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<p>Extensive research is going on alternative methods that may be helpful in replacing the conventional methods used for detection of Alzheimer’s Disease. To cope up with dangerously increasing cases of dementia and alzheimer’s disease worldwide for past some years, academia is actively involved in designing methods to detect this fatal disease well in advance. A family of III-V compound semiconductors Gallium Arsenide (GaAs) monolayer has a large band gap and honeycomb structure, making it an attractive option for sensing applications. In this work, the adsorption behavior of four molecules- Butylated hydroxytoluene(BHT), Pivalic acid(PVA), Phenylenediamine(PDM) and Styrene on GaAs monolayers, which are known as Volatile Organic Compound (VOC) for detection of Alzheimer’s disease, were examined using density functional theory (DFT). For every GaAs@gas system, the band structure, density of states (DOS), charge transfer ( <i>Q</i><sub><i>T</i></sub>), adsorption energy (<i>E</i><sub><i>ads</i></sub> ), stability analysis (Work function and formation energy), sensitivity and Recovery Time (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\tau \)</EquationSource> </InlineEquation>) were calculated as part of the analysis. The band gap was reduced significantly after adsorption from 1.4&#xa0;eV to 0.94&#xa0;eV in the GaAs monolayer. Interestingly, pivalic acid displayed the highest adsorption energy on the GaAs monolayer out of all the gases examined. And also based on the displayed recovery time of <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(4.75 \times {10^{ - 6}}\)</EquationSource> </InlineEquation>sec, pivalic acid may be suitable as a bio-sensor for Alzheimer’s disease due to its comparatively lower recovery time. Moreover in room temperature of 300&#xa0;K, PVA adsorption yields in peak sensitivity of 2.94 × 10<sup>3</sup>, far exceeding other gases. According to these results, the GaAs monolayer doped with these gas molecules may successfully function as a bio sensor to detect alzheimer’s disease in its early stages.</p>

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Design and comparative study of hybridized gallium arsenide based bio-sensors for Alzheimer’s disease early identification with DFT approach

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Saptamee De,
  • Suman Sarkar,
  • Manash Chanda,
  • Anup Dey,
  • Debashis De,
  • Rajat Shubhra Pal

摘要

Extensive research is going on alternative methods that may be helpful in replacing the conventional methods used for detection of Alzheimer’s Disease. To cope up with dangerously increasing cases of dementia and alzheimer’s disease worldwide for past some years, academia is actively involved in designing methods to detect this fatal disease well in advance. A family of III-V compound semiconductors Gallium Arsenide (GaAs) monolayer has a large band gap and honeycomb structure, making it an attractive option for sensing applications. In this work, the adsorption behavior of four molecules- Butylated hydroxytoluene(BHT), Pivalic acid(PVA), Phenylenediamine(PDM) and Styrene on GaAs monolayers, which are known as Volatile Organic Compound (VOC) for detection of Alzheimer’s disease, were examined using density functional theory (DFT). For every GaAs@gas system, the band structure, density of states (DOS), charge transfer ( QT), adsorption energy (Eads ), stability analysis (Work function and formation energy), sensitivity and Recovery Time ( \(\tau \) ) were calculated as part of the analysis. The band gap was reduced significantly after adsorption from 1.4 eV to 0.94 eV in the GaAs monolayer. Interestingly, pivalic acid displayed the highest adsorption energy on the GaAs monolayer out of all the gases examined. And also based on the displayed recovery time of \(4.75 \times {10^{ - 6}}\) sec, pivalic acid may be suitable as a bio-sensor for Alzheimer’s disease due to its comparatively lower recovery time. Moreover in room temperature of 300 K, PVA adsorption yields in peak sensitivity of 2.94 × 103, far exceeding other gases. According to these results, the GaAs monolayer doped with these gas molecules may successfully function as a bio sensor to detect alzheimer’s disease in its early stages.