Quantitative NMR monitoring of liquid ingress into repellent heterogeneous layered fabrics

Fabrics which are water repellent and repellent to other liquids are often constructed using multiple layers of material. Such a construction is preferable to a single layer of a liquid-repellent textile because, under the action of an applied pressure, ingress of a liquid through the first layer can be halted by the second or subsequent layers. In the quantitative investigation of this problem, current techniques provide limited information on the progress and distribution of the liquid as it ingresses into a fabric. Moreover, many techniques require that the material is delaminated prior to analysis, and cannot be conducted in real time to measure the progress of a liquid through the textile substrate. In this work we demonstrate that unilateral NMR, which allows signal to be collected from a volume of interest in a material residing above the instrument, can be a powerful tool to quantitatively monitor the ingress of a liquid through a layered sample exhibiting pronounced heterogeneities in repellency. A known volume of oil was placed on the top of a model textile sample composed of three 80 μm thick layers. Spatially resolved one dimensional vertical NMR profiles of the system were acquired as a function of the pressure vertically applied to the top of the sample. These profiles show that the absolute liquid volume present in each layer of textile can routinely be measured within 4 min with a spatial resolution of 15 μm. If each individual layer exhibits different repellency to the test liquid, the complexity of the dynamics of the ingress can be investigated in great detail. An elegant application of the unilateral instrument was obtained in which the sensitive volume matched the region of interest of the individual layers of the textile under investigation

Quantification of MRI sensitivity for mono-disperse microbubbles to measure subatmospheric fluid pressure changes

It would be very beneficial to perform MRI of fluids and sense the fluid pressure changes. Our aim is to demonstrate a contrast agent capable of MR sensitivity to sub-atmospheric pressure changes. To achieve this, monodisperse microbubbles were prepared with an optically measured mean radius of 1.4 ± 0.8 μm. A repeated pressure change cycle was applied on the microbubble contrast agent, until it produced an MR signal change solely due to the bubble radius change. The bubbles’ contribution to the relaxation rate before and after applying sub-atmospheric pressure changes was estimated and its echo time dependence modelled, so as to inform the mean radius change. The periodic subatmospheric pressure change was further applied until the MR signal change was only due to the bubble radius change. An excellent MR sensitivity of 28 % bar-1 is demonstrated, bubble radii of 2.4 and 1.8 μm are numerically estimated before and after the application of pressure, and the simulations are further used to estimate the optimum bubble radius maximising the MR sensitivity to a small change in radius

Towards MRI microarrays

Superparamagnetic iron oxide nanometre scale particles have been utilised as contrast agents to image staked target binding oligonucleotide arrays using MRI to correlate the signal intensity and relaxation times in different NMR fluids

Identification of the honey bee swarming process by analysing the time course of hive vibrations

Honey bees live in groups of approximately 40,000 individuals and go through their reproductive cycle by the swarming process, during which the old queen leaves the nest with numerous workers and drones to form a new colony. In the spring time, many clues can be seen in the hive, which sometimes demonstrate the proximity to swarming, such as the presence of more or less mature queen cells. In spite of this the actual date and time of swarming cannot be predicted accurately, as we still need to better understand this important physiological event. Here we show that, by means of a simple transducer secured to the outside wall of a hive, a set of statistically independent instantaneous vibration signals of honey bees can be identified and monitored in time using a fully automated and non-invasive method. The amplitudes of the independent signals form a multi-dimensional time-varying vector which was logged continuously for eight months. We found that combined with specifically tailored weighting factors, this vector provides a signature highly specific to the swarming process and its build up in time, thereby shedding new light on it and allowing its prediction several days in advance. The output of our monitoring method could be used to provide other signatures highly specific to other physiological processes in honey bees, and applied to better understand health issues recently encountered by pollinators

Detection of virgin olive oil adulteration using low field unilateral NMR

The detection of adulteration in edible oils is a concern in the food industry, especially for the higher priced virgin olive oils. This article presents a low field unilateral nuclear magnetic resonance (NMR) method for the detection of the adulteration of virgin olive oil that can be performed through sealed bottles providing a non-destructive screening technique. Adulterations of an extra virgin olive oil with different percentages of sunflower oil and red palm oil were measured with a commercial unilateral instrument, the profile NMR-Mouse. The NMR signal was processed using a 2-dimensional Inverse Laplace transformation to analyze the transverse relaxation and self-diffusion behaviors of different oils. The obtained results demonstrated the feasibility of detecting adulterations of olive oil with percentages of at least 10% of sunflower and red palm oils

Analysis of clogging in constructed wetlands using magnetic resonance

In this work we demonstrate the potential of permanent magnet based magnetic resonance sensors to monitor and assess the extent of pore clogging in water filtration systems. The performance of the sensor was tested on artificially clogged gravel substrates and on gravel bed samples from constructed wetlands used to treat wastewater. Data indicate that the spin lattice relaxation time is linearly related to the hydraulic conductivity in such systems. In addition, within biologically active filters we demonstrate the ability to determine the relative ratio of biomass to abiotic solids, a measurement which is not possible using alternative techniques

Influence of polymerisation conditions on the properties of polymer/clay nanocomposite hydrogels

Free-radical polymerisation of acrylamide derivatives in the presence of exfoliated clay platelets has recently emerged as a new technique for the synthesis of strong and tough nanocomposite hydrogels (NCHs) with a unique hybrid organic/inorganic network structure. The central intent of many research studies in the field of NCHs conducted so far was to change hydrogel properties with the introduction of various clays and variation of the clay content. Here, we demonstrate that the properties of NCHs significantly vary depending on initiating conditions used for hydrogel synthesis via in situ polymerisation in solutions of high monomer concentrations (above 1 mol L-1 ). A unique, complementary combination of real-time dynamic rheology and pulsed NMR/MRI has been used to study the influence of the composition of a redox initiating system on the gelation process and hydrogel properties. The molar ratio of the persulphate initiator to tertiary amine activator affects the polymerisation kinetics, morphology and mechanical properties of the hydrogels. We further show that activator-dominated systems tend to produce hydrogels with higher storage modulus and lower water intake. This trend is attributed to the increase in the cross-linking degree. From the analysis of the water state in NCH and hydrogels prepared with and without an organic cross-linker, it was concluded that clay platelets did not form covalent bonds with polymer molecules but contributed to the formation of a physical network. There is evidence of self-crosslinking of polymer chains during acrylamide polymerisation at high monomer concentration. The composition of the initiating system influences the number of formed self-crosslinks

Honeybee Colony Vibrational Measurements to Highlight the Brood Cycle

Insect pollination is of great importance to crop production worldwide and honey bees are amongst its chief facilitators. Because of the decline of managed colonies, the use of sensor technology is growing in popularity and it is of interest to develop new methods which can more accurately and less invasively assess honey bee colony status. Our approach is to use accelerometers to measure vibrations in order to provide information on colony activity and development. The accelerometers provide amplitude and frequency information which is recorded every three minutes and analysed for night time only. Vibrational data were validated by comparison to visual inspection data, particularly the brood development. We show a strong correlation between vibrational amplitude data and the brood cycle in the vicinity of the sensor. We have further explored the minimum data that is required, when frequency information is also included, to accurately predict the current point in the brood cycle. Such a technique should enable beekeepers to reduce the frequency with which visual inspections are required, reducing the stress this places on the colony and saving the beekeeper time

B-GOOD: Giving Beekeeping Guidance by cOmputatiOnal-assisted Decision making

A key to healthy beekeeping is the Health Status Index (HIS) inspired by EFSA’s Healthy-B toolbox which we will make fully operational, with the active collaboration of beekeepers, by facilitating the coordinated and harmonised flow of data from various sources and by testing and validating each component thoroughly. We envisage a step-by-step expansion of participating apiaries, and will eventually cover all EU biogeographic regions. The key to a sustainable beekeeping is a better understanding of its socio-economics, particularly within local value chains, its relationship with bee health and the human-ecosystem equilibrium of the beekeeping sector and to implement these insights into the data processing and decision making. We will fully integrate socio-economic analyses, identify viable business models tailored to different contexts for European beekeeping and determine the carrying capacity of the landscape. In close cooperation with the EU Bee Partnership, an EU-wide bee health and management data platform and affiliated project website will be created to enable sharing of knowledge and learning between scientists and stakeholders within and outside the consortium. We will utilise and further expand the classification of the open source IT-application for digital beekeeping, BEEP, to streamline the flow of data related to beekeeping management, the beehive and its environment (landscape, agricultural practices, weather and climate) from various sources. The dynamic bee health and management data platform will allow us to identify correlative relationships among factors impacting the HSI, assess the risk of emerging pests and predators, and enable beekeepers to develop adaptive management strategies that account for local and EU-wide issues. Reinforcing and establishing, where necessary, new multi-actor networks of collaboration will engender a lasting learning and innovation system to ensure social-ecological resilient and sustainable beekeeping

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping