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Record W3024313225 · doi:10.1149/ma2020-0110867mtgabs

Tuning the Conductivity of Molybdenum Disulfide (MoS<sub>2</sub>) Thin Films through Defect Engineering

2020· article· en· W3024313225 on OpenAlexaff
Dipankar Saha, Ravi Selvaganapathy, Peter Kruse

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMolybdenum disulfideMaterials scienceNanotechnologyPhosphoreneOptoelectronicsSemiconductorTransistorGrapheneElectrical conductorConductivityBand gapField-effect transistorEngineering physicsVoltageElectrical engineeringChemistryComposite material

Abstract

fetched live from OpenAlex

Two-dimensional (2D) materials have attracted much attention over the last decade due to their high performance in nanoelectronic devices. The discovery of graphene opened up many opportunities to investigate and explore other 2D materials. There has been a drive to expand the toolbox of 2D materials to also include insulators and semiconductors with a variety of bandgaps. As a result, a wide range of materials have been discovered or predicted, 1 with molybdenum disulfide (MoS 2 ) being particularly popular. The semiconducting phase of MoS 2 (2H-MoS 2 ) is one of the most commonly studied among the transition metal dichalcogenides. 2 It has a thickness dependent band gap which has drawn attention for field-effect transistors (FET) where a high on/off current ratio is desired. 3-5 However, for applications in batteries, 6 supercapacitors, 7 and solar cells, 8 a substantially increased conductivity is required in order to achieve reasonable currents. Using 2H-MoS 2 requires a relatively high voltage to get sufficient conductivity due to the presence of a band gap. The most common source of conductive MoS 2 is metallic MoS 2 (1T-MoS 2 ) that has been prepared via the lithium intercalation process, which requires inert atmosphere processing and safety procedures. 9 Hence, there is a desire to develop a safer and more efficient process to yield conductive MoS 2. Defects plays a very important role in modulating the electrical properties of MoS 2 . Sonication of MoS 2 in an appropriate solvent results in many disordered structural defects. The most common defects on MoS 2 are sulfur defects. 10 These defects increase the energy level of the gap state and eventually deteriorate the device performance. Thiol based molecules are commonly used to reduce the number of sulfur defects on MoS 2 . 11 Other molecules such as oxygen or organic super acids like bis(trifluoromethane) sulfonamide (TFSI) have also been reported to passivate the surface defect. 12,13 Past research has mainly focused on the theoretical study of defective MoS 2 and how to utilize those defects for improving photoluminescent efficiency. However, those defects can also be utilized to improve the conductivity of MoS 2 as a safer alternative for applications in batteries, supercapacitors, solar cells and sensors. In this work, we show a simple and effective way to prepare few layer conductive MoS 2 under ambient conditions. We have demonstrated that the sheet resistance of the conductive MoS 2 that we prepared is up to five orders of magnitude higher than that of the semiconducting phase of MoS 2 , depending on the dopant concentration. The samples were also characterized with Hall measurements, X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. An important goal of our work is to control the conductivity of the MoS 2 thin films in safe and facile ways that enable their application in low-cost chemiresistive sensors in liquid environments. We fabricated chemiresistive pH sensors with centimeter channel lengths while maintaining low measurement voltages. Our study furthers the understanding of conductive forms of MoS 2 , and also opens a new pathway for next generation electronic devices. References: 1. M. D. Segall et al. , J. Phys.: Condens. Matter , 14 , 2717–2744 (2002). 2. H. Wan et al. , RSC Adv., 5 , 7944 (2015). 3. D. Kiriya et al. , J. Am. Chem. Soc. , 136 , 7853−7856 (2014). 4. H. Fang et al. , Nano Lett. , 13 , 1991−1995 (2013). 5. M. Choi et al. , ACS Nano , 8 , 9332-9340 (2014). 6. T. Stephenson et al. , Energy Environ. Sci. , 7 , 209-231 (2014). 7. L. Cao et al. , Small , 9 , 2905–2910 (2013). 8. M.-L.Tsai et al. , ACS Nano , 8 , 8317-8322 (2014). 9. G. Eda et al. , Nano Lett. , 11 , 5111–5116 (2011). 10. A. Dabral et al. , Phys. Chem. Chem. Phys. , 21 , 1089-1099 (2019). 11. D. M. Sim et al. , ACS Nano , 9 , 12115-12123 (2015). 12. K. C. Santosh et al. , J. Appl. Phys. , 117 , 135301 (2015). 13. H. Lu et al. , APL Mater. , 6 , 066104 (2018). Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.215
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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