硅烯纳米片感知食品质量：密度泛函理论研究
Sensing food quality by silicene nanosheets : a Density Functional Theory study
AI总结：
本文利用DFT和NEGF方法建模硅烯纳米片气体传感器，发现氟钝化硅烯对六种食品VOCs的灵敏度显著优于未钝化硅烯和r-GO，并可分别识别四种VOCs，为二维纳米传感器检测食品质量提供了理论依据。
摘要：
食品释放的挥发性有机化合物（VOCs）被认为是评估食品质量的标志物。本工作采用第一性原理密度泛函理论（DFT）和非平衡格林函数（NEGF）方法，对基于二维硅烯纳米片的化学电阻式气体传感器进行建模，该传感器能够感知标准食品释放的六种不同VOCs。我们以未钝化和氟钝化硅烯（F-silicene）片作为传感器材料的计算表明，氟钝化硅烯对全部六种VOC分子（丙酮、二甲基硫醚、乙醇、甲醇、乙酸甲酯和甲苯）具有显著更好的灵敏度。此外，发现氟化硅烯传感器能够分别识别四种VOCs，性能远优于近期实验中使用的还原氧化石墨烯（r-GO）。我们从吸附能、电荷转移和电子结构变化的角度分析了影响未钝化和氟化硅烯传感能力的微观图像。我们发现氟化硅烯纳米片更好的传感能力可归因于不同VOCs吸附后费米能级附近电子结构的变化。结果表明，对于一般食品VOCs，钝化硅烯作为传感器的性能优于r-GO。这些结果具有重要意义，因为可以以类似方式对各种二维纳米传感器进行建模，以检测特定食品释放的更复杂VOCs。
英文摘要：
Volatile organic compounds (VOCs) emitted by food products are considered markers for assessing quality of food. In this work, first-principles Density Functional Theory (DFT) and Non-equilibrium Green's function (NEGF) methods have been employed to model chemo-resistive gas sensor based on two-dimensional silicene based nanosheets that can sense the six different VOCs emitted by standard food products. Our calculations with unpassivated and flourine passivated silicene(F-silicene) sheets as sensor materials show that flourine passivated silicene has significantly better sensitivity towards all six VOC molecules (Acetone, Dimethylsulfide, Ethanol, Methanol, Methylacetate and Toluene). Moreover, flourinated silicene sensor is found to be capable of separately recognising four VOCs, a much better performance than r-GO used in a recent experiment. We analyse the microscopic picture influencing sensing capabilities of un-passivated and fluorinated silicene from the perspectives of adsorption energy, charge transfer and changes in the electronic structure. We find that better sensing ability of fluorinated silicene nanosheet can be correlated with the changes in the electronic structures near the Fermi level upon adsorption of different VOCs. The results imply that passivated silicene can work better as a sensor than r-GO in case of generic food VOCs. The results are important since modelling of various two-dimensional nano-sensors can be done in the similar way for detection of more complex VOCs emitted by specific food products.
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