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J A Larsson

Publications and source records attributed to J A Larsson.

5 recordsLinked to original sources
PubMed2006

Modeling competitive guest binding to beta-cyclodextrin molecular printboards.

Anchoring of functionalized guest molecules to self-assembled monolayers (SAMs) is key to the development of molecular printboards for nanopatterning. One very promising system involves guest binding to immobilized beta-cyclodextrin (beta-CD) hosts, with guest:host recognition facilitated by a hydrophobic interaction between uncharged anchor groups on the guest molecule and beta-CD hosts self-assembled at gold surfaces. We use molecular dynamics free energy (MDFE) simulations to describe the specificity of guest:beta-CD association. We find good agreement with experimental thermodynamic measurements for binding enthalpy differences between three commonly used phenyl guests: benzene, toluene, and t-butylbenzene. van der Waals interaction with the inside of the host cavity accounts for almost all of the net stabilization of the larger phenyl guests in beta-CD. Partial and full methylation of the secondary rim of beta-CD decreases host rigidity and significantly impairs binding of both phenyl and larger adamantane guest molecules. The beta-CD cavity is also very intolerant of guest charging, penalizing the oxidized state of ferrocene by at least 7 kcal/mol. beta-CD hence expresses moderate specificity toward uncharged organic guest molecules by van der Waals recognition, with a much higher specificity calculated for electrostatic recognition of organometallic guests.

Journal Article↗
PubMed1989

Linearized model for error-compensated kinetic determinations without prior knowledge of reaction order or rate constant.

This paper describes a new algorithm for calculation of reaction orders, rate constants, and initial and final values of detector signal from several signal vs time data points. The algorithm utilizes a linearized version of the rate equation and is intended primarily to provide initial estimates of these kinetic parameters for other curve-fitting methods. However, under some circumstances, the linearized model can provide sufficiently reliable results that subsequent processing by other methods is not needed. Simulated data with different levels of superimposed noise, data densities, reaction orders, rate constants, and signal change are used to evaluate the algorithm both for its primary purpose of providing initial estimates for other curve-fitting methods and as an independent method. Results are compared with those obtained with a nonlinear least-squares method and two initial-rate methods. The new algorithm provides less reliable results than those obtained by the nonlinear curve-fitting method for some situations (e.g. reaction orders greater than two, low data densities) but has the advantage that it is applicable to reaction orders at and near unity where the nonlinear method to which it is compared fails.

Kinetics↗