Determining Hildebrand Solubility Parameter by Ultraviolet Spectroscopy and Microcalorimetry

Carvalho, Suzanny Paiva; Lucas, Elizabete F.; González, Gaspar; Spinelli, Luciana S.

doi:10.5935/0103-5053.20130250

Cited by 19 publications

(17 citation statements)

References 7 publications

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“…The Hildebrand solubility parameters were calculated for DMF, ethanol and water are 12.2, 12.9 and 23. . The Hildebrand solubility parameter of a solvent mixture was calculated by a volume adjusted average 42 . The values of the synthesis solutions F1-4 are shown in Table 4, alongside the calculated Hildebrand solubility parameter for H4DHTP.…”

Section: Controlling Mg-mof-74 Particle Sizementioning

confidence: 99%

Controlling the size and shape of Mg-MOF-74 crystals to optimise film synthesis on alumina substrates

Campbell

Tokay

2017

Microporous and Mesoporous Materials

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Section: Controlling Mg-mof-74 Particle Sizementioning

confidence: 99%

Controlling the size and shape of Mg-MOF-74 crystals to optimise film synthesis on alumina substrates

Campbell

Tokay

2017

Microporous and Mesoporous Materials

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“…Besides the typical methods described in the section above, a number of other analytical techniques used to determine the solubility parameter have been reported. These include swelling, turbidity, ultraviolet spectroscopy, and differential scanning calorimetry (DSC) as well as differential scanning microcalorimetry (μDSC) …”

Section: Theory and Experimental Aspects Of The Solubility Parameter mentioning

confidence: 99%

“…Carvalho et al . used several methods to determine the solubility parameter for naphthalene, phenanthrene and pyrene and reported UV‐vis as the most suited method for a wider molar mass range.…”

Section: Theory and Experimental Aspects Of The Solubility Parameter mentioning

confidence: 99%

Application of the solubility parameter concept to assist with oral delivery of poorly water-soluble drugs – a PEARRL review

Jankovic

Tsakiridou

Ditzinger

et al. 2018

Journal of Pharmacy and Pharmacology

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In particular, partial solubility parameters hold great promise for aiding the development of poorly soluble drug delivery systems. This is particularly true in early-stage development, where compound availability and resources are limited. The experimental determination of solubility parameters has its merits despite being rather labour-intensive because further data can be used to continuously improve in silico predictions. Such improvements will ensure that solubility parameters will also in future guide scientists in finding suitable drug formulations.

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“…Results are significantly dependent on solvent selection, total number of solvents and HSP-accuracy of solvents. Additionally the choice of experimental method and data evaluation can influence the outcome of the experiment [9,15,14,54,27]. For example, for a single solute, the semiconducting polymer P3HT, several HSP can be found in literature as illustrated in Table S1.…”

Section: What Is An Acceptable Error?mentioning

confidence: 99%

A Bayesian Approach to Predict Solubility Parameters

Sánchez-Lengeling¹,

Roch²,

Perea³

et al. 2018

Preprint

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Solubility is a ubiquitous phenomenon in many aspects of science. While solubility can be determined by considering the cohesive forces in a liquid via Hansen solubility parameters (HSP), the prediction is often done using Quantitative structure-property relationship models due to its low computational cost. Herein, we report an interpretable and versatile probabilistic approach (gpHSP) to determining HSP. Our model is based on Gaussian processes (GP), a Bayesian machine learning approach, which also provides uncertainty bounds to prediction. gpHSP achieves its flexibility by leveraging a variety of input data, such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculations. gpHSP is built on experimentally determined HSP: a general solvents set aggregated from literature, and a polymer set, in-house characterized. In both sets, we obtained a high degree of agreement, surpassing well-established machine learning methods. We demonstrate the general applicability of gpHSP to miscibility of organic semiconductors, drug compounds and in general solvents, which can be further extended to other domains. gpHSP is a fast and accurate toolbox, which could be applied to molecular design for solution processing technologies.<br>

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Determining Hildebrand Solubility Parameter by Ultraviolet Spectroscopy and Microcalorimetry

Cited by 19 publications

References 7 publications

Controlling the size and shape of Mg-MOF-74 crystals to optimise film synthesis on alumina substrates

Controlling the size and shape of Mg-MOF-74 crystals to optimise film synthesis on alumina substrates

Application of the solubility parameter concept to assist with oral delivery of poorly water-soluble drugs – a PEARRL review

A Bayesian Approach to Predict Solubility Parameters

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