1
|
Bakola V, Kotrotsiou O, Ntziouni A, Dragatogiannis D, Plakantonaki N, Trapalis C, Charitidis C, Kiparissides C. Development of Composite Nanostructured Electrodes for Water Desalination via Membrane Capacitive Deionization. Macromol Rapid Commun 2024; 45:e2300640. [PMID: 38184786 DOI: 10.1002/marc.202300640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/22/2023] [Indexed: 01/08/2024]
Abstract
Novel two-layer nanostructured electrodes are successfully prepared for their application in membrane capacitive deionization (MCDI) processes. Nanostructured carbonaceous materials such as graphene oxide (GO) and carbon nanotubes (CNTs), as well as activated carbon (AC) are dispersed in a solution of poly(vinyl alcohol) (PVA), mixed with polyacrylic acid (PAA) or polydimethyldiallylammonium chloride (PDMDAAC), and subsequently cast on the top surface of an AC-based modified graphite electrode to form a thin composite layer that is cross-linked with glutaraldehyde (GA). Cyclic voltammetry (CV) is performed to investigate the electrochemical properties of the composite electrodes and desalination experiments are conducted in batch mode using a MCDI unit cell to investigate the effects of i) the nanostructured carbonaceous material, ii) its concentration in the polymer blend, and iii) the molecular weight of the polymers on the desalination efficiency of the system. Comparative studies with commercial membranes are performed proving that the composite nanostructured electrodes are more efficient in salt removal. The improved performance of the composite electrodes is attributed to the ion exchange properties of the selected polymers and the increased specific capacitance of the nanostructured carbonaceous materials. This research paves the way for wider application of MCDI in water desalination.
Collapse
Affiliation(s)
- Veroniki Bakola
- Centre for Research and Technology Hellas (CERTH), Chemical Process and Energy Resources Institute (CPERI), 6th km Charilaou-Thermi Rd, Thermi, Thessaloniki, 57001, Greece
- Aristotle University of Thessaloniki (AUTH), Department of Chemical Engineering, University Campus, Thessaloniki, 54124, Greece
| | - Olympia Kotrotsiou
- Centre for Research and Technology Hellas (CERTH), Chemical Process and Energy Resources Institute (CPERI), 6th km Charilaou-Thermi Rd, Thermi, Thessaloniki, 57001, Greece
| | - Afroditi Ntziouni
- Research Unit of Advanced, Composite, Nano-Materials and Nanotechnology, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens, 15780, Greece
| | - Dimitris Dragatogiannis
- DELTA-MPIS, Technological Park of Lefkippos, Neapoleos and Patriarchou Grigoriou St, Agia Paraskevi, Attikis, Athens, 15341, Greece
| | - Niki Plakantonaki
- Institute of Nanoscience and Nanotechnology, N.C.S.R. "Demokritos", Agia Paraskevi, Attikis, Athens, 15341, Greece
| | - Christos Trapalis
- Institute of Nanoscience and Nanotechnology, N.C.S.R. "Demokritos", Agia Paraskevi, Attikis, Athens, 15341, Greece
| | - Costas Charitidis
- Research Unit of Advanced, Composite, Nano-Materials and Nanotechnology, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens, 15780, Greece
| | - Costas Kiparissides
- Centre for Research and Technology Hellas (CERTH), Chemical Process and Energy Resources Institute (CPERI), 6th km Charilaou-Thermi Rd, Thermi, Thessaloniki, 57001, Greece
- Aristotle University of Thessaloniki (AUTH), Department of Chemical Engineering, University Campus, Thessaloniki, 54124, Greece
| |
Collapse
|
2
|
Konstantakopoulos FS, Georga EI, Fotiadis DI. An Automated Image-Based Dietary Assessment System for Mediterranean Foods. IEEE Open J Eng Med Biol 2023; 4:45-54. [PMID: 37223053 PMCID: PMC10202193 DOI: 10.1109/ojemb.2023.3266135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/03/2023] [Accepted: 03/26/2023] [Indexed: 05/25/2023] Open
Abstract
Goal: The modern way of living has significantly influenced the daily diet. The ever-increasing number of people with obesity, diabetes and cardiovascular diseases stresses the need to find tools that could help in the daily intake of the necessary nutrients. Methods: In this paper, we present an automated image-based dietary assessment system of Mediterranean food, based on: 1) an image dataset of Mediterranean foods, 2) on a pre-trained Convolutional Neural Network (CNN) for food image classification, and 3) on stereo vision techniques for the volume and nutrition estimation of the food. We use a pre-trained CNN in the Food-101 dataset to train a deep learning classification model employing our dataset Mediterranean Greek Food (MedGRFood). Based on the EfficientNet family of CNNs, we use the EfficientNetB2 both for the pre-trained model and its weights evaluation, as well as for classifying food images in the MedGRFood dataset. Next, we estimate the volume of the food, through 3D food reconstruction of two images taken by a smartphone camera. The proposed volume estimation subsystem uses stereo vision techniques and algorithms, and needs the input of two food images to reconstruct the point cloud of the food and to compute its quantity. Results: The classification accuracy where true class matches with the most probable class predicted by the model (Top-1 accuracy) is 83.8%, while the accuracy where true class matches with any one of the 5 most probable classes predicted by the model (Top-5 accuracy) is 97.6%, for the food classification subsystem. The food volume estimation subsystem achieves an overall mean absolute percentage error 10.5% for 148 different food dishes. Conclusions: The proposed automated image-based dietary assessment system provides the capability of continuous recording of health data in real time.
Collapse
Affiliation(s)
- Fotios S. Konstantakopoulos
- Unit of Medical Technology and Intelligent Information Systems, Materials Science and Engineering DepartmentUniversity of IoanninaGR45110IoanninaGreece
- Biomedical Research InstituteFORTH, University of IoanninaGR45110IoanninaGreece
| | - Eleni I. Georga
- Unit of Medical Technology and Intelligent Information Systems, Materials Science and Engineering DepartmentUniversity of IoanninaGR45110IoanninaGreece
- Biomedical Research InstituteFORTH, University of IoanninaGR45110IoanninaGreece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Materials Science and Engineering DepartmentUniversity of IoanninaGR45110IoanninaGreece
- Biomedical Research InstituteFORTH, University of IoanninaGR45110IoanninaGreece
| |
Collapse
|