Defect Engineering in Ti‐Doped Ta$_3$N$_5$ Thin Films for Enhanced Photoelectrochemical Water Splitting: Electronic Structure Modulation and Charge Carrier Dynamics
Tantalum nitride (Ta$_3$N$_5$) is a promising semiconductor for solar-driven photoelectrochemical (PEC) water splitting, but its performance is limited by intrinsic defects. Here, we investigate the effect of titanium (Ti) doping (0–10 at%) on the structural, compositional, and optoelectronic properties of Ta$_3$N$_5$ thin films. At low concentrations (<2 at%), Ti$^{4+}$ preferentially substitutes Ta at four-coordinated sites, enhancing nitrogen incorporation and suppressing defect states associated with under-coordinated Ta. This leads to improved carrier dynamics and prolonged electron–hole lifetimes. Higher doping levels (≥3.5 at%) result in occupation of three-coordinated sites, inducing increase in the oxygen content, lattice distortion, and defect formation that deteriorate carrier lifetimes. PEC measurements reveal that optimized Ti doping significantly reduces charge transfer resistance and nearly seven-fold increase in the photocurrent. These findings underscore the importance of controlled Ti doping for defect engineering and band structure tuning to boost the PEC performance of Ta$_3$N$_5$ thin films.
Authors
- Shailesh Kalal (ORCID: https://orcid.org/0000-0003-4864-596X)
- Michele Orlandi (ORCID: https://orcid.org/0000-0001-5738-3231)
- A. Gloskovskii
- Per Eklund (ORCID: https://orcid.org/0000-0003-1785-0864)
- Kenneth Järrendahl (ORCID: https://orcid.org/0000-0003-2749-8008)
- Martin Magnuson (ORCID: https://orcid.org/0000-0002-0317-0190)
- Ching‐Lien Hsiao (ORCID: https://orcid.org/0000-0001-7192-0670)
- Sophia Sahoo (ORCID: https://orcid.org/0000-0003-2027-0254)
- Mukul Kumar Gupta
- Feng Wang
- Akshaya A
- Alessandro Chesini
- Dibyendu Bhattacharyya
- Sanath Kumar Honnali
- Jens Birch
- Nakul Jain
Institutions
- Deutsches Elektronen-Synchrotron DESY (DE)
Publication Details
- Journal
- DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron)
- Published
- 2026-01-01
- DOI
- https://doi.org/10.3204/pubdb-2026-00040
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00