Composition and Manufacturing Effects on Electrical Properties of Li/FeS2 Thermal Battery Cathodes

Li/FeS2 thermal batteries provide a stable, robust, and reliable power source capable of long-term electrical energy storage without performance degradation. These systems rely on a eutectic salt that melts at elevated temperature, activating the cell. When the electrolyte melts, the cathode becomes a suspension, with cathode particles suspended in a molten salt. The suspension experiences mechanical deformation, or slumping.\u27 This slump changes the mechanical compression of the cell, as well as the tortuosity and electronic and ionic conductivity of the cell as the cathode mesostructure is reordered in response to the external compressive stress. The combined effect of deformation, component composition, and manufacturing conditions on electrical conductivity has not been studied, yet the cathode electrical properties are critically important to battery performance. This thesis presents simulation results from a computer model in combination with experiments to elucidate the effects of electrical conductivity in FeS2 cathode pellets when composition and manufacturing parameters are varied. Experiments applied impedance spectroscopy measurements of pressed-powder cathode pellets before and after slumping. Pellets were manufactured with variations in pellet density, FeS2 particle size distribution, and FeS2 content. The results showed that prior to slumping, the electrical conductivity increased with pellet density and FeS2 content. After slumping, pellets exhibited greater electrical conductivity, but the effects of processing parameters appear to have been erased, at least within the ranges tested. The conformal decomposition finite element method (CDFEM) was applied to surface-meshed geometric representations of cathode microstructures generated from microcomputed tomography reconstructions. Results from the SIERRA/Aria finite element code indicate that the selected processing and composition parameters do not provide a clear trend on the preslumped electrical conductivity, but density slightly affected the postslumped conductivity. These results indicate that the simulations lacked fidelity compared to experiments. However, the simulations combined with experimental data provide a fundamental look at the effects of processing and composition on thermal battery microstructure and electrical conductivity. The understanding of manufacturing effects on battery performance is not well developed, and this effort represents a step forward in correlated and predicting performance of cells based upon observed manufacturing trends.\u2

New Methods Visualizing Mesostructured Materials

On the one hand this work intends to present new possibilities on how the combination of characterization methods can be used to gain information not available from the individual techniques. On the other hand discrete tomography - a relatively new method in materials science - is used to image real three-dimensional nano structures with a resolution of only a few nanometers. Visualization not only facilitates the interpretation of scientific results, but also aims at contributing to a better general understanding of nano technology

An Intelligent Classification System For Aggregate Based On Image Processing And Neural Network

Bentuk dan tekstur permukaan aggregat mempengaruhi kekuatan dan struktur konkrit. Secara tradisi, mesin pengayakan mekanikal dan pengukuran manual digunakan bagi menentukan kedua-dua saiz dan bentuk aggregat. Aggregate’s shape and surface texture immensely influence the strength and structure of the resulting concrete. Traditionally, mechanical sieving and manual gauging are used to determine both the size and shape of the aggregates

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Gas chromatography on self assembled single walled carbon nanotubes

Carbon nanotubes (CNTs) are nano-sized carbon-based sorbents, which have high surface area, large aspect ratio, can be self-assembled and are known to be stable at high temperatures. It is therefore conceivable that separation techniques, such as, gas chromatography (GC) can benefit from their unique properties and nano-scale interactions. Self-assembly, in-contrast to packing these materials in a tube, prevents them from agglomeration and thus facilitates in retaining their nano-characteristics. In this research, novel substrates, such as, steel tubings, on a scaled-up level have been explored for the self-assembly process of CNTs, for applications such as gas chromatography, where the CNTs served as stationary phases. In the first part of this research, the self-assembly of multi-walled carbon nanotubes (MWCNTs) on the inside wall of long stainless steel tubings was studied. The CNTs were deposited by the chemical vapor deposition (CVD) using ethylene as the carbon source and the iron nanostructures in the stainless steel as the catalyst. Variation in uniformity in terms of the thickness and morphology of the deposited film and surface coverage were studied along the length of a tube by scanning electron microscopy (SEM). The effects of process conditions, such as flow rate and deposition time on the coating thickness, were studied. The catalytic effect of the iron nanostructures depended on surface conditioning of the tubing. It was found that the pretreatment temperature influenced the quality of the nanotube coating. The morphology of the CNT deposit supported the base-growth scheme and VLS (vapor—liquid—solid) growth mechanisms of CNTs. This study served as the basis for the development of CNTs in the larger scale application. Scaled up self-assembly of single-walled carbon nanotubes (SWCNTs) was studied in long tubes and finally they were used as GC columns. The strategy for selective SWCNT growth required the prevention of iron in the bulk steel from participating in the catalytic CVD process, as the presence of iron always led to MWCNT formation. Consequently, silica lined stainless steel tubings, such as, SilcosteelTM and SulfinertTM were selected. A SWCNT film with an average thickness of 300 nm was self-assembled by a unique single-step, catalytic CVD process consisting of dissolved cobalt and molybdenum salts in ethanol, where ethanol served as the precursor and cobalt and molybdenum as catalysts. Such large-scale assembly required process and catalyst optimization. A variety of organic compounds with varying polarity were separated at high resolution and the column efficiency demonstrated around 1000 theoretical plates/in, comparable to commercial GC columns. Evaluation of Van’t Hoff and Van deemter plots suggested that the CNTs followed classical chromatography behavior. Comparison of capacity factors (k’) and isosteric heats of adsorption (ΔHS) with a packed column containing a commercial sorbent (Carbopack CTM) showed comparable results. This demonstrated high capacity and strong sorbate-sorbent interactions on the SWCNT phase. Evaluation of the McReynolds constants suggested that the SWCNT was a non-polar phase. The high surface area of the SWCNT media allowed separations of gases, and at the same time, its high thermal stability (\u3e425°C) permitted separations of higher molecular weights at higher temperatures, thus extending the range of conventional chromatography on the same column

Laboratory directed research and development program, FY 1996

The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) Laboratory Directed Research and Development Program FY 1996 report is compiled from annual reports submitted by principal investigators following the close of the fiscal year. This report describes the projects supported and summarizes their accomplishments. It constitutes a part of the Laboratory Directed Research and Development (LDRD) program planning and documentation process that includes an annual planning cycle, projection selection, implementation, and review. The Berkeley Lab LDRD program is a critical tool for directing the Laboratory`s forefront scientific research capabilities toward vital, excellent, and emerging scientific challenges. The program provides the resources for Berkeley Lab scientists to make rapid and significant contributions to critical national science and technology problems. The LDRD program also advances the Laboratory`s core competencies, foundations, and scientific capability, and permits exploration of exciting new opportunities. Areas eligible for support include: (1) Work in forefront areas of science and technology that enrich Laboratory research and development capability; (2) Advanced study of new hypotheses, new experiments, and innovative approaches to develop new concepts or knowledge; (3) Experiments directed toward proof of principle for initial hypothesis testing or verification; and (4) Conception and preliminary technical analysis to explore possible instrumentation, experimental facilities, or new devices

Quantum dots

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 153-152).We have developed a continuous multi-stage high-temperature and high-pressure microfluidic system. High-pressure conditions enabled the use low molecular weight solvents that have previously not been available for quantum dot (QD) synthesis such as hexane or octane. The use of supercritical phase provided excellent mixing, which was critical in producing high quality QDs. In addition, the microfluidic system allowed precise control of synthetic conditions for the fast screening of reaction parameters. The continuous multi-stage microfluidic system enabled separating of reaction conditions such as mixing and aging steps, which was not possible in batch synthesis, as a result it was possible to conduct systematic investigation of the synthesis of indium phosphide (InP) QDs. We investigated synthesis of InP QDs with a continuous 3-stage high-temperature and high-pressure microreactor system without incorporating any batch manipulations between the synthesis steps. By separating the mixing process from the following aging process, we found that InP QD synthesis were mainly dominated by coalescence processes. Indium to fatty acid ratio showed the largest effect on particle size due to enhanced inter-particle processes. Concentrations or mixing temperatures changes, which are important reaction parameters of cadmium selenide (CdSe) QD growth, had no significant impact. We also synthesized larger (>3.2 nm) InP QDs with a sequential injection microreactor consisting of 6 sequential alternative monomer injections similar to the successive ion layers adsorption and reaction (SILAR) method. We obtained InP QDs with size distributions as narrow or narrower than the InP QDs synthesized via the ripening process. Indium phosphide / zinc sulfide (InP / ZnS) core-shell QDs were obtained with a 5 or 6 -stage microreactor system consisting of additional shell growth reactors, in addition to the three-step InP growth system. We were able to obtain narrow emissions with high quantum yield. This system was also used for the synthesis of indium phosphide / cadmium sulfide (InP / CdS), indium arsenide / indium phosphide (InAs / InP), and indium arsenide / cadmium sulfide (InAs / CdS) core-shell QDs. We also investigated the growth of InAs QDs using the same system for InP QD synthesis. We found that the InAs growth from indium myristate (In(MA) 3) and tristrimethylsilyl arsine ((TMS) 3As) precursors showed similar behavior as InP growth. However, different from the growth of InP nanocrystals, the amount of excess fatty acid did not affect on the growth of InAs nanocrystals. Indium phosphide arsenide (InPxAs1 -) alloy nanocrystals were also synthesized by precise control of phosphorus (P) and arsenic (As) precursor amounts. Mixing two anionic and cationic precursors at an elevated temperature followed by fast heating up to the reaction zone is very important for InPxAsl1x alloy nanocrystal synthesis. A multistage microfluidic system with a mixing reactor with gradient temperature was a useful tool for this synthesis. InPxAs - alloy nanocrystals were characterized with optical measurements and wide angle X-ray diffraction scattering. We investigated growth of InAs nanocrystals from a less reactive arsenic precursor, tris(trimethygermyl) arsine (TMG3As). We obtained InAs nanocrystals with better size distribution than those synthesized from TMS3As. We also compared the growth behavior of InAs nanocrystals synthesized from those two different arsenic precursors. With TMG3As, we observed a growth behavior potentially following a similar nucleation and growth model to that of growth of II-VI QDs.by Jinyoung Baek.Ph.D

Sensor-Based Monitoring and Inspection of Surface Morphology in Ultraprecision Manufacturing Processes

This research proposes approaches for monitoring and inspection of surface morphology with respect to two ultraprecision/nanomanufacturing processes, namely, ultraprecision machining (UPM) and chemical mechanical planarization (CMP). The methods illustrated in this dissertation are motivated from the compelling need for in situ process monitoring in nanomanufacturing and invoke concepts from diverse scientific backgrounds, such as artificial neural networks, Bayesian learning, and algebraic graph theory. From an engineering perspective, this work has the following contributions:1. A combined neural network and Bayesian learning approach for early detection of UPM process anomalies by integrating data from multiple heterogeneous in situ sensors (force, vibration, and acoustic emission) is developed. The approach captures process drifts in UPM of aluminum 6061 discs within 15 milliseconds of their inception and is therefore valuable for minimizing yield losses.2. CMP process dynamics are mathematically represented using a deterministic multi-scale hierarchical nonlinear differential equation model. This process-machine inter-action (PMI) model is evocative of the various physio-mechanical aspects in CMP and closely emulates experimentally acquired vibration signal patterns, including complex nonlinear dynamics manifest in the process. By combining the PMI model predictions with features gathered from wirelessly acquired CMP vibration signal patterns, CMP process anomalies, such as pad wear, and drifts in polishing were identified in their nascent stage with high fidelity (R2 ~ 75%).3. An algebraic graph theoretic approach for quantifying nano-surface morphology from optical micrograph images is developed. The approach enables a parsimonious representation of the topological relationships between heterogeneous nano-surface fea-tures, which are enshrined in graph theoretic entities, namely, the similarity, degree, and Laplacian matrices. Topological invariant measures (e.g., Fiedler number, Kirchoff index) extracted from these matrices are shown to be sensitive to evolving nano-surface morphology. For instance, we observed that prominent nanoscale morphological changes on CMP processed Cu wafers, although discernible visually, could not be tractably quantified using statistical metrology parameters, such as arithmetic average roughness (Sa), root mean square roughness (Sq), etc. In contrast, CMP induced nanoscale surface variations were captured on invoking graph theoretic topological invariants. Consequently, the graph theoretic approach can enable timely, non-contact, and in situ metrology of semiconductor wafers by obviating the need for reticent profile mapping techniques (e.g., AFM, SEM, etc.), and thereby prevent the propagation of yield losses over long production runs.Industrial Engineering & Managemen

Graphene inspired sensing devices

Graphene’s exciting characteristics such as high mechanical strength, tuneable electrical prop- erties, high thermal conductivity, elasticity, large surface-to-volume ratio, make it unique and attractive for a plethora of applications including gas and liquid sensing. Adsorption, the phys- ical bonding of molecules on solid surfaces, has huge impact on the electronic properties of graphene. We use this to develop gas sensing devices with faster response time by suspending graphene over large area (cm^2) on silicon nanowire arrays (SiNWAs). These are fabricated by two-step metal-assisted chemical etching (MACE) and using a home-developed polymer-assisted graphene transfer (PAGT) process. The advantage of suspending graphene is the removal of diffusion-limited access to the adsorption sites at the interface between graphene and its support. By modifying the Langmuir adsorption model and fitting the experimental response curves, we find faster response times for both ammonia and acetone vapours. The use of suspended graphene improved the overall response, based on speed and amplitude of response, by up to 750% on average. This device could find applications in biomedical breath analysis for diseases such lung cancer, asthma, kidney failure and more. Taking advantage of the mechanical strength of graphene and using the developed PAGT process, we transfer it on commercial (CMOS) Ion-Sensitive Field-Effect Transistor (ISFET) arrays. The deposition of graphene on the top sensing layer reduces drift that results from the surface modification during exposure to electrolyte while improving the overall performance by up to about 10^13 % and indicates that the ISFET can operate with metallic sensing membrane and not only with insulating materials as confirmed by depositing Au on the gate surface. Post- processing of the ISFET top surface by reactive ion plasma etching, proved that the physical location of trapped charge lies within the device structure. The process improved its overall performance by about 105 %. The post-processing of the ISFET could be applied for sensor performance in any of its applications including pH sensing for DNA sequencing and glucose monitoring.Open Acces