Rational Design of Cyclopentadiene-Based Super- and Hyperacids Based on Aromaticity

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Acid-Base Chemistry

Background:

• Neutral organic superacids are crucial for various chemical applications, including polymerization and cation isolation.
• Cyclopentadiene is a mild organic acid; stabilizing its conjugate base can enhance acidity.
• Aromatic substituents on cyclopentadiene were explored to achieve superacidic and hyperacidic properties.

Purpose of the Study:

• To design and synthesize novel neutral organic superacids based on substituted cyclopentadiene.
• To investigate the relationship between structural modifications (phenyl and cyano substituents) and acidity.
• To evaluate the potential of these compounds to exhibit superacidic and hyperacidic behavior.

Main Methods:

• Employed computational methods, including MP2, DFT (B3LYP, M06-2X), and CBS-QB3, to calculate gas-phase proton affinities.
• Utilized Density Functional Theory (DFT) methods, validated against experimental data for cyclopentadiene.
• Assessed aromaticity and conjugation of cyclopentadienyl anions using nucleus-independent chemical shift (NICS) and harmonic oscillator model of aromaticity (HOMA).

Main Results:

• Trisubstituted cyclopentadiene derivatives demonstrated hyperacidity, with gas-phase enthalpies of deprotonation (ΔHacid) of 245 and 239 kcal/mol (DFT B3LYP and M06-2X).
• Some tautomers exhibited hyperacidity with proton affinity values ranging from 205-240 kcal/mol.
• Triphenyl-substituted cyclopentadiene transformed into a superacid (ΔHacid = 298 kcal/mol) upon replacement of phenyl groups with nitrobenzene, exceeding sulfuric acid's acidity.

Conclusions:

• Increased aromaticity and conjugation in cyclopentadiene derivatives after deprotonation significantly enhance the stability of conjugate bases and their acidity.
• The designed substituted cyclopentadienes show potential as potent superacids and hyperacids for advanced chemical applications.
• Computational modeling provides a reliable approach for predicting and designing organic superacids with tailored properties.