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The ability to image light elements in soft matter at atomic resolution enables unprecedented insight into the structure and properties of molecular heterostructures and beam-sensitive nanomaterials. In this study, we introduce a scanning transmission electron microscopy technique combining a pre-specimen phase plate designed to produce a probe with structured phase with a high-speed direct electron detector to generate nearly linear contrast images with high efficiency. We demonstrate this method by using both experiment and simulation to simultaneously image the atomic-scale structure of weakly scattering amorphous carbon and strongly scattering gold nanoparticles. Our method demonstrates strong contrast for both materials, making it a promising candidate for structural determination of heterogeneous soft/hard matter samples even at low electron doses comparable to traditional phase-contrast transmission electron microscopy. Simulated images demonstrate the extension of this technique to the challenging problem of structural determination of biological material at the surface of inorganic crystals. Scanning transmission electron microscopy is a powerful material probe, but constrained to large atomic number samples due to the issues of beam damage and weak scattering. Here, Ophus et al. propose a method that produces linear phase contrast in a focused electron beam to image dose-sensitive objects.

Agglomeration and sedimentation of nanoparticles (NPs) within biological solutions is a major limitation in their use in many downstream applications. It has been proposed that serum proteins associate with the NP surface to form a protein corona that limits agglomeration and sedimentation. Here, we investigate the effect of fetal bovine serum (FBS) proteins on the dispersion stability, dosimetry, and NP-induced cytotoxicity of cationic zinc oxide nanoparticles (nZnO) synthesized via forced hydrolysis with a core size of 10 nm. Two different in vitro cell culture models, suspension and adherent, were evaluated by comparing a phosphate buffered saline (PBS) nZnO dispersion (nZnO/PBS) and an FBS-stabilized PBS nZnO dispersion (nZnO – FBS/PBS). Surface interactions of FBS on nZnO were analyzed via spectroscopic and optical techniques. Fourier transformed infrared spectroscopy (FTIR) confirmed the adsorption of negatively charged protein components on the cationic nZnO surface through the disappearance of surfaced-adsorbed carboxyl functional groups and the subsequent detection of vibrational modes associated with the protein backbone of FBS-associated proteins. Further confirmation of these interactions was noted in the isoelectric point shift of the nZnO from the characteristic pH of 9.5 to a pH of 6.1. In nZnO – FBS/PBS dispersions, the FBS reduced agglomeration and sedimentation behaviors to impart long-term improvements (>24 h) to the nZnO dispersion stability. Furthermore, mathematical dosimetry models indicate that nZnO – FBS/PBS dispersions had consistent NP deposition patterns over time unlike unstable nZnO/PBS dispersions. In suspension cell models, the stable nZnO – FBS/PBS dispersion resulted in a ~33 % increase in the NP-induced cytotoxicity for both Jurkat leukemic and Hut-78 lymphoma cancer cells. In contrast, the nZnO – FBS/PBS dispersion resulted in 49 and 71 % reductions in the cytotoxicity observed towards the adherent breast (T-47D) and prostate (LNCaP) cancer cell lines, respectively. Presence of FBS in the NP dispersions also increased the reactive oxygen species generation. These observations indicate that the improved dispersion stability leads to increased NP bioavailability for suspension cell models and reduced NP sedimentation onto adherent cell layers resulting in more accurate in vitro toxicity assessments.

Nitrogen (N) is the most limiting nutrient in cereal production, yet its use efficiency remains very low at only 35%. Nutrient use efficiency (NUE) is crucial for increasing crop yield and quality while reducing fertilizer inputs and minimizing environmental damage. Optimum N rates that maximize yield without reducing NUE have been found to vary from location to location. This field study assessed the effect of N rates on the yield and quality of spring wheat (Triticum aestivum L.) at five locations in southern Idaho in 2015–2017. Nitrogen was applied as urea (46–0–0) immediately after planting at five rates: 0, 84, 168, 252, and 336 kg ha–1. Nitrogen application improved grain quality (increased protein) even when no increase in yield was noted. Nitrogen use efficiency and N uptake were affected by N rate at only 2 and 4 of 14 site‐years, respectively. These observations highlight the challenging task of pinpointing the appropriate N rates for optimizing wheat yield, grain protein, N uptake and NUE; and the importance of adjusting N rates based on location, year, and prevalent environmental conditions. Core Ideas Application of all N fertilizer at planting is not efficient for wheat. Nitrogen fertilizer rates for wheat should account for site‐ and year‐specific conditions. When prescribing N rates to wheat, yield potential and responsiveness to N should be considered. Higher N rates resulted in enhanced grain protein content, but low N use efficiency.

The accuracy of the traditional soil and plant‐based techniques for assessing sugarbeet demand for nitrogen (N) and yield prediction is generally low. Refining N and irrigation water management is a key to maximizing return for sugarbeet (Beta vulgaris L.) growers from agronomic, economic, and environmental perspective. The use of Normalized Difference Vegetative Index (NDVI) in combination with the unmanned aerial vehicle (UAV)‐based data collection for in‐season estimation of sugarbeet root yield and sugar concentration has potential for precision N management. Sugarbeet field trials were conducted in Idaho in 2019 and 2020 to assess (1) effects of water and N fertilizer rates on yield and estimated recoverable sugar (ERS) and (2) feasibility of predicting root yield and ERS using UAV NDVI. At the lowest N rate, application of water at 100% level resulted in greater yield, compared to 50%, in both years. At higher N rates, 50% level produced higher yields. At each N level, application of water at 100% level resulted in lower ERS, compared to 50%. The UAV NDVI was strongly correlated with root yield and ERS. The relationship between UAV NDVI and root yield and ERS was stronger in July (60 days after planting) compared to June (40 days after planting). Estimating the yield and ERS potential in late June/early July and topdressing the crop before the end of July may help to improve N use efficiency while optimizing sugarbeet production. Core Idea The UAV‐based NDVI was strongly correlated with sugarbeet root yield and the estimated recoverable sugar (ERS). The relationship between UAV NDVI and root yield and ERS was stronger in July (60 days after planting), compared to June (40 days after planting). Determining the sugarbeet crop yield and ERS potential in late June/early July and topdressing before the end of July may help to improve N use efficiency while optimizing production.

Many studies throughout the world have shown positive responses of various crops to silicon (Si) application in terms of plant health, nutrient uptake, yield, and quality. Although not considered an essential element for plant growth, Si has been recently recognized as a “beneficial substance” or “quasi-essential” due to its important role in plant nutrition, especially notable under stressed conditions. The goal of this study was to evaluate the effect of Si on wheat plant height, grain yield (GY), and grain protein content (GP). The experiment was conducted during two consecutive growing seasons in Idaho. A split-plot experimental design was used with three Si fertilization rates (140, 280, and 560 kg Si ha−1) corresponding to 100, 50, and 25% of manufacturer-recommended rates and two application times—at planting and tillering (Feekes 5). MontanaGrowTM (0-0-5) by MontanaGrow Inc. (Bonner, MT, USA) used in this study is a Si product sourced from a high-energy amorphous (non-crystalized) volcanic tuff. There was no significant effect of Si rate and application time on plant height, nutrient uptake, GY, or GP of irrigated winter wheat grown in non-stressed conditions. These results could be directly related to the Si fertilizer source used in the study. We are planning to further evaluate Si’s effect on growth and grain production of wheat grown in non-stressed vs. stressed conditions utilizing several different Si sources and application methods.

Nitrogen and water are two key factors for wheat production due to their major roles in plant growth and development, photosynthesis, yield, and grain protein content. Plant uptake of water and N is fundamentally interactive. Our objectives were: (a) to analyze the effects of different irrigation (IR) and N rates on spring wheat (Triticum aestivum L.) yield and grain protein, and spectral indices (relative greenness [SPAD] and Normalized Difference Vegetation Index [NDVI]), and (b) to identify the optimum IR and N requirements for wheat grain production in semi‐arid conditions of Montana and Idaho. This article details the results from field experiments conducted at three locations for two growing seasons (6 site‐years). Relative greenness measured by SPAD chlorophyll meter was used to assess plant N status, whereas NDVI was used for both plant N status and estimation of wheat yield. Both SPAD and NDVI values increased as N and IR application rates increased. The SPAD and NDVI values explained 80 and 84% of the variation in wheat yield, respectively. We found that IR at 75% of evapotranspiration (ET) throughout the growing season is adequate to optimize wheat yield and grain protein. Nitrogen rate was not correlated with wheat yield at any of the site‐years. Based on this study's results, approximately 150 kg N ha−1 (total, soil residual N plus N added as fertilizer) may be sufficient to optimize yield and grain protein content of irrigated spring wheat in semi‐arid cropping systems.

Precision agriculture provides efficient means of obtaining real‐time data to guide nitrogen (N) management based on predicted crop profitability. This study was conducted to assess the efficacy of using in‐season measurements (plant height, biomass weight, biomass N, soil plant analysis development [SPAD], GreenSeeker [GS] normalized difference vegetative index [NDVI], and unmanned aerial vehicle [UAV] NDVI) at Feekes 5 (tillering) and Feekes 10 (anthesis) to estimate wheat (Triticum aestivum L.) yield and protein. The secondary aim was to determine whether the accuracy of yield and protein prediction varies by wheat class and cultivar. Six cultivars—hard red spring (HRS) wheat ‘Jefferson’ and ‘SY Basalt’, hard white spring (HWS) wheat ‘Dayn’ and ‘UI Platinum’, and soft white spring (SWS) wheat ‘Seahawk’ and ‘UI Stone’—were planted at two locations in Idaho in 2018–2020. Plots were arranged in a randomized complete block design with four replications with each cultivar evaluated at seven N rates (0, 50, 100, 150, 200, 250, and 300 kg N ha–1). The determination of the Pearson correlation coefficients revealed that all parameters were linearly correlated with yield except for SPAD at Feekes 5 and biomass weight at Feekes 10. Although estimation of in‐season grain protein remains a challenge, NDVI was strongly correlated with yield especially at Feekes 5. The accuracy of yield prediction was similar for all wheat classes. Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI. Both hand‐held and aerial‐based spectral measurements could be used to prescribe N rates to be applied during tiller formation when wheat yield can be optimized. Core Ideas GreenSeeker (GS) and UAV NDVI at Feekes 5 shows the most potential for in‐season wheat yield estimation. Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI. The accuracy of yield estimation was comparable for all wheat classes and cultivars. Estimation of in‐season wheat protein content remains a challenge.

Accurate sensor-based prediction of crop yield and grain quality in-season would enable growers to adjust nitrogen (N) fertilizer management for optimized production. This study assessed the feasibility (and compared the accuracy) of wheat (Triticum aestivum L.) yield, grain N uptake, and protein content prediction with in-season crop spectral reflectance measurements (Normalized Difference Vegetative Index, NDVI) obtained with a handheld GreenSeeker (GS) sensor and an Unmanned Aerial Vehicle (UAV)-mounted sensor. A strong positive correlation was observed between GS NDVI and UAV NDVI at Feekes 5 (R2 = 0.78) and Feekes 10 (R2 = 0.70). At Feekes 5, GS NDVI and UAV NDVI explained 42% and 43% of wheat yield, respectively. The correlation was weaker at Feekes 10 (R2 of 0.34 and 0.25 for GS NDVI and UAV NDVI, respectively). The accuracy of wheat grain N uptake prediction was comparable to that of yield: the R2 values for GS NDVI and UAV NDVI were 0.53 and 0.37 at Feekes 5 and 0.13 and 0.20 at Feekes 10. We found that neither GS NDVI nor UAV NDVI in-season data were useful in prediction of wheat grain protein content. In conclusion, wheat yield and grain N uptake can be estimated at Feekes 5 using either handheld or aerial based NDVI with comparable accuracy.