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Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
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Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
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EDTA: Auxiliary Complexing Reagents01:26

EDTA: Auxiliary Complexing Reagents

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EDTA titrations are usually carried out in highly basic conditions, where the fully deprotonated form of EDTA, Y4−, actively complexes with the free metal ions in the solution. Several metal ions precipitate as hydrous oxide (hydroxides, oxides, or oxyhydroxides) under these conditions, lowering the concentration of free metal ions in the solution. For this reason, auxiliary complexing agents or ligands such as ammonia, tartrate, citrate, or triethanolamine are used in EDTA titrations to...
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Basicity of Heterocyclic Aromatic Amines01:25

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Heterocyclic amines, where the N atom is a part of an alicyclic system, are similar in basicity to alkylamines. Interestingly, the heterocyclic amine having a nitrogen atom as part of an aromatic ring has much less basicity than its corresponding alicyclic counterpart. For this reason, as presented in Figure 1, piperidine (pKb = 2.8) is significantly more basic than pyridine (pKb = 8.8).
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Related Experiment Video

Updated: Dec 21, 2025

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route
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Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

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How Accurately Do Approximate Density Functionals Predict Trends in Acidic Zeolite Catalysis?

Philipp N Plessow1, Felix Studt1,2

  • 1Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.

The Journal of Physical Chemistry Letters
|May 16, 2020
PubMed
Summary

Density functional theory (DFT) accurately predicts trends in zeolite catalyst performance, despite potential for large errors. This supports DFT

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Area of Science:

  • Computational chemistry
  • Materials science
  • Catalysis

Background:

  • Density functional theory (DFT) is widely used for computational screening of new catalysts.
  • Accurate prediction of relative material differences is crucial for catalyst design.
  • Generalized gradient approximation (GGA) level DFT is a common choice for these calculations.

Purpose of the Study:

  • To evaluate the accuracy of GGA-level DFT for predicting catalytic reaction energies and barriers in acidic zeotypes.
  • To compare DFT performance against highly accurate DLPNO-CCSD(T) calculations.
  • To assess the reliability of DFT for identifying trends in catalyst performance.

Main Methods:

  • Utilized Density Functional Theory (DFT) at the generalized gradient approximation (GGA) level.
  • Employed PBE-D3 and BEEF-vdW functionals for calculations.
  • Used highly accurate DLPNO-CCSD(T) calculations as a reference for comparison.
  • Analyzed 65 reaction energies and 130 reaction barriers in zeolite catalysis.

Main Results:

  • While PBE-D3 and BEEF-vdW functionals can exhibit large errors for absolute values, they predict trends across different catalysts with an accuracy of approximately 5 kJ/mol.
  • DFT calculations show good performance in capturing relative differences crucial for catalyst screening.
  • The study confirms the utility of DFT for comparative analysis of catalytic materials.

Conclusions:

  • DFT calculations, even with GGA functionals, are reliable for predicting trends in catalytic activity for zeotype materials.
  • The accuracy of trend prediction supports the continued use of DFT for computational screening and design of novel catalysts.
  • DFT remains a valuable tool for accelerating the discovery of new catalytic materials.