Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
This study presents the first instance of a crucial route for the efficient removal of boron from effluents using a strategically applied electrosorption technology using nanodiamonds annealed under argon (denoted as A-NDs). We demonstrate a significant enhancement in adsorption capacity for boron removal facilitated by a flow-through electrosorption cell, and outline the results of surface characterization and electrochemical activity tests of the fabricated nanodiamond (ND) anodes (e.g., Pristine ND and A-NDs annealed at 800 and 1200 ℃). To identify the role of DO in the electrosorption system, we compared the results obtained in the natural state (without gas purging) with those obtained with ambient air and N gas purging. In particular, the degree of electrode deterioration (change in the cathode carbon compositional ratio) during the charging process was characterized using X-ray photoelectron spectroscopy. Overall, our system exhibits a favorable boron removal capability (sorption capacity reached 10.5 μmol/g) and energy consumption of <3.4 kWh g-B. Finally, we developed a prediction model for effluent properties using time-series machine learning algorithms based on various electrosorption variables (e.g., DO, pH dynamics, charging/discharging modes and times, and voltage), Through post-process of constructed ML model, voltage showed significant predictive importance. Additionally, the necessity of sequential modeling was emphasized by SHAP analysis. The application of ML algorithms provided a novel approach for the system optimization of electrified water treatment.
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Source |
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http://dx.doi.org/10.1016/j.watres.2024.123080 | DOI Listing |
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