Out-Of-Plane permeability evaluation of carbon fiber preforms by ultrasonic wave propagation

Out-of-plane permeability of reinforcement preforms is of crucial importance in the infusion of large and thick composite panels, but so far, there are no standard experimental methods for its determination. In this work, an experimental set-up for the measurement of unsaturated through thickness permeability based on the ultrasonic wave propagation in pulse echo mode is presented. A single ultrasonic transducer, working both as emitter and receiver of ultrasonic waves, was used to monitor the through thickness flow front during a vacuum assisted resin infusion experiment. The set-up was tested on three thick carbon fiber preforms, obtained by stacking thermal bonding of balanced or unidirectional plies either by automated fiber placement either by hand lay-up of unidirectional plies. The ultrasonic data were used to calculate unsaturated out-of-plane permeability using Darcy's law. The permeability results were compared with saturated out-of-plane permeability, determined by a traditional gravimetric method, and validated by some analytical models. The results demonstrated the feasibility and potential of the proposed set-up for permeability measurements thanks to its noninvasive character and the one-side access

An Overview of the Measurement of Permeability of Composite Reinforcements

Liquid composite molding (LCM) is a class of fast and cheap processes suitable for the fabrication of large parts with good geometrical and mechanical properties. One of the main steps in an LCM process is represented by the filling stage, during which a reinforcing fiber preform is impregnated with a low-viscosity resin. Darcy’s permeability is the key property for the filling stage, not usually available and depending on several factors. Permeability is also essential in computational modeling to reduce costly trial-and-error procedures during composite manufacturing. This review aims to present the most used and recent methods for permeability measurement. Several solutions, introduced to monitor resin flow within the preform and to calculate the in-plane and out-of-plane permeability, will be presented. Finally, the new trends toward reliable methods based mainly on non-invasive and possibly integrated sensors will be described

Agriculture 4.0: A systematic literature review on the paradigm, technologies and benefits

Demographics will increase the demand for food and reduce the availability of labour in many countries all over the world. Moreover, scarcity of natural resources, climate change and food waste these are issues that are strongly impacting the agricultural sector and undermining sus-tainability. Digitalisation is expected to be a driving force in tackling these problems that are characterising agriculture. In particular, the adoption of digital technologies to support processes in the primary sector goes by the name of Agriculture 4.0. Although the number of contributions related to these issues is constantly growing, several areas are still unexplored or not fully addressed. This paper addresses the adoption of digital technologies and investigates the appli-cation domain of these technologies, presenting a systematic review of the literature on this subject. Moreover, this research shed light on the technologies adopted and related benefits. Hence, the research has turned its attention to the description of the main pillars, such as the categorisation of its main application domains and enabling technologies. The results of the research show that the different technologies applied in the various fields of application provide benefits both in terms of efficiency (cost reduction, farm productivity) and reduced environ-mental impact and increased sustainability

Production and characterization of polyethylene terephthalate nanoparticles

Microplastic (MP) pollution represents one of the biggest environmental problems that is further exacerbated by the continuous degradation in the marine environment of MPs to nanoplastics (NPs). The most diffuse plastics in oceans are commodity polymers, mainly thermoplastics widely used for packaging, such as polyethylene terephthalate (PET). However, the huge interest in the chemical vector role of micro/nanoplastics, their fate and negative effects on the environment and human health is still under discussion and the research is still sparse due also to the difficulties of sampling MPs and NPs from the environment or producing NPs in laboratory. Moreover, the research on MPs and NPs pollution relies on the availability of engineered nanoparticles similar to those present in the marine environment for toxicological, transport and adsorption studies in biological tissues as well as for wastewater remediation studies. This work aims to develop an easy, fast and scalable procedure for the production of representative model nanoplastics from PET pellets. The proposed method, based on a simple and economic milling process, has been optimized considering the peculiarities of the polymer. The results demonstrated the reliability of the method for preparing particle suspensions for aquatic microplastic research, with evident advantages compared to the present literature procedures, such as low cost, the absence of liquid nitrogen, the short production time, the high yield of the process, stability, reproducibility and polydisperse size distribution of the produced water dispersed nanometric PET

Potential of Cellulose-Based Superabsorbent Hydrogels as Water Reservoir in Agriculture

The present work deals with the development of a biodegradable superabsorbent hydrogel, based on cellulose derivatives, for the optimization of water resources in agriculture, horticulture and, more in general, for instilling a wiser and savvier approach to water consumption. The sorption capability of the proposed hydrogel was firstly assessed, with specific regard to two variables that might play a key role in the soil environment, that is, ionic strength and pH. Moreover, a preliminary evaluation of the hydrogel potential as water reservoir in agriculture was performed by using the hydrogel in experimental greenhouses, for the cultivation of tomatoes. The soil-water retention curve, in the presence of different hydrogel amounts, was also analysed. The preliminary results showed that the material allowed an efficient storage and sustained release of water to the soil and the plant roots. Although further investigations should be performed to completely characterize the interaction between the hydrogel and the soil, such findings suggest that the envisaged use of the hydrogel on a large scale might have a revolutionary impact on the optimization of water resources management in agriculture

Ueber die Darstellung von Aldehyden aus Organomagnesiumverbindungen

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Experimental and numerical study of vacuum resin infusion of stiffened carbon fiber reinforced panels

Liquid resin infusion processes are becoming attractive for aeronautic applications as an alternative to conventional autoclave-based processes. They still present several challenges, which can be faced only with an accurate simulation able to optimize the process parameters and to replace traditional time-consuming trial-and-error procedures. This paper presents an experimentally validated model to simulate the resin infusion process of an aeronautical component by accounting for the anisotropic permeability of the reinforcement and the chemophysical and rheological changes in the crosslinking resin. The input parameters of the model have been experimentally determined. The experimental work has been devoted to the study of the curing kinetics and chemorheological behavior of the thermosetting epoxy matrix and to the determination of both the in-plane and out-of-plane permeability of two carbon fiber preforms using an ultrasonic-based method, recently developed by the authors. The numerical simulation of the resin infusion process involved the modeling of the resin flow through the reinforcement, the heat exchange in the part and within the mold, and the crosslinking reaction of the resin. The time necessary to fill the component has been measured by an optical fiber-based equipment and compared with the simulation results

MEG Upgrade Proposal

We propose the continuation of the MEG experiment to search for the charged lepton flavour violating decay (cLFV) \mu \to e \gamma, based on an upgrade of the experiment, which aims for a sensitivity enhancement of one order of magnitude compared to the final MEG result, down to the $6 \times 10^{-14}$ level. The key features of this new MEG upgrade are an increased rate capability of all detectors to enable running at the intensity frontier and improved energy, angular and timing resolutions, for both the positron and photon arms of the detector. On the positron-side a new low-mass, single volume, high granularity tracker is envisaged, in combination with a new highly segmented, fast timing counter array, to track positron from a thinner stopping target. The photon-arm, with the largest liquid xenon (LXe) detector in the world, totalling 900 l, will also be improved by increasing the granularity at the incident face, by replacing the current photomultiplier tubes (PMTs) with a larger number of smaller photosensors and optimizing the photosensor layout also on the lateral faces. A new DAQ scheme involving the implementation of a new combined readout board capable of integrating the diverse functions of digitization, trigger capability and splitter functionality into one condensed unit, is also under development. We describe here the status of the MEG experiment, the scientific merits of the upgrade and the experimental methods we plan to use.Comment: A. M. Baldini and T. Mori Spokespersons. Research proposal submitted to the Paul Scherrer Institute Research Committee for Particle Physics at the Ring Cyclotron. 131 Page

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation, volcanic activity or aridity. Their analytical detection depends on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge and often restrict sampling strategies. To help overcome these limitations, we present a framework based on Flow Imaging Microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify 7 commonly found classes: mineral dust, felsic and basaltic volcanic ash (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber) and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system’s potentials and limitations with respect to the detection of mineral dust, pollen grains and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non destructive, fully reproducible and does not require any sample preparation step. The presented framework can bolster research in the field, by cutting down processing time, supporting human-operated microscopy and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented