TY - CHAP
T1 - Applications of Predictive Modeling for Dye-Sensitized Solar Cells (DSSCs)
AU - Kar, Supratik
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Dye-sensitized solar Cells (DSSCsDye-sensitized solar cells (DSSCs)) present an innovative alternative to conventional photovoltaic technologies, boasting lower production costs and superior performance in low-light conditions. This chapter investigates the synergistic integration of predictive modelingPredictive modeling techniques with experimental methodologies to optimize the DSSCDye-sensitized solar cells (DSSCs) design and efficiency. The fundamental components of DSSCsDye-sensitized solar cells (DSSCs)—photoanode, dye sensitizerDye sensitizers, electrolyteElectrolytes, and counter electrodeCounter electrodes—are initially examined, along with the associated efficiency and long-term stability challenges. The comprehensive review follows, detailing predictive modelingPredictive modeling techniques, including quantum mechanical modelsQuantum mechanical models, quantitative structure–property relationshipQuantitative structure-activity/property relationship(QSPR)Quantitative Structure Property Relationship (QSPR) and quantitative read-across structure–property relationshipQuantitative structure-activity/property relationship(q-RASPR) Radial symmetry models (q-RASPR)models, molecular dynamicsMolecular Dynamics (MD) simulations simulations, and machine learningMachine learning (ML) approaches. These computational methods are crucial in simulating complex DSSCDye-sensitized solar cells (DSSCs) processes, guiding material selection, and predicting cell behavior under diverse environmental conditions. The chapter emphasizes the advantages of hybrid modelingHybrid modeling approaches, which combine multiple computational methods to enhance prediction accuracy and reduce experimental costs. Case studies illustrate the successful application of these techniques in practical DSSCDye-sensitized solar cells (DSSCs) development. By bridging theoretical predictions with experimental validation, this chapter provides valuable insights into the design of more efficient, stable, and commercially viable DSSCsDye-sensitized solar cells (DSSCs). This integrated approach sets the stage for future advancements in solar energy technologies, potentially accelerating the widespread adoption of DSSCsDye-sensitized solar cells (DSSCs) as a sustainable energy solution.
AB - Dye-sensitized solar Cells (DSSCsDye-sensitized solar cells (DSSCs)) present an innovative alternative to conventional photovoltaic technologies, boasting lower production costs and superior performance in low-light conditions. This chapter investigates the synergistic integration of predictive modelingPredictive modeling techniques with experimental methodologies to optimize the DSSCDye-sensitized solar cells (DSSCs) design and efficiency. The fundamental components of DSSCsDye-sensitized solar cells (DSSCs)—photoanode, dye sensitizerDye sensitizers, electrolyteElectrolytes, and counter electrodeCounter electrodes—are initially examined, along with the associated efficiency and long-term stability challenges. The comprehensive review follows, detailing predictive modelingPredictive modeling techniques, including quantum mechanical modelsQuantum mechanical models, quantitative structure–property relationshipQuantitative structure-activity/property relationship(QSPR)Quantitative Structure Property Relationship (QSPR) and quantitative read-across structure–property relationshipQuantitative structure-activity/property relationship(q-RASPR) Radial symmetry models (q-RASPR)models, molecular dynamicsMolecular Dynamics (MD) simulations simulations, and machine learningMachine learning (ML) approaches. These computational methods are crucial in simulating complex DSSCDye-sensitized solar cells (DSSCs) processes, guiding material selection, and predicting cell behavior under diverse environmental conditions. The chapter emphasizes the advantages of hybrid modelingHybrid modeling approaches, which combine multiple computational methods to enhance prediction accuracy and reduce experimental costs. Case studies illustrate the successful application of these techniques in practical DSSCDye-sensitized solar cells (DSSCs) development. By bridging theoretical predictions with experimental validation, this chapter provides valuable insights into the design of more efficient, stable, and commercially viable DSSCsDye-sensitized solar cells (DSSCs). This integrated approach sets the stage for future advancements in solar energy technologies, potentially accelerating the widespread adoption of DSSCsDye-sensitized solar cells (DSSCs) as a sustainable energy solution.
KW - DSSCs
KW - Dyes
KW - In silico
KW - ML
KW - PCE
KW - QSPR
KW - Solar cells
UR - https://www.scopus.com/pages/publications/105001043695
U2 - 10.1007/978-3-031-78728-7_8
DO - 10.1007/978-3-031-78728-7_8
M3 - Chapter
AN - SCOPUS:105001043695
T3 - Challenges and Advances in Computational Chemistry and Physics
SP - 167
EP - 198
BT - Challenges and Advances in Computational Chemistry and Physics
PB - Springer Science and Business Media B.V.
ER -