Low-cost heat assisted ambient ionization source for mass spectrometry in food and pharmaceutical screening.

Ambient Ionization Mass Spectrometry (AI-MS) techniques have revolutionized analytical chemistry by enabling rapid analysis of samples under atmospheric conditions with minimal to no preparation. In this study, the optimization of a cold atmospheric plasma for the analysis of food and pharmaceutical samples, liquid and solid, using a Heat-Assisted Dielectric Barrier Discharge Ionization (HA-DBDI) source is described. A significant enhancement in analyte signals was observed when a heating element was introduced into the design, potentially allowing for greater sensitivity. Furthermore, the synergy between the inlet temperature of the mass spectrometer and the heating element allows for precise control over the analytical process, leading to improved detection sensitivity and selectivity. Incorporating computational fluid dynamic (CFD) simulations into the study elucidated how heating modifications can influence gas transport properties, thereby facilitating enhanced analyte detection and increased signal intensity. These findings advance the understanding of HA-DBDI technology and provide valuable insights for optimizing AI-MS methodologies for a wide range of applications in food and pharmaceutical analysis.

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis.

Accurate photovoltaic (PV) diagnosis is of paramount importance for reducing investment risk and increasing the bankability of the PV technology. The application of fault diagnostic solutions and troubleshooting on operating PV power plants is vital for ensuring optimal energy harvesting, increased power generation production and optimised field operation and maintenance (O&M) activities. This study aims to give an overview of the existing approaches for PV plant diagnosis, focusing on unmanned aerial vehicle (UAV)-based approaches, that can support PV plant diagnostics using imaging techniques and data-driven analytics. This review paper initially outlines the different degradation mechanisms, failure modes and patterns that PV systems are subjected and then reports the main diagnostic techniques. Furthermore, the essential equipment and sensor's requirements for diagnosing failures in monitored PV systems using UAV-based approaches are provided. Moreover, the study summarizes the operating conditions and the various failure types that can be detected by such diagnostic approaches. Finally, it provides recommendations and insights on how to develop a fully functional UAV-based diagnostic tool, capable of detecting and classifying accurately failure modes in PV systems, while also locating the exact position of faulty modules.

FLEXGRID - A novel smart grid architecture that facilitates high-RES penetration through innovative flexibility markets towards efficient stakeholder interaction.

The FLEXGRID 2 project develops a digital platform designed to offer Digital Energy Services (DESs) that facilitate energy sector stakeholders (i.e. DSOs, TSOs, market operators, RES producers, retailers, flexibility aggregators) towards: i) automating and optimizing their investments and operation/management of their systems/assets, and ii) interacting in a dynamic and efficient way with their environment (electricity system) and the rest of the stakeholders. In this way, FLEXGRID envisages secure, sustainable, competitive, and affordable smart grids. A key objective is the incentivization of large-scale bottom-up investments in Distributed Energy Resources (DERs) through innovative smart grid management. Towards this goal, FLEXGRID develops innovative data models and energy market architectures (with high liquidity and efficiency) that effectively manage smart grids through an advanced TSO-DSO interaction as well as interaction between Transmission Network and Distribution Network level energy markets. Consequently, and through intelligence that exploits the innovation of the proposed market architecture, FLEXGRID develops investment tools able to examine in depth the emerging energy ecosystem and allow in this way: i) the financial sustainability of DER investors, and ii) the market liquidity/efficiency through advanced exploitation of DERs and intelligent network upgrades.

Improved field localization in transcranial magnetic stimulation of the brain with the utilization of a conductive shield plate in the stimulator.

In this paper, a carefully designed conductive shield plate is presented, which helps to improve localization of the electric field distribution induced by transcranial magnetic stimulation for neuron stimulation. The shield plate is introduced between a figure-of-eight coil and the head. In order to accurately predict the field distribution inside the brain and to examine the effects of the shield plate, a realistic head model is constructed from magnetic resonance image data with the help of image processing tools and the finite-element method in three dimensions is employed. Finally, to show the improvements obtained, the results are compared with two conventional coil designs. It is found that an incorporation of the shield plate into the coil, effectively improves the induced field localization by more than 50%, and prevents other parts of the brain from exposure to high pulsed magnetic fields.

Numerical simulation of DNA sample preconcentration in microdevice electrophoresis.

A numerical model is presented for the accurate and efficient prediction of preconcentration and transport of DNA during sample introduction and injection in microcapillary electrophoresis. The model incorporates conservation laws for the different buffer ions, salt ions, and DNA sample, coupled through a Gaussian electric field to account for the field modifications that cause electromigration. The accuracy and efficiency required to capture the physics associated with such a complex transient problem are realized by the use of the finite element-flux corrected transport (FE-FCT) algorithm in two dimensions. The model has been employed for the prediction of DNA sample preconcentration and transport during electrophoresis in a double-T injector microdevice. To test its validity, the numerical results have been compared with the corresponding experimental data under similar conditions, and excellent agreement has been found. Finally, detailed results from a simulation of DNA sample preconcentration in electrophoretic microdevices are presented using as parameters the electric field strength and the other species concentrations. The effect of the Tris concentration on sample stacking is also investigated. These results demonstrate the great potential offered by the model for future optimization of such microchip devices with respect to significantly enhanced speed and resolution of sample separation.