Spectroscopic Observation and Modeling of Photonic Modes in CeO2 Nanocubes

Photonic modes in dielectric nanostructures, e.g., wide gap semiconductor like CeO2 (ceria), has potential for various applications such as light harvesting and information transmission. To fully understand the properties of such phenomenon in nanoscale, we applied electron energy-loss spectroscopy (EELS) in scanning transmission electron microscope (STEM) to detect such modes in a well-defined ceria nanocube. Through spectra and mapping, we demonstrated a geometrical difference of mode excitation. By comparing various spectra taken at different location relative to the cube, we also showed the transmission properties of the mode. To confirm our observation, we performed EELS simulation with finite-element dielectric calculations in COMSOL Multiphysics. We also revealed the origin of the modes through the calculation. We purposed a simple analytical model to estimate the energy of photonic modes as well. In all, this work gave a fine description of the photonic modes' properties in nanostructures, while demonstrating the advantage of EELS in characterizing optical phenomena in nanoscale

Valence-programmable nanoparticle architectures.

Nanoparticle-based clusters permit the harvesting of collective and emergent properties, with applications ranging from optics and sensing to information processing and catalysis. However, existing approaches to create such architectures are typically system-specific, which limits designability and fabrication. Our work addresses this challenge by demonstrating that cluster architectures can be rationally formed using components with programmable valence. We realize cluster assemblies by employing a three-dimensional (3D) DNA meshframe with high spatial symmetry as a site-programmable scaffold, which can be prescribed with desired valence modes and affinity types. Thus, this meshframe serves as a versatile platform for coordination of nanoparticles into desired cluster architectures. Using the same underlying frame, we show the realization of a variety of preprogrammed designed valence modes, which allows for assembling 3D clusters with complex architectures. The structures of assembled 3D clusters are verified by electron microcopy imaging, cryo-EM tomography and in-situ X-ray scattering methods. We also find a close agreement between structural and optical properties of designed chiral architectures

Forest emissions reduction assessment from airborne LiDAR data using multiple machine learning approaches

Objective: This study aims to evaluate the accuracy of different modeling methods and tree structural parameters extracted from airborne LiDAR for estimating carbon emissions reduction and assess their reliability as Certified Emission Reduction (CER) assessment techniques.Methods: LiDAR data was collected from an afforestation project in Beijing, China. Various modeling methods, including statistical regression and machine learning algorithms, were used to estimate biomass and carbon emissions reduction. The models were evaluated under two schemes: tree-species-specific modeling scheme (Scheme 1) and all-sample modeling scheme (Scheme 2) using cross-validation and compared with ground-based estimations and pre-estimated emission reductions.Results: Totally, the biomass estimation models in scheme 1 showed better accuracy than scheme 2. In scheme 1, The Random Forest (RF) and Cubist models achieved the highest prediction accuracy (R2 = 0.89, RMSE = 22.87 kg, CV RMSE = 52.00 kg), followed by GDBT and Cubist, with SVR and GAM performing the weakest. In scheme 2, Cubist model had the highest accuracy (R2 = 0.75, RMSE = 33.95 kg, CV RMSE = 36.05 kg), followed by RF and GBDT, with SVR and GAM performing the weakest. LiDAR-based estimates of carbon emissions reduction were closer to ground-based estimations and higher than pre-estimated values.Conclusion: This study demonstrates that LiDAR-based models using tree structural parameters can accurately assess carbon emissions reduction. The models outperformed traditional methods in terms of cost and accuracy. Considering tree species in the modeling process improved the accuracy of the models. LiDAR technology has the potential to be a reliable assessment technique for carbon emissions reduction in forestry projects. The pre-trained models can be used for multiple predictions, reducing the cost of carbon sink surveys. Overall, LiDAR-based models provide a promising approach for assessing carbon emissions reduction and can contribute to mitigating climate change

Strain Anisotropy Driven Spontaneous Formation of Nanoscrolls from Two-Dimensional Janus Layers

Two-dimensional Janus transition metal dichalcogenides (TMDs) have attracted attention due to their emergent properties arising from broken mirror symmetry and self-driven polarisation fields. While it has been proposed that their vdW superlattices hold the key to achieving superior properties in piezoelectricity and photovoltiacs, available synthesis has ultimately limited their realisation. Here, we report the first packed vdW nanoscrolls made from Janus TMDs through a simple one-drop solution technique. Our results, including ab-initio simulations, show that the Bohr radius difference between the top sulphur and the bottom selenium atoms within Janus M_Se^S (M=Mo, W) results in a permanent compressive surface strain that acts as a nanoscroll formation catalyst after small liquid interaction. Unlike classical 2D layers, the surface strain in Janus TMDs can be engineered from compressive to tensile by placing larger Bohr radius atoms on top (M_S^Se) to yield inverted C scrolls. Detailed microscopy studies offer the first insights into their morphology and readily formed Moir\'e lattices. In contrast, spectroscopy and FETs studies establish their excitonic and device properties and highlight significant differences compared to 2D flat Janus TMDs. These results introduce the first polar Janus TMD nanoscrolls and introduce inherent strain-driven scrolling dynamics as a catalyst to create superlattices

Ambipolar ferromagnetism by electrostatic doping of a manganite

Complex-oxide materials exhibit physical properties that involve the interplay of charge and spin degrees of freedom. However, an ambipolar oxide that is able to exhibit both electron-doped and hole-doped ferromagnetism in the same material has proved elusive. Here we report ambipolar ferromagnetism in LaMnO3, with electron–hole asymmetry of the ferromagnetic order. Starting from an undoped atomically thin LaMnO3 film, we electrostatically dope the material with electrons or holes according to the polarity of a voltage applied across an ionic liquid gate. Magnetotransport characterization reveals that an increase of either electron-doping or hole-doping induced ferromagnetic order in this antiferromagnetic compound, and leads to an insulator-to-metal transition with colossal magnetoresistance showing electron–hole asymmetry. These findings are supported by density functional theory calculations, showing that strengthening of the inter-plane ferromagnetic exchange interaction is the origin of the ambipolar ferromagnetism. The result raises the prospect of exploiting ambipolar magnetic functionality in strongly correlated electron systems

Skeletal muscle O-GlcNAc transferase is important for muscle energy homeostasis and whole-body insulin sensitivity

Objective: Given that cellular O-GlcNAcylation levels are thought to be real-time measures of cellular nutrient status and dysregulated O-GlcNAc signaling is associated with insulin resistance, we evaluated the role of O-GlcNAc transferase (OGT), the enzyme that mediates O-GlcNAcylation, in skeletal muscle. Methods: We assessed O-GlcNAcylation levels in skeletal muscle from obese, type 2 diabetic people, and we characterized muscle-specific OGT knockout (mKO) mice in metabolic cages and measured energy expenditure and substrate utilization pattern using indirect calorimetry. Whole body insulin sensitivity was assessed using the hyperinsulinemic euglycemic clamp technique and tissue-specific glucose uptake was subsequently evaluated. Tissues were used for histology, qPCR, Western blot, co-immunoprecipitation, and chromatin immunoprecipitation analyses. Results: We found elevated levels of O-GlcNAc-modified proteins in obese, type 2 diabetic people compared with well-matched obese and lean controls. Muscle-specific OGT knockout mice were lean, and whole body energy expenditure and insulin sensitivity were increased in these mice, consistent with enhanced glucose uptake and elevated glycolytic enzyme activities in skeletal muscle. Moreover, enhanced glucose uptake was also observed in white adipose tissue that was browner than that of WT mice. Interestingly, mKO mice had elevated mRNA levels of Il15 in skeletal muscle and increased circulating IL-15 levels. We found that OGT in muscle mediates transcriptional repression of Il15 by O-GlcNAcylating Enhancer of Zeste Homolog 2 (EZH2). Conclusions: Elevated muscle O-GlcNAc levels paralleled insulin resistance and type 2 diabetes in humans. Moreover, OGT-mediated signaling is necessary for proper skeletal muscle metabolism and whole-body energy homeostasis, and our data highlight O-GlcNAcylation as a potential target for ameliorating metabolic disorders. Keywords: O-GlcNAc signaling, Type 2 diabetes, N-acetyl-d-glucosamine, Tissue cross talk, Epigenetic regulation of Il15 transcription, Insulin sensitivit