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The role of bacterial urease activity on the uniformity of carbonate precipitation profiles of bio-treated coarse sand specimens
No full textAbstractProtocols for microbially induced carbonate precipitation (MICP) have been extensively studied in the literature to optimise the process with regard to the amount of injected chemicals, the ratio of urea to calcium chloride, the method of injection and injection intervals, and the population of the bacteria, usually using fine- to medium-grained poorly graded sands. This study assesses the effect of varying urease activities, which have not been studied systematically, and population densities of the bacteria on the uniformity of cementation in very coarse sands (considered poor candidates for treatment). A procedure for producing bacteria with the desired urease activities was developed and qPCR tests were conducted to measure the counts of the RNA of the Ure-C genes. Sand biocementaton experiments followed, showing that slower rates of MICP reactions promote more effective and uniform cementation. Lowering urease activity, in particular, results in progressively more uniformly cemented samples and it is proven to be effective enough when its value is less than 10 mmol/L/h. The work presented highlights the importance of urease activity in controlling the quality and quantity of calcium carbonate cements.</jats:p
A generalization of the Langrange-Hamilton formalism with application to non-conservative systems and the quantum to classical transition
No full textThis work has two aims. The first is to develop a Lagrange-Hamilton framework for the analysis of multi-degree-of-freedom nonlinear systems in which non-conservative effects are included in the variational principle of least action from the outset. The framework is a generalization of the Bateman approach in which a set of adjoint coordinates is introduced. A function termed the M-function is introduced as the Fourier transform over the momenta of the joint probability density function (JPDF) of the displacements and momenta, and it is shown that for statistical systems, this function can be written as an expectation involving the new principle function and a general dimensional constant ħ . This leads to a concise derivation of the Fokker-Planck-Kolmogorov equation. It is found that the equation governing the M-function can be expressed in terms of the new Hamiltonian by replacing momenta by differential operators, meaning that the function satisfies the same equation as the quantum wave function. This gives rise to the second aim of this work: to explore relations between the developed classical framework and quantum mechanics. It is shown that for an undamped linear system, the solution of the M-function equation yields the response JPDF as a sum of Wigner functions. This classical analysis leads to a number of well-known results from quantum mechanics as ħ → 0, and the extension of this result to nonlinear systems is discussed. The quantum wave function associated with the Hamiltonian is then considered, and the relevance of this function to the physical system is discussed
Chemical characterization of size-selected nanoparticles emitted by a gasoline direct injection engine: Impact of a catalytic stripper
No full textThis work combines laser desorption/ionization mass spectrometry (L2MS) and advanced statistical techniques to reveal the impact of a catalytic stripper (CS) on the chemical composition (at the molecular level) of a gasoline direct injection engine exhaust, and follow the evolution of size-dependent chemical characteristics over the whole particles size range (10–560 nm). The gas phase and polydisperse particles making up the exhaust are separated and sampled on distinct substrates using an original homebuilt two-filter system, while size-selected particles are collected using a cascade impactor and separated into 13 different size bins (smallest diameters 10–18 nm). We demonstrate that a fine molecular-level characterization of the exhaust particulate matter is necessary to assess the effect of the CS, especially for the smallest ultra-fine particles carrying the largest volatile fraction
Channel inversion method for optimum power delivery in RF harvesting backscatter systems
No full textThis work presents a method for enhanced wireless power transfer using an algorithm to calculate the optimum phases of multiple transmitting antennas in a passive UHF RFID system. The algorithm performs the calculation based on measured backscatter phase value of individual antenna port and the phase rotation caused by each port's receiving channel. Through experimental validations, it is shown that the proposed algorithm can achieve up to 18 dB improvement in the tag RSSI using three transmitting antennas. The proposed algorithm could be used in the next generation sensor tags to optimise power delivery efficiency
Co-simulating a greenhouse in a building to quantify co-benefits of different coupled configurations
No full textRecent findings suggest that rooftop greenhouses could be more efficient when combined with waste streams in buildings, but there is a gap in quantification of the combined performance of building integrated greenhouses. This paper addresses this deficit for school buildings in London, UK, where urban agriculture is of increasing interest. A building energy simulation (BES) of an archetype school building is developed in EnergyPlus and co-simulated with a validated greenhouse energy simulator (GES). The performance of different greenhouse-building coupling configurations is evaluated to estimate the potential for crop growth, heat recovery and reduction in ventilation demand, through a sensitivity analysis and parametric study. Our results show that a 250 m (Formula presented.) greenhouse on the top floor of the school could produce 6t lettuce with half the energy demand of the same standalone greenhouse. Trade-offs across increase in humidity, yields, and energy efficiency indicate the importance of modelling to ensure optimal designs
Rapid setup and management of medical device design and manufacturing consortia: experiences from the COVID-19 crisis in the UK
No full textThe COVID-19 pandemic caused severe ventilator shortages in many healthcare systems worldwide. The UK government reacted to this with a three-pronged approach of importing, up-scaling existing production and supporting new design projects. The latter two parts – labelled the UK Ventilator Challenge – included over 50 companies from various sectors including the automotive and aerospace industries. Nine multi-partner consortia and five single-company projects were initiated with varying approaches. This study explores lessons learned during the setup and management of these medical device designs and manufacturing consortia. A qualitative survey methodology was employed, and 32 semi-structured stakeholder interviews were conducted. The primary data was triangulated through the collection of 42 secondary data sources such as webinars and radio interviews. Transcription and a three-step data analysis process of thematic coding identified six lessons learned. The analysis of the data showed that a strong, appealing common goal can enable employee motivation and trust as well as align priorities across all companies involved. This facilitates the involvement and fruitful collaboration of companies with varying sizes and fields of expertise. Furthermore, selecting the most suitable employees with specialist knowledge for high-priority projects and empowering them to make decisions can have a positive effect on project performance. The findings from the study complement existing literature on new product development and crisis management processes. In addition, the results uncover potential long-term effects such as more openness for cross-sector collaborations, which can serve as interesting sources for further research
2021 roadmap on lithium sulfur batteries
No full textBatteries that extend performance beyond the intrinsic limits of Li-ion batteries are among the most important developments required to continue the revolution promised by electrochemical devices. Of these next-generation batteries, lithium sulfur (Li–S) chemistry is among the most commercially mature, with cells offering a substantial increase in gravimetric energy density, reduced costs and improved safety prospects. However, there remain outstanding issues to advance the commercial prospects of the technology and benefit from the economies of scale felt by Li-ion cells, including improving both the rate performance and longevity of cells. To address these challenges, the Faraday Institution, the UK’s independent institute for electrochemical energy storage science and technology, launched the Lithium Sulfur Technology Accelerator (LiSTAR) programme in October 2019. This Roadmap, authored by researchers and partners of the LiSTAR programme, is intended to highlight the outstanding issues that must be addressed and provide an insight into the pathways towards solving them adopted by the LiSTAR consortium. In compiling this Roadmap we hope to aid the development of the wider Li–S research community, providing a guide for academia, industry, government and funding agencies in this important and rapidly developing research space
Using a flame ionisation detector to measure the rate and duration of pyrolysis of a biomass particle
No full textSingle cubes and spheres of spruce wood have been heated in beds of inert sand, fluidised by nitrogen and heated electrically to 500–700 °C. The release of volatile matter from these pyrolysing particles, submerged inside a cage in the bed, was monitored by continuously sampling the off-gas from the bed into a rapid, flame ionisation detector (FID). This instrument's output was shown to be proportional to the rate at which carbonaceous volatile species were produced by a wooden particle, when thermally decomposing in the hot fluidised bed. The FID's rapid response revealed that some volatiles were released in brief, explosive bursts (lasting ~ 1 s), probably after local build-ups of pressure inside the biomass. Interestingly, the production of volatiles continued after the centre of a decomposing particle had reached the bed's temperature. Thus, the FID provided good measurements of pyrolysis times. The measurements also indicated that volatiles appeared in a fluidised bed as a cloud of bubbles rising around a decomposing particle. The bubbles pushed away the hot sand and so markedly reduced the rate of heat transfer from the bed to a particle. This had unexpected consequences. In a hot bed (700 °C), the duration of pyrolysis for a cube of spruce was proportional to the length, L, of the cube's side, for L ≤ 7 mm. This means that external heat transfer, which included radiation from the bed, then controlled the rate of thermal decomposition. In a cooler bed (500 °C), the duration of pyrolysis depended on a mix of L and L2, indicating that control was then by both internal and external heat transfer. Thus, from 500 to 700 °C bubbles of volatiles increasingly inhibited external heat transfer from the bed to a pyrolysing particle. Also, at 700 °C, the bed's radiation was largely absorbed by the products of pyrolysis inside the bubbles. After a particle's centre had reached the bed's temperature, volatiles continued to appear slowly at a rate, probably controlled by chemical kinetics. The identity of the rate-determining step is discussed for spruce particles of different sizes and beds at various temperatures. However, it is clear that the FID, with its rapid response and sensitivity, revealed new details of the pyrolysis of small particles (2 – 7 mm) of wood in a fluidised bed. For example, the thermal decomposition of spruce involves at least two separate, endothermic reactions and a final, exothermic step
Capacity-achieving Spatially Coupled Sparse Superposition Codes with AMP Decoding
No full textSparse superposition codes, also referred to as sparse regression codes (SPARCs), are a class of codes for efficient communication over the AWGN channel at rates approaching the channel capacity. In a standard SPARC, codewords are sparse linear combinations of columns of an i.i.d. Gaussian design matrix, while in a spatially coupled SPARC the design matrix has a block-wise structure, where the variance of the Gaussian entries can be varied across blocks. A well-designed spatial coupling structure can significantly enhance the error performance of iterative decoding algorithms such as Approximate Message Passing (AMP). In this paper, we obtain a non-asymptotic bound on the probability of error of spatially coupled SPARCs with AMP decoding. Applying this bound to a simple band-diagonal design matrix, we prove that spatially coupled SPARCs with AMP decoding achieve the capacity of the AWGN channel. The bound also highlights how the decay of error probability depends on each design parameter of the spatially coupled SPARC. An attractive feature of AMP decoding is that its asymptotic mean squared error (MSE) can be predicted via a deterministic recursion called state evolution. Our result provides the first proof that the MSE concentrates on the state evolution prediction for spatially coupled designs. Combined with the state evolution prediction, this result implies that spatially coupled SPARCs with the proposed band-diagonal design are capacity-achieving. Using the proof technique used to establish the main result, we also obtain a concentration inequality for the MSE of AMP applied to compressed sensing with spatially coupled design matrices. Finally, we provide numerical simulation results that demonstrate the finite length error performance of spatially coupled SPARCs. The performance is compared with coded modulation schemes that use LDPC codes from the DVB-S2 standard
Characteristics of changeable systems across value chains
No full textEngineering changes (ECs) are inevitable for businesses due to increasing innovation, shorter lifecycles, technology and process improvements and cost reduction initiatives. The ECs could propagate and cause further changes due to existing system dependencies, which can be challenging. Hence, change management (CM) is a relevant discipline, which aims to reduce the impact of changes. EC assessment methods form the basis of CM that support in assessing system dependencies and the impact of changes. However, understanding of which factors influence the changeability across value chains (VCs) is limited. This research adopted a VC approach to EC assessment. Dependencies in products and processes were captured, followed by risk (i.e. likelihood x impact) assessment of ECs using change prediction method (CPM). Four industrial case studies were conducted (3x automotive, 1x furniture manufacturing) to identify design (product) and manufacturing (process) elements with high risk to be affected by ECs. Based on the case results, characteristics were identified that influence changeability across VC. This contributed to the CM domain while businesses could also use the results to assess ECs across VC, and improve the design of products and processes by increasing their changeability across VC e.g. by proactive decoupling or reactive handling of system dependencies