Factors influencing the cleaning of plant samples with ultrasonic technology

蒸馏水 制浆造纸工业 Mercury(编程语言) 沉浸式(数学) 环境科学 污染 化学 数学 色谱法 计算机科学 生物 工程类 程序设计语言 生态学 纯数学
作者
J. Rodríguez-Alonso,Cristina Cabrales-García,Rocío Millán
出处
期刊:International Journal of Phytoremediation [Informa]
卷期号:25 (3): 359-367 被引量:1
标识
DOI:10.1080/15226514.2022.2085241
摘要

Effective cleaning of biological samples is a critical step in environmental studies. However, the literature lacks standardized cleaning procedures and protocols and there is little information about how even the most basic conditions may affect cleaning efficiency. Here, leaves of different species were first exposed to the soil naturally containing mercury particles and then washed in an ultrasound water bath under the following conditions: newly cleaned/reused beakers, water temperature, sample immersion/free-floating, sample quantity, and the number of washing cycles. Additionally, the effects of sample pubescence on cleaning efficacy were also assessed. Results indicated that the best cleaning efficacy was recorded when samples were placed in cold water under forced immersion and beakers were cleaned between washing cycles. At least two of these three conditions were needed for adequate washing. The results also indicated that, for the glabrous leaves, a cumulative leaf surface area of ≤10,000 mm2 was efficiently cleaned after 3-5 washing cycles, while as pubescence increased, 9-11 cycles were needed and often the sample quantity had to be reduced (<5,000 mm2). Our experiments reveal that cleaning can be optimized by applying easy procedures and according to individual sample typology, resulting in faster and more effective cleaning. Novelty statementThe cleaning of samples is a frequent stage in the analytical processes of phytoremediation studies. This work provides new and valuable information to optimize the cleaning of plant samples by simply applying ultrasonic technology and distilled water. In fact, we have tested the influence of some factors never taken into account previously.
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