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Formation and Characterization of Gold Nanoparticles

Nanowires have applications in LEDs, lasers, and biochemical sensors.

One-dimensional nanowires are of increasing interest due to their size, physical properties, and other applications in opto-electronics. For example, nanowires have already been used in light-emitting diodes and lasers, photo-detectors, resonant tunneling diodes, field-effect and single-electron transistors, and biochemical sensors. In addition, nanowires are useful because they can be grown almost dislocation-free due to their nano dimension. When dislocation- free nanowires nucleated on the substrate merge to form a continuous film, voids are left underneath, which act as sinks for dislocations, allowing other structures to be grown stress-free on top of the nano-engineered buffer layer.

SEM and AFM images of Gold Nanoparticles annealed at 580 °C for 10 minutes, and varied thicknesses of the gold films. Growth times were (a) 100 s, (b) 200 s, (c) 400 s, (d) 600 s, and (e) 800 s. The top row of images was taken with the SEM at a magnification of about 220,000 times and a working distance of around 14 mm. The second row of images was taken with the AFM at a sample size of 500 nm x 500 nm and vertical scale of 25 nm, with the corresponding 3D view below.
Compared to other growth methods, molecular beam epitaxy’s (MBE) relatively low growth temperature, ultrahigh vacuum, and precise control of beam fluxes reduces impurities and allows for greater control in doping individual nanowires and forming complex junctions. Furthermore, various methods of in situ observation, such as reflection high-energy electron diffraction (RHEED), are relatively easy to implement in an MBE system.

To grow nanowires that would eventually merge into a continuous film, the nanoparticles in this project had to be as large and as close together as possible. MBE was used to grow the gold nanoparticles. The influence of the relative thickness of the initial layer of gold, the annealing temperature, and the annealing time on the size, density, diameter, and height of the gold particles were investigated.

Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were both used to characterize these gold nanoparticles due to their high resolution and precise measurement capabilities. Because of the SEM’s relatively quick scanning rate, it was used to first confirm that nanoparticles were indeed present, while the AFM was used to provide three-dimensional images and the dimensions of the nanoparticles.

Fabrication of gold particles on Si was conducted using a DCA MBE system equipped with a 3.25" substrate heater. Three-inch Si(100) nominal wafers were used as substrates. The samples were quickly heated to 1050 °C to remove the oxide layer, and then quickly cooled under an As4 flux to 500 °C. Finally, the sample was cooled to the nucleation temperature of 230 °C without any flux. After oxide desorption, a thin gold film was deposited and annealed to form nanoparticles. The temperature of the gold effusion cell was set to 1250 °C for a flux of approximately 0.125 nm/s. The time of deposition, annealing temperature, and annealing time were varied. RHEED was used to observe the process in situ. After annealing, some samples were studied using SEM, and all samples were studied with AFM to look at the size and number density of the particles.

Changing the thickness of the initial layer, the annealing temperature, and annealing time had some influence on the size of the nanoparticles, but did not clearly influence the density. The annealing time seemed to have the greatest effect, while the annealing temperature has the smallest effect. In general, annealing for longer times yields smaller particles with a smaller size distribution. However, the exact relationship between the growth parameters and the resulting properties is unclear. Further runs with thicker layers and at higher and lower annealing temperatures and times could be helpful in illuminating the overall trend. For the purpose of forming nanobridges, it was desired that the nanoparticles be as dense and large as possible. Thus, an initial layer 100-nm thick yielded the largest nanoparticles, while annealing at 530 °C and 10 or 15 minutes yielded the densest particles.

This work was done by Glenna R. Yu, Hans M. Guo, and Yuanping Chen of the Army Research Laboratory. ARL-0155

Topics:
Emissions Exhaust emissions Forming Heat treatment Imaging and visualization Lasers Light emitting diodes (LEDs) Manufacturing processes Materials Metallurgy Microscopy Optics Particulate matter (PM)
This Brief includes a Technical Support Package (TSP).
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Evaluating a Data Clustering Approach for Lifecycle Facility Control

(reference ARL-0155) is currently available for download from the TSP library.

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    Defense Tech Briefs Magazine

    This article first appeared in the December, 2013 issue of Defense Tech Briefs Magazine (Vol. 7 No. 6).

    Read more articles from this issue here.

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    Overview

    The document titled "Evaluating a Data Clustering Approach for Life-Cycle Facility Control" discusses the integration of data from various building automation systems to enhance facility management and performance monitoring. With a growing emphasis on sustainability and efficient resource consumption, the need for effective monitoring systems has become increasingly critical. The authors highlight that building monitoring systems often operate as closed-loop systems for security, fire safety, water, electrical, and HVAC (Heating, Ventilation, and Air-Conditioning), but without proper planning, the complexity and volume of data can overwhelm users.

    The study emphasizes the importance of comparing planned versus actual results in terms of cost, risk, and quality control within facility engineering. A structured specification of building space requirements and mechanisms for detecting divergent system and occupant behaviors are essential for effective commissioning, energy efficiency, and maintenance. The U.S. federal government, as a significant player in the capital facility industry, aims to minimize its facility footprint based on current building utilization, necessitating quantifiable metrics for accurate reporting.

    The authors propose a model that connects sensor data with various building models—geometrical, functional, structural, and managerial. This integration allows for the comparison of expected and actual resource utilization, facilitating better decision-making. They developed an algorithm to categorize daily utilization data, which can be compared to expected schedules, thus providing insights into resource usage patterns.

    The document also discusses the unexpected findings regarding the variation in sensor readings, particularly noting that the main office lighting level sensor exhibited low variation due to the influence of ambient light. In contrast, cooling and air handling units showed substantial variation due to seasonal temperature changes.

    Overall, the research presents a promising approach to data clustering that enhances the ability to monitor and manage building resources effectively. By improving data integration and analysis, the proposed methods can lead to better anomaly detection, resource management, and ultimately contribute to the sustainability goals of modern facilities. The findings underscore the necessity of continuous commissioning and the potential for advanced data analytics in optimizing building operations.

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