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- Islam, Jamil, et al.
(författare)
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Graphene as thinnest coating on copper electrodes in microbial methanol fuel cells
- 2023
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Ingår i: ACS Nano. - : American Chemical Society (ACS). - 1936-0851 .- 1936-086X. ; 17:1, s. 137-145
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Tidskriftsartikel (refereegranskat)abstract
- Dehydrogenation of methanol (CH3OH) into direct current (DC) in fuel cells can be a potential energy conversion technology. However, their development is currently hampered by the high cost of electrocatalysts based on platinum and palladium, slow kinetics, the formation of carbon monoxide intermediates, and the requirement for high temperatures. Here, we report the use of graphene layers (GL) for generating DC electricity from microbially driven methanol dehydrogenation on underlying copper (Cu) surfaces. Genetically tractable Rhodobacter sphaeroides 2.4.1 (Rsp), a nonarchetypical methylotroph, was used for dehydrogenating methanol at the GL-Cu surfaces. We use electrochemical methods, microscopy, and spectroscopy methods to assess the effects of GL on methanol dehydrogenation by Rsp cells. The GL-Cu offers a 5-fold higher power density and 4-fold higher current density compared to bare Cu. The GL lowers charge transfer resistance to methanol dehydrogenation by 4 orders of magnitude by mitigating issues related to pitting corrosion of underlying Cu surfaces. The presented approach for catalyst-free methanol dehydrogenation on copper electrodes can improve the overall sustainability of fuel cell technologies.
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| 2. |
- Oruganti, Raj Kumar, et al.
(författare)
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Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery : a critical review
- 2023
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Ingår i: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 876
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Forskningsöversikt (refereegranskat)abstract
- The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.
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