6 resultados para Method of multiple scale
em Aston University Research Archive
Resumo:
Quantum dots (Qdots) are fluorescent nanoparticles that have great potential as detection agents in biological applications. Their optical properties, including photostability and narrow, symmetrical emission bands with large Stokes shifts, and the potential for multiplexing of many different colours, give them significant advantages over traditionally used fluorescent dyes. Here, we report the straightforward generation of stable, covalent quantum dot-protein A/G bioconjugates that will be able to bind to almost any IgG antibody, and therefore can be used in many applications. An additional advantage is that the requirement for a secondary antibody is removed, simplifying experimental design. To demonstrate their use, we show their application in multiplexed western blotting. The sensitivity of Qdot conjugates is found to be superior to fluorescent dyes, and comparable to, or potentially better than, enhanced chemiluminescence. We show a true biological validation using a four-colour multiplexed western blot against a complex cell lysate background, and have significantly improved previously reported non-specific binding of the Qdots to cellular proteins.
Resumo:
Biomass is projected to account for approximately half of the new energy production required to achieve the 2020 primary energy target in the UK. Combined heat and power (CHP) bioenergy systems are not only a highly efficient method of energy conversion, at smaller-scales a significant proportion of the heat produced can be effectively utilised for hot water, space heating or industrial heating purposes. However, there are many barriers to project development and this has greatly inhibited deployment in the UK. Project viability is highly subjective to changes in policy, regulation, the finance market and the low cost incumbent; a high carbon centralised energy system. Unidentified or unmitigated barriers occurring during the project lifecycle may not only negatively impact on the project but could ultimately lead to project failure. The research develops a decision support system (DSS) for small-scale (500 kWe to 10 MWe) biomass combustion CHP project development and risk management in the early stages of a potential project’s lifecycle. By supporting developers in the early stages of project development with financial, scheduling and risk management analysis, the research aims to reduce the barriers identified and streamline decision-making. A fuzzy methodology is also applied throughout the developed DSS to support developers in handling the uncertain or approximate information often held at the early stages of the project lifecycle. The DSS is applied to a case study of a recently failed (2011) small-scale biomass CHP project to demonstrate its applicability and benefits. The application highlights that the proposed development within the case study was not viable. Moreover, further analysis of the possible barriers with the DSS confirmed that some possible modifications to be project could have improved this, such as a possible change of feedstock to a waste or residue, addressing the unnecessary land lease cost or by increasing heat utilisation onsite. This analysis is further supported by a practitioner evaluation survey that confirms the research contribution and objectives are achieved.
Resumo:
A series of alkali-doped metal oxide catalysts were prepared and evaluated for activity in the transesterification of rapeseed oil to biodiesel. Of those evaluated, LiNO3/CaO, NaNO3/CaO, KNO3/CaO and LiNO3/MgO exhibited >90% conversion in a standard 3 h test. There was a clear correlation between base strength and activity. These catalysts appeared to be promising candidates to replace conventional homogeneous catalysts for biodiesel production as the reaction times are low enough to be practical in continuous processes and the preparations are neither prohibitively difficult nor costly. However, metal leaching from the catalyst was detected, and this resulted in some homogeneous activity. This would have to be resolved before these catalysts would be viable for large-scale biodiesel production facilities.
Resumo:
The study examined the effect of range of a confidence scale on consumer knowledge calibration, specifically whether a restricted range scale (25%- 100%) leads to difference in calibration compared to a full range scale (0%-100%), for multiple-choice questions. A quasi-experimental study using student participants (N = 434) was employed. Data were collected from two samples; in the first sample (N = 167) a full range confidence scale was used, and in the second sample (N = 267) a restricted range scale was used. No differences were found between the two scales on knowledge calibration. Results from studies of knowledge calibration employing restricted range and full range confidence scales are thus comparable. © Psychological Reports 2014.
Resumo:
Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.
Resumo:
When machining a large-scale aerospace part, the part is normally located and clamped firmly until a set of features are machined. When the part is released, its size and shape may deform beyond the tolerance limits due to stress release. This paper presents the design of a new fixing method and flexible fixtures that would automatically respond to workpiece deformation during machining. Deformation is inspected and monitored on-line, and part location and orientation can be adjusted timely to ensure follow-up operations are carried out under low stress and with respect to the related datum defined in the design models.