“…As expected, the energy input for per mole Cl – generation was inversely related to the coulombic efficiency, and the energy input of MEC-RVC can be as low as 0.034 kW h mol –1 (Figure B). The smaller current and the better dechlorination performance contributed to the more excellent electrical efficiency of MEC-RVC . Both the dechlorination capacity and energy utilization efficiency of MECs illustrated that RVC outperformed the highly electronically conductive NF.…”
Chlorobenzene (CB) is typically emitted during industry
and waste
incineration, posing hazards to the safety of humans and the ecosystem.
Successful CB elimination has been achieved in microbial electrolysis
cells (MECs), whereas little is known about elimination effectiveness
in response to the structural and functional characteristics of biofilms.
Herein, the bacterial viability and functional gene abundance of biofilms
in MECs with carbon cloth (CC), reticulated vitreous carbon (RVC),
and nickel foam (NF) as cathodes are emphasized. Experimental results
reveal that bacterial viability on RVC is 62 and 92% higher than that
of CC and NF, respectively, achieving 30 and 65% superior CB removal
efficiency. Notably, the MEC with RVC outperforms that with the highly
conductive NF due to low electron loss, causing a twice as much increase
in coulombic efficiency. Metagenomic analysis indicates that the CB
degrader (i.e., Pandoraea), electrochemically
active bacteria (i.e., Chryseobacterium), and genes responsible for CB metabolism (i.e., todD and clcB) in RVC biofilms increase by over 30%,
fourfold, and 10%, respectively, compared to CC and NF. Moreover,
CB degradation is found to occur via two possible pathways: (1) thorough
elimination by dechlorination, hydrolysis, and oxidation after conversion
to 3-chlorocatechol, and (2) conversion into phenol and benzoate for
complete degradation.
“…As expected, the energy input for per mole Cl – generation was inversely related to the coulombic efficiency, and the energy input of MEC-RVC can be as low as 0.034 kW h mol –1 (Figure B). The smaller current and the better dechlorination performance contributed to the more excellent electrical efficiency of MEC-RVC . Both the dechlorination capacity and energy utilization efficiency of MECs illustrated that RVC outperformed the highly electronically conductive NF.…”
Chlorobenzene (CB) is typically emitted during industry
and waste
incineration, posing hazards to the safety of humans and the ecosystem.
Successful CB elimination has been achieved in microbial electrolysis
cells (MECs), whereas little is known about elimination effectiveness
in response to the structural and functional characteristics of biofilms.
Herein, the bacterial viability and functional gene abundance of biofilms
in MECs with carbon cloth (CC), reticulated vitreous carbon (RVC),
and nickel foam (NF) as cathodes are emphasized. Experimental results
reveal that bacterial viability on RVC is 62 and 92% higher than that
of CC and NF, respectively, achieving 30 and 65% superior CB removal
efficiency. Notably, the MEC with RVC outperforms that with the highly
conductive NF due to low electron loss, causing a twice as much increase
in coulombic efficiency. Metagenomic analysis indicates that the CB
degrader (i.e., Pandoraea), electrochemically
active bacteria (i.e., Chryseobacterium), and genes responsible for CB metabolism (i.e., todD and clcB) in RVC biofilms increase by over 30%,
fourfold, and 10%, respectively, compared to CC and NF. Moreover,
CB degradation is found to occur via two possible pathways: (1) thorough
elimination by dechlorination, hydrolysis, and oxidation after conversion
to 3-chlorocatechol, and (2) conversion into phenol and benzoate for
complete degradation.
“…DL is to learn the sample data's internal law and representation level. The information obtained in the learning process is beneficial to interpreting data such as text, images, and sound [ 8 , 9 ]. Its ultimate goal is to make the machine have the ability to analyze and learn like human beings and recognize characters, images, sounds, and other data.…”
This exploration intends to remove chloride ions in production and life, enhance buildings’ durability, and protect the natural environment from pollution. The current dechlorination technology is discussed based on the relevant theories, such as the lightweight deep learning (DL) model and chloride ion characteristics. Next, data statistics and comparative analysis methods are used to study the adsorption and desorption performance of dechlorination adsorbents. Finally, the lightweight DL model is introduced into the chloride diffusion prediction experiment of slag powder and fly ash concrete. The results show that in the study of dechlorination adsorption performance, the chloride ion concentration decreases gradually with the extension of adsorption time. However, with the increasing temperature, the chloride ion removal rate is increasing. The removal rate of chloride ions in water can decrease slowly with the increase of adsorbent. Therefore, selecting the 2 mol/L sodium hydroxide as the alkali concentration for adsorbent regeneration is the most appropriate. Besides, the regeneration performance of the adsorbent gradually declines with the increase of sodium chloride concentration in the solution. The lightweight DL model is applied to the chloride diffusion prediction experiment of slag powder and fly ash concrete. It is found that when the curing age is selected at 18 days, 90 days, and 180 days, respectively, the error between the lightweight DL model and the experimental results is about 0.2. It shows that the lightweight DL model is feasible for predicting the diffusion of chloride ions. Therefore, this exploration designs and studies the dechlorination experiment based on the lightweight DL model, which provides a new theoretical basis and optimization direction for removing chloride ions in the future industry.
“…For instance, H 2 generated in the cathode drives the growth of Dehalogenimonas alkenigignens and its dechlorination of 1, 2-dichloropropane to non-toxic propene ( Fig. 8 ) [ 37 ]. Furthermore, overexpressing the EET-related omcX is suggested to more easily receive the electrical stimulation from the electrode.…”
Section: Synthetic Biology Techniques To Improve the Performance Of M...mentioning
confidence: 99%
“… Fig. 8 Schematic diagram of BES catalyzing dechlorination reactions in which H 2 generated in the cathode drives the growth of Dehalogenimonas alkenigignens and its dechlorination of 1, 2-dichloropropane to non-toxic propene (reproduced with permission from Elsevier) [ 37 ]. …”
Section: Synthetic Biology Techniques To Improve the Performance Of M...mentioning
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