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Formation kinetics of metal nanoparticles are generally describedviamass transport and thermodynamics‐based models, such as diffusion‐limited growth and classical nucleation theory (CNT). However, metal monomers are commonly assumed as precursors, leaving the identity of molecular intermediates and their contribution to nanoparticle formation unclear. Herein, liquid phase transmission electron microscopy (LPTEM) and reaction kinetic modeling are utilized to establish the nucleation and growth mechanisms and discover molecular intermediates during silver nanoparticle formation. Quantitative LPTEM measurements show that their nucleation rate decreases while growth rate is nearly invariant with electron dose rate. Reaction kinetic simulations show that Ag₄and Ag⁻follow a statistically similar dose rate dependence as the experimentally determined growth rate. We show that experimental growth rates are consistent with diffusion‐limited growthviathe attachment of these species to nanoparticles. The dose rate dependence of nucleation rate is inconsistent with CNT. A reaction‐limited nucleation mechanism is proposed and it is demonstrated that experimental nucleation kinetics are consistent with Ag₄²⁺aggregation rates at millisecond time scales. Reaction throughput analysis of the kinetic simulations uncovered formation and decay pathways mediating intermediate concentrations. We demonstrate the power of quantitative LPTEM combined with kinetic modeling for establishing nanoparticle formation mechanisms and principal intermediates.

 

Aluminum‐dependent stoppage of root growth requires the DNA damage response (DDR) pathway including the p53‐like transcription factor SUPPRESSOR OF GAMMA RADIATION 1 (SOG1), which promotes terminal differentiation of the root tip in response to Al dependent cell death. Transcriptomic analyses identified Al‐induced SOG1‐regulated targets as candidate mediators of this growth arrest. Analysis of these factors either as loss‐of‐function mutants or by overexpression in theals3‐1background shows ERF115, which is a key transcription factor that in other scenarios is rate‐limiting for damaged stem cell replenishment, instead participates in transition from an actively growing root to one that has terminally differentiated in response to Al toxicity. This is supported by a loss‐of‐functionerf115mutant raising the threshold of Al required to promote terminal differentiation of Al hypersensitiveals3‐1. Consistent with its key role in stoppage of root growth, a putativeERF115barley ortholog is also upregulated following Al exposure, suggesting a conserved role for this ATR‐dependent pathway in Al response. In contrast to other DNA damage agents, these results show that ERF115 and likely related family members are important determinants of terminal differentiation of the root tip following Al exposure and central outputs of the SOG1‐mediated pathway in Al response.

 

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

 

Key theoretical frameworks have proposed that examining the impact of exposure to specific dimensions of stress at specific developmental periods is likely to yield important insight into processes of risk and resilience. Utilizing a sample ofN= 549 young adults who provided a detailed retrospective history of their lifetime exposure to numerous dimensions of traumatic stress and ratings of their current trauma-related symptomatology via completion of an online survey, here we test whether an individual’s perception of their lifetime stress as either controllable or predictable buffered the impact of exposure on trauma-related symptomatology assessed in adulthood. Further, we tested whether this moderation effect differed when evaluated in the context of early childhood, middle childhood, adolescence, and young adulthood stress. Consistent with hypotheses, results highlight both stressor controllability and stressor predictability as buffering the impact of traumatic stress exposure on trauma-related symptomatology and suggest that the potency of this buffering effect varies across unique developmental periods. Leveraging dimensional ratings of lifetime stress exposure to probe heterogeneity in outcomes following stress – and, critically, considering interactions between dimensions of exposure and the developmental period when stress occurred – is likely to yield increased understanding of risk and resilience following traumatic stress.

 

The Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) field campaign provides accurate data for aerosol characterization and trace gas profiles, and establishes knowledge of the relationships between aerosols and water. The dropsonde dataset provides anin situcharacterization of the vertical thermodynamic structure of the atmosphere during 165 research flights by NASA Langley’s King Air research aircraft between February 2020 and June 2022 and four test flights between December 2019 and November 2021. The research flights covered the western North Atlantic region, off the coast of the Eastern United States and around Bermuda and covered all seasons. The dropsonde profiles provide observations of temperature, pressure, relative humidity, and horizontal and vertical winds between the surface and about 9 km. 801 dropsondes were released, of which 796 were processed and 788 provide complete profiles of all parameters between the flight level and the surface with normal parachute performance. Here, we describe the dataset, the processing of the measurements, general statistics, and applications of this rich dataset.

 

High entropy alloy (HEA) nanoparticles hold promise as active and durable (electro)catalysts. Understanding their formation mechanism will enable rational control over composition and atomic arrangement of multimetallic catalytic surface sites to maximize their activity. While prior reports have attributed HEA nanoparticle formation to nucleation and growth, there is a dearth of detailed mechanistic investigations. Here we utilize liquid phase transmission electron microscopy (LPTEM), systematic synthesis, and mass spectrometry (MS) to demonstrate that HEA nanoparticles form by aggregation of metal cluster intermediates. AuAgCuPtPd HEA nanoparticles are synthesized by aqueous co-reduction of metal salts with sodium borohydride in the presence of thiolated polymer ligands. Varying the metal : ligand ratio during synthesis showed that alloyed HEA nanoparticles formed only above a threshold ligand concentration. Interestingly, stable single metal atoms and sub-nanometer clusters are observed by TEM and MS in the final HEA nanoparticle solution, suggesting nucleation and growth is not the dominant mechanism. Increasing supersaturation ratio increased particle size, which together with observations of stable single metal atoms and clusters, supported an aggregative growth mechanism. Direct real-time observation with LPTEM imaging showed aggregation of HEA nanoparticles during synthesis. Quantitative analyses of the nanoparticle growth kinetics and particle size distribution from LPTEM movies were consistent with a theoretical model for aggregative growth. Taken together, these results are consistent with a reaction mechanism involving rapid reduction of metal ions into sub-nanometer clusters followed by cluster aggregation driven by borohydride ion induced thiol ligand desorption. This work demonstrates the importance of cluster species as potential synthetic handles for rational control over HEA nanoparticle atomic structure.