Predictability of localized plasmonic responses in nanoparticle assemblies

Design of nanoscale structures with desired nanophotonic properties are key tasks for nanooptics and nanophotonics. Here, the correlative relationship between local nanoparticle geometries and their plasmonic responses is established using encoder-decoder neural networks. In the im2spec network, the correlative relationship between local particle geometries and local spectra is established via encoding the observed geometries to a small number of latent variables and subsequently decoding into plasmonic spectra; in the spec2im network, the relationship is reversed. Surprisingly, these reduced descriptions allow high-veracity predictions of the local responses based on geometries for fixed compositions and chemical states of the surface. The analysis of the latent space distributions and the corresponding decoded and closest (in latent space) encoded images yields insight into the generative mechanisms of plasmonic interactions in the nanoparticle arrays. Ultimately, this approach creates a path toward determining configurations that can yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures

Establishment of Passive Energy Conservation Measure and Economic Evaluation of Fenestration System in Nonresidential Building of Korea

ECO2 (building energy efficiency rating program) and passive energy conservation measures (ECMs) were established as a basic study for targeted methodologies and decision support systems development in Korea to meet national regulations. The primary energy consumption and economic evaluation of nonresidential buildings was performed. Passive ECMs were classified as planning and performance elements. The planning elements are the window-to-wall ratio (WWR) and horizontal shading angle. The performance elements are the thermal transmittance (U-value) of the walls, roof, and floor and the U-value and solar heat gain coefficient (SHGC) of windows. This study focused on the window-to-wall ratio and the U-value and solar heat gain coefficient of windows. An economic efficiency database for the constructed alternatives was built; the target building was set and the Passive ECM List for the target building was derived. The energy consumption evaluation and economic evaluation were performed for each of the constructed alternatives, and a methodology for guiding energy efficiency decisions was proposed based on the performance evaluation results, and the optimal Passive ECM List for the target building was derived

Sculpting the plasmonic responses of nanoparticles by directed electron beam irradiation

Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomena and in multiple applications including plasmonics, optoelectronics, and sensing. In parallel, the emergence of monochromated electron energy loss spectroscopy (EELS) has enabled exploration of local nanoplasmonic functionalities within single nanoparticles and the collective response of nanoparticle assemblies, providing deep insight into the associated mechanisms. However, modern synthesis processes for plasmonic nanostructures are often limited in the types of accessible geometry and materials, and even then, limited to spatial precisions on the order of tens of nm, precluding the direct exploration of critical aspects of the structure-property relationships. Here, we use the atomic-sized probe of the scanning transmission electron microscope (STEM) to perform precise sculpting and design of nanoparticle configurations. Furthermore, using low-loss (EELS), we provide dynamic analyses of evolution of the plasmonic response during the sculpting process. We show that within self-assembled systems of nanoparticles, individual nanoparticles can be selectively removed, reshaped, or arbitrarily patterned with nanometer-level resolution, effectively modifying the plasmonic response in both space and energy domains. This process significantly increases the scope for design possibilities and presents opportunities for arbitrary structure development, which are ultimately key for nanophotonic design. Nanosculpting introduces yet another capability to the electron microscope.This effort is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (K.M.R., S.V.K.) S.H.C and D.J.M. acknowledge (NSF CHE-1905263, and CDCM, an NSF MRSEC DMR-1720595), the Welch Foundation (F-1848), and the Fulbright Program (IIE-15151071). Electron microscopy was performed using instrumentation within ORNL’s Materials Characterization Core provided by UT-Battelle, LLC, under Contract No. DE-AC05- 00OR22725 with the DOE and sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy.Center for Dynamics and Control of Material

Universal Gelation of Metal Oxide Nanocrystals via Depletion Attractions

Nanocrystal gelation provides a powerful framework to translate nanoscale properties into bulk materials and to engineer emergent properties through the assembled microstructure. However, many established gelation strategies rely on chemical reactions and specific interactions, e.g., stabilizing ligands or ions on the surface of the nanocrystals, and are therefore not easily transferrable. Here, we report a general gelation strategy via non-specific and purely entropic depletion attractions applied to three types of metal oxide nanocrystals. The gelation thresholds of two compositionally distinct spherical nanocrystals agree quantitatively, demonstrating the adaptability of the approach for different chemistries. Consistent with theoretical phase behavior predictions, nanocrystal cubes form gels at a lower polymer concentration than nanocrystal spheres, allowing shape to serve as a handle to control gelation. These results suggest that the fundamental underpinnings of depletion-driven assembly, traditionally associated with larger colloidal particles, are also applicable at the nanoscale

Assembly of Linked Nanocrystal Colloids by Reversible Covalent Bonds

The use of dynamically bonding molecules designed to reversibly link solvent-dispersed nanocrystals (NCs) is a promising strategy to form colloidal assemblies with controlled structure and macroscopic properties. In this work, tin-doped indium oxide NCs are functionalized with ligands that form reversible covalent bonds with linking molecules to drive assembly of NC gels. We monitor gelation using small angle X-ray scattering and characterize how changes in the gel structure affect infrared optical properties arising from the localized surface plasmon resonance of the NCs. The assembly is reversible because of the designed linking chemistry, and we disassemble the gels using two strategies: addition of excess NCs to change the ratio of linking molecules to NCs and addition of a capping molecule that displaces the linking molecules. The assembly behavior is rationalized using a thermodynamic perturbation theory to compute the phase diagram of the NC–linking molecule mixture. Coarse-grained molecular dynamics simulations reveal the competition between loop and bridge linking motifs essential for understanding NC gelation. This combined experimental, computational, and theoretical work provides a platform for controlling and designing the properties of reversible colloidal assemblies that incorporate NC and solvent compositions beyond those compatible with other contemporary (e.g, DNA-based) linking strategies.We would like to acknowledge the UT Mass Spectrometry Facility for their instrumental help and the UT NMR facilities for equipment use and assistance: NIH Grant Number 1 S10 OD021508-01. This work was primarily supported by the National Science Foundation through the Center for Dynamics and Control of Materials: an NSF Materials Research Science and Engineering Center (NSF MRSEC) under Cooperative Agreement DMR-1720595. This work was also supported by NSF Graduate Research Fellowships DGE-1610403 (M.N.D. and S.V.), an Arnold O. Beckman Postdoctoral Fellowship (Z.M.S.), NSF (CHE- 1905263), and the Welch Foundation (F-1848 and F-1696). E.V.A. acknowledges support from the Welch Regents Chair (F-0046). We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources.Center for Dynamics and Control of Material

Cetaceans evolution:insights from the genome sequences of common minke whales

Background: Whales have captivated the human imagination for millennia. These incredible cetaceans are the only mammals that have adapted to life in the open oceans and have been a source of human food, fuel and tools around the globe. The transition from land to water has led to various aquatic specializations related to hairless skin and ability to regulate their body temperature in cold water. Results: We present four common minke whale (Balaenoptera acutorostrata) genomes with depth of ×13 ~ ×17 coverage and perform resequencing technology without a reference sequence. Our results indicated the time to the most recent common ancestors of common minke whales to be about 2.3574 (95% HPD, 1.1521 - 3.9212) million years ago. Further, we found that genes associated with epilation and tooth-development showed signatures of positive selection, supporting the morphological uniqueness of whales. Conclusions: This whole-genome sequencing offers a chance to better understand the evolutionary journey of one of the largest mammals on earth

Infrared plasmonic doped metal oxide nanocubes

Localized surface plasmon resonance (LSPR) in semiconductor nanocrystals (NCs) that results in resonant absorption, scattering, and near field enhancement around the NC can be tuned across a wide optical spectral range from visible to far-infrared by synthetically varying doping level. Cube-shaped NCs of conventional metals like gold and silver generally exhibit LSPR in the visible region with spectral modes determined by their faceted shapes. However, faceted NCs exhibiting LSPR response in the infrared (IR) region are relatively rare. We describe the colloidal synthesis of nanoscale fluorine-doped indium oxide (F:In₂O₃) cubes with LSPR response in the IR region, wherein fluorine was found to both direct the cubic morphology and act as an aliovalent dopant. The presence of fluorine was found to impart higher stabilization to the (100) facets, suggesting that the cubic morphology results from surface binding of F-atoms. In addition, fluorine acts as an anionic aliovalent dopant in the cubic bixbyite lattice of In₂O₃, introducing a high concentration of free electrons leading to LSPR. The cubes exhibit narrow, shape-dependent multimodal LSPR extinction peaks due to corner- and edge-centered modes. The spatial origin of these different contributions to the spectral response are directly visualized by electron energy loss spectroscopy (EELS) in a scanning transmission electron microscope (STEM). A synthetic challenge in faceted metal oxide NCs is realizing tunable LSPR near-field response in the IR. We expand to colloidal synthesis of fluorine, tin co-doped indium oxide (F,Sn:In₂O₃) NC cubes with tunable IR range LSPR. Free carrier concentration is tuned through controlled Sn dopant incorporation, where Sn is an aliovalent n-type dopant in the In₂O₃ lattice. Monolayer NC arrays are fabricated through liquid-air interface assembly, NC film nanocavities with heightened near-field enhancement (NFE). The tunable F,Sn:In₂O₃ NC near-field is coupled with PbS quantum dots, via the Purcell effect. The detuning frequency between the nanocavity and exciton is varied, resulting in IR near-field dependent enhanced exciton lifetime decayChemical Engineerin

Machine-Learning-Based Coefficient of Performance Prediction Model for Heat Pump Systems

In a heat pump system, performance is an important indicator that should be monitored for system optimization, fault diagnosis, and operational efficiency improvement. Real-time performance measurement and monitoring during heat pump operation is difficult because expensive performance measurement devices or additional installation of various monitoring sensors required for performance calculation are required. When using a data-based machine-learning model, it is possible to predict and monitor performance by finding the relationship between input and output values through an existing sensor. In this study, the performance prediction model of the air-cooled heat pump system was developed and verified using artificial neural network, support vector machine, random forest, and K-nearest neighbor model. The operation data of the heat pump system installed in the university laboratory was measured and a prediction model for each machine-learning stage was developed. The mean bias error analysis is −3.6 for artificial neural network, −5 for artificial neural network, −7.7 for random forest, and −8.3 for K-nearest neighbor. The artificial neural network model with the highest accuracy and the shortest calculation time among the developed prediction models was applied to the Building Automation System to enable real-time performance monitoring and to confirm the field applicability of the developed model

Machine-Learning-Based Coefficient of Performance Prediction Model for Heat Pump Systems

In a heat pump system, performance is an important indicator that should be monitored for system optimization, fault diagnosis, and operational efficiency improvement. Real-time performance measurement and monitoring during heat pump operation is difficult because expensive performance measurement devices or additional installation of various monitoring sensors required for performance calculation are required. When using a data-based machine-learning model, it is possible to predict and monitor performance by finding the relationship between input and output values through an existing sensor. In this study, the performance prediction model of the air-cooled heat pump system was developed and verified using artificial neural network, support vector machine, random forest, and K-nearest neighbor model. The operation data of the heat pump system installed in the university laboratory was measured and a prediction model for each machine-learning stage was developed. The mean bias error analysis is −3.6 for artificial neural network, −5 for artificial neural network, −7.7 for random forest, and −8.3 for K-nearest neighbor. The artificial neural network model with the highest accuracy and the shortest calculation time among the developed prediction models was applied to the Building Automation System to enable real-time performance monitoring and to confirm the field applicability of the developed model