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Abstract Surface functionalization and colloidal stability are pivotal for numerous applications of gold nanoparticles (Au‐NPs). Over the past decade, N‐heterocyclic carbenes (NHCs) have emerged as promising ligands for stabilizing Au‐NPs owing to their ease of synthesis, structural diversity, and strong metal‐ligand bonds. Here, we introduce new Au(I)–NHCcopolymer scaffolds as precursors to multidentate NHC‐protected Au‐NPs. Ring‐opening metathesis copolymerization of a norbornene‐appended Au(I)−NHC complex with another functionalized norbornene comonomer provides NHC–Au(I) copolymers with modular compositions and structures. Upon reduction, these copolymers yield multidentate polyNHC‐coated Au‐NPs with varied properties and corona functionalities dictated by the secondary monomer. These nanoparticles exhibit excellent size homogeneity and stability against aggregation in various buffers, cell culture media, and under exposure to electrolytes, oxidants, and exogenous thiols over extended periods. Moreover, we demonstrate post‐synthetic surface functionalization reactions of polyNHC−Au‐NPs while maintaining colloidal stability, highlighting their robustness and potential for applications such as bioconjugation. Overall, these findings underscore the potential of ROMP‐derived NHC‐containing copolymers as highly tunable and versatile multidentate ligands that may be suitable for other inorganic colloids and flat surfaces. 

Engaging the public is a common practice in science disciplines and is deeply rooted in the discipline of entomology. These efforts to engage specific target groups within the general public are well studied, especially extension efforts to engage farmers and agricultural stakeholders, but this is not the case for K-12 educational spaces. Here, we conducted a scoping review to (1) determine the characteristics of entomology outreach efforts engaging K-12 populations and (2) identify opportunities for improvement based on the synthesis of those characteristics. We systematically searched five databases to identify 42 publications relevant to the parameters of this project. Analysis of characteristics indicated that entomology outreach efforts in K-12 classrooms tend to be reflective, are more often published in educationally focused journals, and rarely evaluate the interventions employed. Opportunities for improvement were identified from these trends, and from them we suggest that the practice of K-12 outreach benefits from (i) publishing in entomology-focused journals, (ii) including non-academic authors, (iii) evaluating interventions, (iv) including student data, and (v) considering axes of diversity and inclusion. 

Studying moths is an excellent way to include students in science practices by introducing them to a ubiquitous but under-appreciated animal group that can be found in their local places, including urban, suburban, agricultural, forested, and other habitats. In this paper, we share a simple, low-cost method that can allow individual students or groups to collect moth specimens and begin to ask and answer questions about moth diversity and abundance in their local community. 

The proportions of carbon (C), nitrogen (N), and phosphorus (P) in surface ocean particulate matter deviate greatly from the canonical Redfield Ratio (C:N:P = 106:16:1) in space and time with significant implications for global carbon storage as this matter reaches the deep ocean. Recent work has revealed clear latitudinal patterns in C:N:P, yet the relative importance of ecological, physiological, or biochemical processes in creating these patterns is unclear. We present high-resolution, concurrent measurements of particulate C:N:P, macromolecular composition, environmental conditions, and plankton community composition from a transect spanning a subtropical-subpolar boundary, the North Pacific Transition Zone. We find that the summed contribution of macromolecules to particulate C, N, and P is consistent with, and provides interpretation for, particulate C:N:P patterns. A decline in particulate C:N from the subtropical to subpolar North Pacific largely reflects an increase in the relative contribution of protein compared to carbohydrate and lipid, whereas variation in C:P and N:P correspond to shifts in protein relative to polyphosphate, DNA, and RNA. Possible causes for the corresponding trends in C:N and macromolecular composition include physiological responses and changes in community structure of phytoplankton, which represented approximately 1/3^rdof particulate C across the transect. Comparison with culture experiments and an allocation-based model of phytoplankton macromolecular composition suggest that physiological acclimation to changing nutrient supply is the most likely explanation for the latitudinal trend in C:N, offering both a mechanistic interpretation and biochemical basis for large-scale patterns in C:N:P. 

Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances. 

Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water Assessment Tool (SWAT) for predicting streamflow in the Rio Grande Headwaters near Del Norte, a snowmelt-dominated mountainous watershed of the Upper Rio Grande Basin. Remotely sensed data were used for the random forest machine learning analysis (RFML) and RStudio for data processing and synthesizing. The RFML model outperformed the SWAT model in accuracy and demonstrated its capability in predicting streamflow in this region. We implemented a customized approach to the RFR model to assess the model’s performance for three training periods, across 1991–2010, 1996–2010, and 2001–2010; the results indicated that the model’s accuracy improved with longer training periods, implying that the model trained on a more extended period is better able to capture the parameters’ variability and reproduce streamflow data more accurately. The variable importance (i.e., IncNodePurity) measure of the RFML model revealed that the snow depth and the minimum temperature were consistently the top two predictors across all training periods. The paper also evaluated how well the SWAT model performs in reproducing streamflow data of the watershed with a conventional approach. The SWAT model needed more time and data to set up and calibrate, delivering acceptable performance in annual mean streamflow simulation, with satisfactory index of agreement (d), coefficient of determination (R2), and percent bias (PBIAS) values, but monthly simulation warrants further exploration and model adjustments. The study recommends exploring snowmelt runoff hydrologic processes, dust-driven sublimation effects, and more detailed topographic input parameters to update the SWAT snowmelt routine for better monthly flow estimation. The results provide a critical analysis for enhancing streamflow prediction, which is valuable for further research and water resource management, including snowmelt-driven semi-arid regions. 

Abstract Supramolecular polymer networks contain non-covalent cross-links that enable access to broadly tunable mechanical properties and stimuli-responsive behaviors; the incorporation of multiple unique non-covalent cross-links within such materials further expands their mechanical responses and functionality. To date, however, the design of such materials has been accomplished through discrete combinations of distinct interaction types in series, limiting materials design logic. Here we introduce the concept of leveraging “nested” supramolecular crosslinks, wherein two distinct types of non-covalent interactions exist in parallel, to control bulk material functions. To demonstrate this concept, we use polymer-linked Pd₂L₄metal–organic cage (polyMOC) gels that form hollow metal–organic cage junctions through metal–ligand coordination and can exhibit well-defined host-guest binding within their cavity. In these “nested” supramolecular network junctions, the thermodynamics of host-guest interactions within the junctions affect the metal–ligand interactions that form those junctions, ultimately translating to substantial guest-dependent changes in bulk material properties that could not be achieved in traditional supramolecular networks with multiple interactions in series. 

While Si-containing polymers can often be deconstructed using chemical triggers such as fluoride, acids, and bases, they are resistant to cleavage by mild reagents such as biological nucleophiles, thus limiting their end-of-life options and potential environmental degradability. Here, using ring-opening metathesis polymerization, we synthesize terpolymers of (1) a “functional” monomer ( e.g. , a polyethylene glycol macromonomer or dicyclopentadiene); (2) a monomer containing an electrophilic pentafluorophenyl (PFP) substituent; and (3) a cleavable monomer based on a bifunctional silyl ether . Exposing these polymers to thiols under basic conditions triggers a cascade of nucleophilic aromatic substitution (S N Ar) at the PFP groups, which liberates fluoride ions, followed by cleavage of the backbone Si–O bonds, inducing polymer backbone deconstruction. This method is shown to be effective for deconstruction of polyethylene glycol (PEG) based graft terpolymers in organic or aqueous conditions as well as polydicyclopentadiene (pDCPD) thermosets, significantly expanding upon the versatility of bifunctional silyl ether based functional polymers.