Novel deployable membrane structures: Design and implementation

Ph.DDOCTOR OF PHILOSOPH

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Determining weights for criteria is an extremely crucial step in the process of selecting an option based on multiple criteria, also known as Multi-Criteria Decision Making (MCDM). This article presents the combination of five objective weighting methods for criteria with three MCDM methods in the context of material selection. The five objective weighting methods considered are Entropy, MEREC (Method based on the Removal Effects of Criteria), LOPCOW (Logarithmic Percentage Change-driven Objective Weighting), CRITIC (Criteria Importance Through Intercriteria Correlation), and MEAN. The three MCDM methods employed are MARA (Magnitude of the Area for the Ranking of Alternatives), RAM (Root Assessment Method), and PIV (Proximity Indexed Value). Material selection investigations were conducted in three different cases, including lubricant selection for two-stroke engines, material selection for manufacturing screw shafts, and material selection for manufacturing gears. The Spearman's rank correlation coefficient was calculated to assess the stability of ranking the alternatives using different MCDM methods. The combinations of objective weighting methods and MCDM methods were evaluated based on factors such as consistency in identifying the best material type, range, average value, and median of each set of Spearman's rank correlation coefficients. Two significant findings were identified. First, the weights of criteria calculated using LOPCOW method appear to be inversely related to those calculated using the Entropy method. Second, among the three MCDM methods used, MARA was identified as the most suiTable for lubricant selection for two-stroke engines, RAM was found to be the most suiTable for material selection for screw shafts and gears. The best material type in each case was also determine

PURIFICATION OF PHOSPHOGYPSUM FOR USE AS CEMENT RETARDER BY SULPHURIC ACID TREATMENT

Phosphogypsum is a by-product of the wet phosphoric acid production. In this study, chemical compositions of phosphogypsum waste (PG) in Hai Phong diammonium phosphate plant (DAP1) and Lao Cai diammonium phosphate plant (DAP2) in Vietnam were surveyed for the purpose of gypsum recovery by P2O5, F removal to meet TCVN11833 for use treated gypsum as cement retarder. Studies of impurities P2O5, F, TOC removal by sulfuric acid 10 % at 28 0C was presented. The results found that the combination of a low concentration of sulfuric acid treatment, washing, lime neutralizing, and thermal treatment was successful in Phoshogypsum treatment for use as cement retarder. The cement test proved that treated PG could partially replace natural gypsum as a retarder.Keywords:  phosphogypsum treatment, phosphorus pentoxide removal, calcium sulfate transition phase, cement retarder.

An in-situ thermoelectric measurement apparatus inside a thermal-evaporator

At the ultra-thin limit below 20 nm, a film's electrical conductivity, thermal conductivity, or thermoelectricity depends heavily on its thickness. In most studies, each sample is fabricated one at a time, potentially leading to considerable uncertainty in later characterizations. We design and build an in-situ apparatus to measure thermoelectricity during their deposition inside a thermal evaporator. A temperature difference of up to 2 K is generated by a current passing through an on-chip resistor patterned using photolithography. The Seebeck voltage is measured on a Hall bar structure of a film deposited through a shadow mask. The measurement system is calibrated carefully before loading into the thermal evaporator. This in-situ thermoelectricity measurement system has been thoroughly tested on various materials, including Bi, Te, and Bi$_2$Te$_3$, at high temperatures up to 500 K

Enhancement of current-voltage characteristics of multilayer organic light emitting diodes by using nanostructured composite films

With the aim of improving the photonic efficiency of an organic light emitting diode (OLED) and its display duration, both the hole transport layer (HTL) and the emitting layer (EL) were prepared as nanostructured thin films. For the HTL, nanocomposite films were prepared by spin-coating a homogeneous solution of low molecular weight poly(4-styrenesulfonate) (PEDOT-PSS) and surfactant-capped TiO2 nanocrystals onto low resistivity indium tin oxide (ITO) substrates; for the EL, nancrystalline titatium oxide (nc-TiO2)-embedded Poly[2-methoxy-5-(2′-ethyl-hexyloxy)-1,4-phenylene vinylene] (MEH-PPV+nc-TiO2) conjugate polymers were spin-coated onto the HTL. Also, for a shallow contact of Al/LiF/MEH-PPV instead of Al/MEH-PPV a super LiF thin film was deposited onto the EL by vacuum evaporation. The resulting multilayer OLED had the following structure of Al/LiF/MEH-PPV+nc-TiO2/PEDOT-PSS+nc-TiO2/ITO. Characterization of the nanocomposite films showed that both the current-voltage (I-V) characteristics and the photoluminescent properties of the nanocomposite materials were significantly enhanced in comparison with the standard polymers. OLEDs made from these layers would exhibit a large photonic efficiency

Choosing the best machine tool in mechanical manufacturing

Machine tools are indispensable components and play an important role in mechanical manufacturing. The equipment of machine tools has a huge effect on the operational efficiency of businesses. Each machine tool type is described by many different criteria, such as cost, technological capabilities, accuracy, energy consumption, convenience in operation, safety for workers, working noise, etc. If the selection of machine is only based on one or several criteria, it will be really easy to make mistakes, which means it is not possible to choose the real best machine. A machine is considered to be the best only when it is chosen based on all of its criteria. This work is called multi-criteria decision-making (MCDM). In this study, the selection of machine tools has been done using two different multi-criteria decision-making methods, including the FUCA method (Faire Un Choix Adéquat) and the CURLI method (Collaborative Unbiased Rank List Intergration). These are two methods with very different characteristics. When using the FUCA method, it is necessary to normalize the data and determine the weights for the criteria. Meanwhile, if using the CURLI method, these two things are not necessary. The selection of these two distinct methods is intended to produce the most generalizable conclusions. Three types of machine tool, which are considered in this study, include grinding machine, drilling machine and milling machine. The number of grinders that were offered for selection was twelve, the number of drills that were surveyed in this study was thirteen, while nine were the number of milling machines that were given for selection. The objective of this study is to determine the best solution in each type of machine. The results of ranking the machines are very similar when using the two mentioned methods. Specially, in all the surveyed cases, the two methods FUCA and CURLI always find the same best alternative. Accordingly, it is possible to firmly come to a conclusion that the FUCA method and the CURLI method are equally effective in machine tool selection. In addition, this study has determined the best three machines corresponding to the three different machine type

IMPROVEMENT OF CO2 PURIFYING SYSTEM BY PHOTOCATALYST FOR APPLICATION IN MICROALGAE CULTURE TECHNOLOGY

By reactive grinding method Vanadium-doped rutile TiO2 nanoparticle material was obtained with an average particle size of 20‐40nm, the Brunauer–Emmet–Teller (BET) specific surface area about 20 m2g−1 and it absorbed strongly in the UV region and increased at the visible wavelength of 430 – 570 nm. This study focused on the improvement of exhaust gas treatment from coal-fired flue gas of the traditional adsorption-catalysis system (Modular System for Treating Flue Gas - MSTFG) by using the V2O5/TiO2 Rutile as photocatalyst. The results showed that integrating both catalytic systems mentioned above increased the gas treatment efficiency: CO from 77 % to over 98 %, NOx from 50 % to 93 %, SO2 was absent as opposed to the input gas component. Also it showed that V2O5/TiO2 Rutile integrated with MSTFG has got high efficiency of CO treatment, also secured the high obtained CO2 concentration as a valuable carbon source for microagal mass culture as well as saving energy and simplifying devices

Preparation of SERS Substrates for the Detection of Organic Molecules at Low Concentration

In this paper, we present the results of the preparation of Surface Enhanced Raman Spectroscopy (SERS) substrates by depositing silver nanoparticles (Ag NPs) onto a porous silicon wafer that is produced by the chemical etching process. The influences of the preparation parameters such as resistivity of the silicon wafer, the anodizing current density, etching time to the size of pores were systematically investigated. The SERS substrates prepared were characterised by using appropriate techniques: the morphology and pores size by scanning electron microscope (SEM), the SERS activity by Raman scattering measure of organic molecules malachite green (MG) embedded into the substrate at room temperature. Our experimental results show that a home-made Raman microscope system could be efficiently used to detect the MG molecules at the concentration lower than 10-7 M with the prepared SERS substrates which have Ag NPs in the obtained pores of 10 – 40 nm

Recommender Systems with Generative Retrieval

Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. To the best of our knowledge, this is the first Semantic ID-based generative model for recommendation tasks. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.Comment: Preprint versio