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适应性肽分子作为有前景的高效气体传感器材料:计算机模拟研究

Adaptive Peptide Molecule as the Promising Highly-Efficient Gas-Sensor Material: In Silico Study.

作者信息

Petrunin Alexander A, Rabchinskii Maxim K, Sysoev Victor V, Glukhova Olga E

机构信息

Institute of Physics, Saratov State University, Astrakhanskaya Street 83, 410012 Saratov, Russia.

Ioffe Institute, Politekhnicheskaya Street 26, 194021 Saint Petersburg, Russia.

出版信息

Sensors (Basel). 2023 Jun 21;23(13):5780. doi: 10.3390/s23135780.

Abstract

Gas sensors are currently employed in various applications in fields such as medicine, ecology, and food processing, and serve as monitoring tools for the protection of human health, safety, and quality of life. Herein, we discuss a promising direction in the research and development of gas sensors based on peptides-biomolecules with high selectivity and sensitivity to various gases. Thanks to the technique developed in this work, which uses a framework based on the density-functional tight-binding theory (DFTB), the most probable adsorption centers were identified and used to describe the interaction of some analyte molecules with peptides. The DFTB method revealed that the physical adsorption of acetone, ammonium, benzene, ethanol, hexane, methanol, toluene, and trinitrotoluene had a binding energy in the range from -0.28 eV to -1.46 eV. It was found that peptides may adapt to the approaching analyte by changing their volume up to a maximum value of approx. 13%, in order to confine electron clouds around the adsorbed molecule. Based on the results obtained, the prospects for using the proposed peptide configurations in gas sensor devices are good.

摘要

气体传感器目前应用于医学、生态学和食品加工等领域的各种应用中,并作为保护人类健康、安全和生活质量的监测工具。在此,我们讨论基于对各种气体具有高选择性和灵敏度的肽 - 生物分子的气体传感器研发的一个有前景的方向。由于这项工作中开发的技术,该技术使用基于密度泛函紧束缚理论(DFTB)的框架,确定了最可能的吸附中心,并用于描述一些分析物分子与肽的相互作用。DFTB方法表明,丙酮、铵、苯、乙醇、己烷、甲醇、甲苯和三硝基甲苯的物理吸附具有 -0.28 eV至 -1.46 eV范围内的结合能。研究发现,肽可以通过将其体积改变至最大值约13%来适应接近的分析物,以便在吸附分子周围限制电子云。基于所获得的结果,在所提出的肽配置用于气体传感器装置方面具有良好的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ed9/10346805/62fae64aa5d2/sensors-23-05780-g001.jpg

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