Study on the mechanism of arsenic removal from aqueous solution by capacitive deionization technology

　　砷的毒害是一種全球性的地下水污染問題，而台灣現有之地下水質砷污染來源包含地質關係而造成之西南沿海與宜蘭地區地下水，以及人為導致之土壤污染列管場址，因土壤中砷傳輸至地下水體，導致地下水中砷濃度超過地下水污染管制標準，若農業及漁業用水引用當地地下水做為水源，將造成生物或人體的潛在危害性。 　　電容去離子技術(Capacitive Deionization, CDI)為一種節能、清淨、無需使用化學藥劑，且不產生二次污染物之新穎電化學處理技術，其原理是利用外加電場的控制與奈米孔洞碳電極的高比表面積，基於電荷分離機制，先以外部供電方式充電，在處理水體中產生電場，利用庫倫作用力將水中離子電吸附於電極表面上，在奈米孔洞間形成電雙層，進而產出乾淨水體。於本研究試驗結果顯示，CDI系統藉由電場的施加與活性碳電極本身的多孔性與高比表面積，可對於水中低濃度(0.2 mg/L)的砷具有良好的移除效果，且當施加1.2 V電壓於CDI系統中，五價砷(H2AsO4－)可直接被電吸附在活性碳電極上而被去除，三價砷(H3AsO30)則是會因電場作用，被氧化成五價砷型態再被電吸附去除。而砷在不同離子強度與天然有機物的干擾下，仍具有部分去除效果，顯示活性碳電極之CDI系統對於砷有一定的選擇性。而經電吸附過後的活性碳電極以SEM與XPS進行分析，由SEM掃描結果可觀察到電極表面結構中，並無明顯晶體被發現；而XPS之全圖譜分析與單元素分析結果，亦無偵測到砷的波鋒，顯示在CDI系統中，砷並不以電沉積之方式被去除，而是以電吸附反應為主。　　Capacitive deionization (CDI), or referred to electrosorption process, has been regarded as a novel water purification technology, which has many advantages including low operating pressure, low energy consumption, no secondary waste, and easy regeneration. The mechanism behind CDI to remove ionic species from water is based on the charge separation, in which nanoporous carbon electrodes are charged and discharged to store and to release large quantities of ions, respectively. 　　In this study, CDI process can electrostatically separate low concentration (0.2 mg/L) arsenic from aqueous solutions. Furthermore, arsenate (As(V)) can be directly removed by electrosorption at 1.2 V because of its negative charge. The results indicate that the mechanism of arsenite (As(III)) removal in CDI system could be involved with the oxidation of As(III) to As(V) which can be removed by electrosorption. In the competitive experiments, although, the presence of sodium chloride and nature organic matter (NOM) obviously interfered with the electrosorption behavior of arsenic, there was still electrosorption capacity for arsenic. Notably, the CDI system still showed good electrosorption selectivity of As. The surface of activated carbon was investigated with SEM and XPS. As evidenced, there was no visible electrodeposition of arsenic on the electrode surface, demonstrating that arsenic was removed by electrosorption

Developing a Stacked Ensemble-Based Classification Scheme to Predict Second Primary Cancers in Head and Neck Cancer Survivors

Despite a considerable expansion in the present therapeutic repertoire for other malignancy managements, mortality from head and neck cancer (HNC) has not significantly improved in recent decades. Moreover, the second primary cancer (SPC) diagnoses increased in patients with HNC, but studies providing evidence to support SPCs prediction in HNC are lacking. Several base classifiers are integrated forming an ensemble meta-classifier using a stacked ensemble method to predict SPCs and find out relevant risk features in patients with HNC. The balanced accuracy and area under the curve (AUC) are over 0.761 and 0.847, with an approximately 2% and 3% increase, respectively, compared to the best individual base classifier. Our study found the top six ensemble risk features, such as body mass index, primary site of HNC, clinical nodal (N) status, primary site surgical margins, sex, and pathologic nodal (N) status. This will help clinicians screen HNC survivors before SPCs occur