particularly, the presence of bases, which are essential for the Suzuki-Miyaura coupling, is known to catalyze protodeboronation. However, protodeboronation catalyzed by palladium-phosphine complexes, the benchmark catalyst system for Suzuki-Miyaura cross-coupling, has been understudied compared to its base-catalyzed counterpart. Here, we demonstrate, using automated high-throughput experimentation, comprehensive computational mechanistic analyses and kinetic modeling, that protodeboronation is accelerated by palladium(II) complexes bound to bulky phosphine ligands. While sterically hindered ligands are typically used to facilitate difficult cross-couplings, these ligands can instead paradoxically impede cross-coupling product formation, requiring careful and judicious consideration when choosing ligands for Suzuki-Miyaura cross-couplings.

Ser, Cher Tian [University of Toronto, ON (Canada)↗
DOE OSTI2020

Recent experimental explorations of non-classical nucleation

Nucleation, the early stage of crystallization, is a key step in producing functional materials, but nucleation processes have yet to be understood in detail. Recent advanced characterization techniques, especially atomic force microscopy, liquid-cell transmission electron microscopy (TEM) and cryo-TEM, enable scientists to observe previously inaccessible nucleation details at the nanoscale. In this review, we summarize newly suggested non-classical nucleation models with respect to mechanistic understanding from experimental views, which highlight multi-step nucleation mechanisms. Different intermediated phases such as clusters at the pre-nucleation stage, liquid-like precursors, amorphous phases and even oligomers have been revealed, which play vital roles in nucleation and structure control. Moreover, these intermediated phases practically contribute in general to the structural variances of materials for nanoscience and nanotechnology. Overall, these studies ultimately enable us to control nucleation processes for materials synthesis.

Jin, Biao↗