The presence of heavy metal ions in the environment is a long-lasting problem that requires the simultaneous detection of Hg(II) and Pb(II) which is both vital and challenging. This present study examines a simplified and effective approach for synthesizing multi-walled carbon nanotube–copper manganese oxide (MWCNT–CuMn2O4) and multi-walled carbon nanotube–zinc manganese oxide (MWCNT–ZnMn2O4) nanocomposites for electrochemical detection of heavy metal ions. The nanocomposites MWCNT–CuMn2O4 and MWCNT–ZnMn2O4 exceptional electrochemical performance was evaluated using Square Wave Anodic Stripping Voltammetry (SWASV). The fabricated MWCNT–ZnMn2O4 demonstrated lower values of Electrochemical Impedance Spectroscopy (EIS) with charge transfer resistance (Rct) of approximately 34.13 Ω. Remarkably, the MWCNT–ZnMn2O4 electrochemical sensor exhibited the widest linear ranges of 0.5–10 μM with sensitive detection limits (0.011 μM for Hg(II) and 0.014 μM for Pb(II)). Interestingly, the MWCNT–ZnMn2O4 sensor showed excellent capability in detecting Hg(II) and Pb(II) in real water samples with a recovery percentage of 94.1% and 91.3%. Overall, the MWCNT–ZnMn2O4 modified GCE showcased superior selectivity, sensitivity, reproducibility, stability, and repeatability.
{"title":"Exploring the synchronized effect of MWCNT/X-manganate (X-Cu, Zn) nanocomposite for the sensitive and selective electrochemical detection of Hg(II) and Pb(II) in water","authors":"Xingpu Qi, Ping Liu, Fang Yao, Mengli Zhao, Xuanyu Shen, Zhengyun Wang","doi":"10.1007/s44211-024-00652-1","DOIUrl":"10.1007/s44211-024-00652-1","url":null,"abstract":"<div><p>The presence of heavy metal ions in the environment is a long-lasting problem that requires the simultaneous detection of Hg(II) and Pb(II) which is both vital and challenging. This present study examines a simplified and effective approach for synthesizing multi-walled carbon nanotube–copper manganese oxide (MWCNT–CuMn<sub>2</sub>O<sub>4</sub>) and multi-walled carbon nanotube–zinc manganese oxide (MWCNT–ZnMn<sub>2</sub>O<sub>4</sub>) nanocomposites for electrochemical detection of heavy metal ions. The nanocomposites MWCNT–CuMn<sub>2</sub>O<sub>4</sub> and MWCNT–ZnMn<sub>2</sub>O<sub>4</sub> exceptional electrochemical performance was evaluated using Square Wave Anodic Stripping Voltammetry (SWASV). The fabricated MWCNT–ZnMn<sub>2</sub>O<sub>4</sub> demonstrated lower values of Electrochemical Impedance Spectroscopy (EIS) with charge transfer resistance (<i>R</i><sub>ct</sub>) of approximately 34.13 Ω. Remarkably, the MWCNT–ZnMn<sub>2</sub>O<sub>4</sub> electrochemical sensor exhibited the widest linear ranges of 0.5–10 μM with sensitive detection limits (0.011 μM for Hg(II) and 0.014 μM for Pb(II)). Interestingly, the MWCNT–ZnMn<sub>2</sub>O<sub>4</sub> sensor showed excellent capability in detecting Hg(II) and Pb(II) in real water samples with a recovery percentage of 94.1% and 91.3%. Overall, the MWCNT–ZnMn<sub>2</sub>O<sub>4</sub> modified GCE showcased superior selectivity, sensitivity, reproducibility, stability, and repeatability.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":7802,"journal":{"name":"Analytical Sciences","volume":"40 12","pages":"2147 - 2165"},"PeriodicalIF":1.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1007/s44211-024-00645-0
Sisi Guo, Ruoyu Zhang, Tao Wang, Jianfeng Wang
One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.