Dataset Information

Strain-Enhanced Large-Area Monolayer MoS₂ Photodetectors.

ABSTRACT: In this study, we show a direct correlation between the applied mechanical strain and an increase in monolayer MoS₂ photoresponsivity. This shows that tensile strain can improve the efficiency of monolayer MoS₂ photodetectors. The observed high photocurrent and extended response time in our devices are indicative that devices are predominantly governed by photogating mechanisms, which become more prominent with applied tensile strain. Furthermore, we have demonstrated that a nonencapsulated MoS₂ monolayer can be used in strain-based devices for many cycles and extensive periods of time, showing endurance under ambient conditions without loss of functionality. Such robustness emphasizes the potential of MoS₂ for further functionalization and utilization of different flexible sensors.

SUBMITTER: Radatovic B

PROVIDER: S-EPMC10982932 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Strain-Enhanced Large-Area Monolayer MoS2 Photodetectors.

Radatović Borna B Çakıroğlu Onur O Jadriško Valentino V Frisenda Riccardo R Senkić Ana A Vujičić Nataša N Kralj Marko M Petrović Marin M Castellanos-Gomez Andres A

ACS applied materials & interfaces 20240318 12

In this study, we show a direct correlation between the applied mechanical strain and an increase in monolayer MoS2 photoresponsivity. This shows that tensile strain can improve the efficiency of monolayer MoS2 photodetectors. The observed high photocurrent and extended response time in our devices are indicative that devices are predominantly governed by photogating mechanisms, which become more prominent with applied tensile strain. Furthermore, we have demonstrated that ...[more]

PMID: 38500411

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Project description:Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS2 with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS2 obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS2 material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS2 materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS2 materials with a reduction in the time and cost of trial and error.

Dataset Information

Strain-Enhanced Large-Area Monolayer MoS₂ Photodetectors.

Publications

Strain-Enhanced Large-Area Monolayer MoS<sub>2</sub> Photodetectors.

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