GROWTH OF ALIGNED CARBON NANOTUBES ON COPPER SUBSTRATES

Since the discovery of carbon nanotubes (CNTs) in the early 1990s, there has been enormous interest in trying to synthesize and understand their growth mechanism. This is required in order to successfully integrate them into new devices and applications that exploit their remarkable physical properties, including high mechanical strength, high aspect ratio, and excellent conductivity. Depending on the alignment of CNTs, random “spaghetti-like” or preferentially aligned CNTs on suitable substrates are of interest for potential applications such as energy storage, sensing, supercapacitors, and nanoelectronic devices via a variety of chemical vapor deposition (CVD) techniques such as thermal, plasma enhanced, water assisted growth. For many of the envisioned applications, dense, aligned CNTs grown using an economically viable technique and good contact with a conductive metallic substrate such as copper is required. The primary objective of the experiments described in this dissertation is to achieve vertical growth of carbon nanotubes on copper substrates using thermal CVD. The second goal is to understand and comprehensively determine how the processing conditions can be tailored to improve the density and degree of vertical alignment of the CNTs. The final goal is to measure properties to establish feasibility of use in device structures using aligned carbon nanotubes on copper. Since copper itself is not a good catalyst for carbon nanotube growth, the technique discusses the use of sputtered thin films of nickel or Inconel deposited on copper substrates with additional catalyst supply of iron from ferrocene decomposition during the CVD growth. Thus the growth studies discussed in the dissertation includes the use of a combination of sputtered thin films and iron as catalysts on copper to promote the dense vertical growth of carbon nanotubes that is desired

Energy Efficiency of 5G Radio Access Networks

The roll-out of the fifth-generation (5G) wireless networks alongside existing generations and characterized by a dense deployment of base stations (BSs) to serve an ever-increasing number of users and services leads to a drastic increase in the overall network energy consumption (EC). It can lead to an unprecedented rise in operational expenditure (OPEX) for the network operators and an increased global carbon footprint. The present-day networks are dimensioned according to the peak traffic demands, and hence are under-utilized due to the daily traffic variations. Therefore, to save energy, BSs can be put into sleep with different levels following the daily load variations. Selection of the right sleep level at the right instant is important to adapt the availability of the resources to the traffic load to maximize the energy savings without degrading the performance of the network. Previous studies focused on the selection of sleep modes (SMs) to maximize energy saving or the sleep duration given configuration and network resources. However, adaptive BS configuration together with SMs have not been investigated. In this thesis, the goal is to consider the design of the wireless network resources to cover an area with a given traffic demand in combination with sleep mode management. To achieve this, a novel EC model is proposed to capture the activity time of a 5G BS in a multi-cell environment. The activity factor of a BS is defined as the fraction of time the BS is transmitting over a fixed period and is dependent on the amount of BS resources. The new model captures the variation in power consumption by configuring three BS resources: 1) the active array size, 2) the bandwidth, and 3) the spatial multiplexing factor. We then implement a Q-learning algorithm to adapt these resources following the traffic demand and also the selection of sleep levels. Our results show that the difference in the average daily EC of BSs considered can be as high as 60% depending on the deployment area. Furthermore, the EC of a BS can be reduced by 57% during the low traffic hours by having deeper sleep levels as compared to the baseline scenario with no sleep modes. Implementing the resource adaptation algorithm further reduces the average EC of the BS by up to 20% as compared to the case without resource adaptation. However, the EE gain obtained by the algorithm depends on its convergence, which varies with the distribution of the users in the cell, the peak traffic demand, and the BS resources available. Our results show that by combining resource adaptation with deep sleep levels, one can obtain significant energy savings under variable traffic load. However, to ensure the reliability of the results obtained, we emphasize the need to guarantee the convergence of the algorithm before its use for resource adaptation. Under de senaste åren har intresset för energieffektivitet (EE) av mobila kommunikationssystem ökat på grund av den ökande energiförbrukningen (EF). Med femte generationens mobilsystem, vilket kännetecknas av mer komplexa och kraftfulla basstationer (BS) för att betjäna ett ständigt ökande antal användare och tjänster, riskerar nätverkets totala EF att öka ytterligare. Detta kan leda till en markant ökning av operativa utgifter (OPEX) för nätoperatörerna och ett ökat globalt koldioxidavtryck. Många studier har visat att dagens nätverk ofta är överdimensionerade och att radioresurserna är underutnyttjade på grund av variationerna i det dagliga trafikbehovet. Genom att anpassa BS radioresurser efter trafikbehovet kan man säkerställa att man uppfyller användarkraven samtidigt som man minskar den totala EF. I denna studie föreslås en aktivitetsbaserad metod för att utvärdera EF för en BS. Aktivitetsfaktorn för en BS definieras som den bråkdel av tiden som BS är aktiv (sänder data) under en fast period och är beroende av mängden radioresurser. För att kvantifiera EF för en BS föreslås en ny modell som beräknar in effekt till BS som funktion av utstrålad effekt från BS. Den nya modellen fångar variationen i energiförbrukning med tre huvudsakliga radioresurser som är: 1) antal sändarantenner 2) bandbredd och 3) den spatiella multiplexingfaktorn (antal användare som schemaläggs samtidigt). Därefter implementeras en Q- inlärningsalgoritm för att anpassa dessa resurser efter det upplevda trafikbehovet och vilolägen som BS kan växla till när den är inaktiv. Ett viloläge innebär att viss hårdvara i BS stängs av. Resultatet visar att man genom att identifiera rätt typ av BS utifrån lokala trafikförhållanden kan få energibesparingar så höga som 60%. Vidare kan EF för en BS reduceras med 57% under den tid av dygnet då trafiken är som lägst genom att ha djupare vilolägen jämfört med basscenariot utan vilolägen. Genom att implementera Q-inlärningsalgoritmen som anpassar tillgängliga radioresurser till trafikbehovet minskar den genomsnittliga EF för BS ytterligare med upp till 20%. Vinsten i EE som erhålls av algoritmen beror dock till stor del på dess konvergens, som varierar med fördelningen av användarna i cellen, topptrafikbehovet och BS tillgängliga radioresurser. Resultatet visar att genom att kombinera resursanpassning med vilolägen kan man få betydande energibesparingar under varierande trafikbelastning. För att säkerställa tillförlitligheten av de erhållna resultaten betonas emellertid behovet av att garantera konvergensen av algoritmen innan den används för resursanpassning

Energy Efficiency of 5G Radio Access Networks

The roll-out of the fifth-generation (5G) wireless networks alongside existing generations and characterized by a dense deployment of base stations (BSs) to serve an ever-increasing number of users and services leads to a drastic increase in the overall network energy consumption (EC). It can lead to an unprecedented rise in operational expenditure (OPEX) for the network operators and an increased global carbon footprint. The present-day networks are dimensioned according to the peak traffic demands, and hence are under-utilized due to the daily traffic variations. Therefore, to save energy, BSs can be put into sleep with different levels following the daily load variations. Selection of the right sleep level at the right instant is important to adapt the availability of the resources to the traffic load to maximize the energy savings without degrading the performance of the network. Previous studies focused on the selection of sleep modes (SMs) to maximize energy saving or the sleep duration given configuration and network resources. However, adaptive BS configuration together with SMs have not been investigated. In this thesis, the goal is to consider the design of the wireless network resources to cover an area with a given traffic demand in combination with sleep mode management. To achieve this, a novel EC model is proposed to capture the activity time of a 5G BS in a multi-cell environment. The activity factor of a BS is defined as the fraction of time the BS is transmitting over a fixed period and is dependent on the amount of BS resources. The new model captures the variation in power consumption by configuring three BS resources: 1) the active array size, 2) the bandwidth, and 3) the spatial multiplexing factor. We then implement a Q-learning algorithm to adapt these resources following the traffic demand and also the selection of sleep levels. Our results show that the difference in the average daily EC of BSs considered can be as high as 60% depending on the deployment area. Furthermore, the EC of a BS can be reduced by 57% during the low traffic hours by having deeper sleep levels as compared to the baseline scenario with no sleep modes. Implementing the resource adaptation algorithm further reduces the average EC of the BS by up to 20% as compared to the case without resource adaptation. However, the EE gain obtained by the algorithm depends on its convergence, which varies with the distribution of the users in the cell, the peak traffic demand, and the BS resources available. Our results show that by combining resource adaptation with deep sleep levels, one can obtain significant energy savings under variable traffic load. However, to ensure the reliability of the results obtained, we emphasize the need to guarantee the convergence of the algorithm before its use for resource adaptation. Under de senaste åren har intresset för energieffektivitet (EE) av mobila kommunikationssystem ökat på grund av den ökande energiförbrukningen (EF). Med femte generationens mobilsystem, vilket kännetecknas av mer komplexa och kraftfulla basstationer (BS) för att betjäna ett ständigt ökande antal användare och tjänster, riskerar nätverkets totala EF att öka ytterligare. Detta kan leda till en markant ökning av operativa utgifter (OPEX) för nätoperatörerna och ett ökat globalt koldioxidavtryck. Många studier har visat att dagens nätverk ofta är överdimensionerade och att radioresurserna är underutnyttjade på grund av variationerna i det dagliga trafikbehovet. Genom att anpassa BS radioresurser efter trafikbehovet kan man säkerställa att man uppfyller användarkraven samtidigt som man minskar den totala EF. I denna studie föreslås en aktivitetsbaserad metod för att utvärdera EF för en BS. Aktivitetsfaktorn för en BS definieras som den bråkdel av tiden som BS är aktiv (sänder data) under en fast period och är beroende av mängden radioresurser. För att kvantifiera EF för en BS föreslås en ny modell som beräknar in effekt till BS som funktion av utstrålad effekt från BS. Den nya modellen fångar variationen i energiförbrukning med tre huvudsakliga radioresurser som är: 1) antal sändarantenner 2) bandbredd och 3) den spatiella multiplexingfaktorn (antal användare som schemaläggs samtidigt). Därefter implementeras en Q- inlärningsalgoritm för att anpassa dessa resurser efter det upplevda trafikbehovet och vilolägen som BS kan växla till när den är inaktiv. Ett viloläge innebär att viss hårdvara i BS stängs av. Resultatet visar att man genom att identifiera rätt typ av BS utifrån lokala trafikförhållanden kan få energibesparingar så höga som 60%. Vidare kan EF för en BS reduceras med 57% under den tid av dygnet då trafiken är som lägst genom att ha djupare vilolägen jämfört med basscenariot utan vilolägen. Genom att implementera Q-inlärningsalgoritmen som anpassar tillgängliga radioresurser till trafikbehovet minskar den genomsnittliga EF för BS ytterligare med upp till 20%. Vinsten i EE som erhålls av algoritmen beror dock till stor del på dess konvergens, som varierar med fördelningen av användarna i cellen, topptrafikbehovet och BS tillgängliga radioresurser. Resultatet visar att genom att kombinera resursanpassning med vilolägen kan man få betydande energibesparingar under varierande trafikbelastning. För att säkerställa tillförlitligheten av de erhållna resultaten betonas emellertid behovet av att garantera konvergensen av algoritmen innan den används för resursanpassning

Bulk Fabrication of SS410 Material Using Cold Metal Transfer-Based Wire Arc Additive Manufacturing Process at Optimized Parameters: Microstructural and Property Evaluation

To make metallic parts for manufacturing industries, additive manufacturing (AM) has acquired considerable significance. However, most efforts have concentrated on powder-based techniques, and there remains a dearth of the experimental evidence on the mechanical characteristics and structural behavior of metallic elements produced using wire-and-arc additive manufacturing (WAAM). This article examined the optimal parameters to enable bulk fabrication of thick walls made with a SS410 wire. The objective was to assess the optimized variables utilizing response surface methodology (RSM), followed by the microstructural analysis and mechanical property evaluation. During optimization, the influence of wire feed speed, travel speed, and gas flow rate on bead width and height was determined. Further, the optimized variables resulted in the successful formation of thick walls. Secondly, the microstructural analysis mainly featured the martensite and delta ferrite, with the latter’s percentage increasing with build height. The maximum micro-hardness of 452 HV was obtained at the base of the wall. In addition, the remarkable increases in the standard deviation of micro-hardness represent the great extent of anisotropy in the thick wall. Moreover, the maximum UTS (803 ± 8 MPa) and YS (659 ± 10 MPa) are achieved for the OB sample, which is similar to conventional components. However, the current investigation’s percentage elongation of 5% (max) demands more study before the actual use of the WAAM manufactured SS410 material. Therefore, due to the significant degree of anisotropy and poor percentage elongation, the findings conclude that post-processing is required after bulk SS410 manufacturing

Effect of Multi-Pass Friction Stir Processing on Microstructure and Mechanical Properties of a Metastable Dual-Phase High Entropy Alloy

Studies on Multi-pass Friction Stir Processing (FSP) of Fe49.5Mn30Co10Cr10C0.5, a metastable dual-phase High Entropy Alloy (HEA), were carried out with the aim to systematically investigate the microstructural changes occurring during different passes, and to evaluate the mechanical response of this alloy with progressive passes. A reduction in grain size and a change in HCP volume fraction was observed after each pass. Dynamic recrystallization, occurring during FSP, led to grain refinement, and the transformation induced plasticity (TRIP) effect resulted in observed changes in HCP phase fraction. One-pass FSPed material exhibits a higher work hardening rate and a higher ultimate tensile strength (UTS.) value, as compared to both, an annealed and two-pass FSPed material. This is due to a combination of two factors, a small grain size and a large fraction of metastable Face Centred Cubic (FCC) phase, in the microstructure of the one-pass material

Bulk Fabrication of SS410 Material Using Cold Metal Transfer-Based Wire Arc Additive Manufacturing Process at Optimized Parameters: Microstructural and Property Evaluation

To make metallic parts for manufacturing industries, additive manufacturing (AM) has acquired considerable significance. However, most efforts have concentrated on powder-based techniques, and there remains a dearth of the experimental evidence on the mechanical characteristics and structural behavior of metallic elements produced using wire-and-arc additive manufacturing (WAAM). This article examined the optimal parameters to enable bulk fabrication of thick walls made with a SS410 wire. The objective was to assess the optimized variables utilizing response surface methodology (RSM), followed by the microstructural analysis and mechanical property evaluation. During optimization, the influence of wire feed speed, travel speed, and gas flow rate on bead width and height was determined. Further, the optimized variables resulted in the successful formation of thick walls. Secondly, the microstructural analysis mainly featured the martensite and delta ferrite, with the latter’s percentage increasing with build height. The maximum micro-hardness of 452 HV was obtained at the base of the wall. In addition, the remarkable increases in the standard deviation of micro-hardness represent the great extent of anisotropy in the thick wall. Moreover, the maximum UTS (803 ± 8 MPa) and YS (659 ± 10 MPa) are achieved for the OB sample, which is similar to conventional components. However, the current investigation’s percentage elongation of 5% (max) demands more study before the actual use of the WAAM manufactured SS410 material. Therefore, due to the significant degree of anisotropy and poor percentage elongation, the findings conclude that post-processing is required after bulk SS410 manufacturing

An Analytical Energy Performance Evaluation Methodology for 5G Base Stations

The implementation of various base station (BS) energy saving (ES) features and the widely varying network traffic demand makes it imperative to quantitatively evaluate the energy consumption (EC) of 5G BSs. An accurate evaluation is essential to understand how to adapt a BS's resources to reduce its EC. On the other hand, modeling the variation in the power consumption (PC) of a BS with its resources considering the user equipment(UE) performance is mathematically rigorous. In this work, we present a novel analytical methodology to evaluate the EC of a 5G BS under varying traffic load. We mathematically formulate the impact of massive multiple-input and multiple-output (MIMO) arrays, vast spectral resources, and the spatial multiplexing ability of these systems on the UE performance and activity of the BS. Next, we present an updated power model to capture the PC variation of two BSs types: a 4T and a 64T BS. Our proposed analytical methodology simplifies the complex network EC evaluation. Using this methodology, we show that identifying the right BS type for a given deployment area can reduce the overall network EC by up to 60%. Furthermore, by implementing deep sleep modes (SMs) facilitated by 5G, one can gain considerable energy savings (ES), especially during the off-peak hours of the day.Part of proceedings: ISBN 978-1-6654-2854-5QC 20210811</p