Scholarlink

<p>In our laboratory a specially designed Bridgman technique was utilized to pre- pare single crystals of TlInTe2. The structure of TlInTe2 in powder form was examined by X-ray diffraction, revealing the lattice parameters of a = 8.494 Å and c = 7.181 Å. The optical properties of single-layered crystals of TlInTe2 were studied using spectrophotometric transmittance and reflectance measure- ments in the wavelength range of 200–2500&#xa0;nm. The dispersion curve of the refractive index showed anomalous dispersion in the absorption region and nor- mal dispersion in the transmitted region. The direct band gap was determined to be 2.08&#xa0;eV. Micro-Raman measurements were carried out at room temperature using a Dilor XY800 triple monochromator with a CCD camera and a Kr+ laser (λexc = 647.1 nm). The laser power was reduced to 1–8 mW to prevent rapid changes in the Raman spectrum, allowing observation of photoinduced spec- tral dynamics. Micro-Raman spectroscopy provides a powerful non-destructive method to investigate the vibrational properties of TlInTe2, offering valuable insights into its structural properties for specific applications in photonics and optoelectronic devices. By careful analysis and comparison with theoretical mod- els and other materials in the same class, we gain a deep understanding of TlInTe2’s potential and limitations. Furthermore, this study integrates machine learning (ML) techniques to improve understanding and prediction of TlInTe2’s. Optical properties. Using synthetic data, Random Forest models were trained to predict optical parameters such as refractive index and absorption coeffi- cient with near-perfect accuracy. These ML models significantly reduce the need for extensive experimental measurements and offer a powerful tool for optimiz- ing the performance of the material in optoelectronic applications. The findings underscore the growing importance of combining ML with materials science to accelerate discoveries and advances in semiconductors such as TlInTe2.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bridging laboratory findings and artificial intelligence for the design of TlInTe2 crystals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月21日

查看全文
  • Muhammad A. O. Ahmed,
  • Hessah Alotaibi,
  • F. Gami,
  • Mohamed M. Fangary

摘要

In our laboratory a specially designed Bridgman technique was utilized to pre- pare single crystals of TlInTe2. The structure of TlInTe2 in powder form was examined by X-ray diffraction, revealing the lattice parameters of a = 8.494 Å and c = 7.181 Å. The optical properties of single-layered crystals of TlInTe2 were studied using spectrophotometric transmittance and reflectance measure- ments in the wavelength range of 200–2500 nm. The dispersion curve of the refractive index showed anomalous dispersion in the absorption region and nor- mal dispersion in the transmitted region. The direct band gap was determined to be 2.08 eV. Micro-Raman measurements were carried out at room temperature using a Dilor XY800 triple monochromator with a CCD camera and a Kr+ laser (λexc = 647.1 nm). The laser power was reduced to 1–8 mW to prevent rapid changes in the Raman spectrum, allowing observation of photoinduced spec- tral dynamics. Micro-Raman spectroscopy provides a powerful non-destructive method to investigate the vibrational properties of TlInTe2, offering valuable insights into its structural properties for specific applications in photonics and optoelectronic devices. By careful analysis and comparison with theoretical mod- els and other materials in the same class, we gain a deep understanding of TlInTe2’s potential and limitations. Furthermore, this study integrates machine learning (ML) techniques to improve understanding and prediction of TlInTe2’s. Optical properties. Using synthetic data, Random Forest models were trained to predict optical parameters such as refractive index and absorption coeffi- cient with near-perfect accuracy. These ML models significantly reduce the need for extensive experimental measurements and offer a powerful tool for optimiz- ing the performance of the material in optoelectronic applications. The findings underscore the growing importance of combining ML with materials science to accelerate discoveries and advances in semiconductors such as TlInTe2.