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@@ -646,6 +646,34 @@
 	keywords = {Chemical reaction network, Structural optimization, Kinetic fitting, Mixed-integer-linear programming},
 	abstract = {The manual determination of chemical reaction networks (CRN) and reaction rate equations is cumbersome and becomes workload prohibitive for large systems. In this paper, a framework is developed that allows an almost entirely automated recovery of sets of reactions comprising a CRN using experimental data. A global CRN structure is used describing all feasible chemical reactions between chemical species, i.e. a superstructure. Network search within this superstructure using mixed integer linear programming (MILP) is designed to promote sparse connectivity and can integrate known structural properties using linear constraints. The identification procedure is successfully demonstrated using simulated noisy data for linear CRNs comprising two to seven species (modelling networks that can comprise up to forty two reactions) and for batch operation of the nonlinear Van de Vusse reaction. A further case study using real experimental data from a biodiesel reaction is also provided.}
 }
+@article{nmrsimml,
+	author = {Rowlands, J. Benji and Jonsson, Lina and Goodman, Jonathan M. and Howe, Peter W. A. and Czechtizky, Werngard and Leek, Tomas and Lewis, Richard J.},
+	title = {Towards automatically verifying chemical structures: the powerful combination of 1H NMR and IR spectroscopy},
+	journal = {Chemical Science},
+	volume = {16},
+	number = {45},
+	pages = {21590-21599},
+	year = {2025},
+	month = {12},
+	abstract = { Human interpretation of spectroscopic data remains key to confirming newly synthesised chemical structures. Whilst there have been advances in automated spectral interpretation, the false positive and false negative rates remain too high to replace human interpretation. One approach, Automated Structure Verification (ASV), scores observed nuclear magnetic resonance (NMR) spectra against predicted NMR spectra. We describe a method to extend this approach to infrared (IR) spectra and apply it alongside proton NMR spectra to distinguish between a challenging set of 99 similar isomer pairs. Based on relative scores, we classify each as correct, incorrect or unsolved. Our results show that IR can be used as an efficient automated method to distinguish similar isomers with an accuracy close to that of proton NMR. We further introduce a method to combine NMR and IR results and show that the combination significantly outperforms either technique alone. At a true positive rate of 90\%, unsolved pairs are reduced to 0–15\% using NMR and IR together compared to 27–49\% using individual techniques alone. At a true positive rate of 95\%, they are reduced to 15–30\% from 39–70\%. These results are a significant step towards efficient automated structure verification based on easily measured spectroscopy data. },
+	issn = {2041-6520},
+	doi = {10.1039/d5sc06866e},
+	url = {https://doi.org/10.1039/d5sc06866e},
+	eprint = {https://pubs.rsc.org/sc/article-pdf/16/45/21590/1904936/d5sc06866e.pdf},
+}
+@article{nmrquant,
+	title = {Quantitative 1H NMR spectroscopy},
+	journal = {TrAC Trends in Analytical Chemistry},
+	volume = {35},
+	pages = {5-26},
+	year = {2012},
+	issn = {0165-9936},
+	doi = {https://doi.org/10.1016/j.trac.2012.02.007},
+	url = {https://www.sciencedirect.com/science/article/pii/S0165993612000702},
+	author = {Santosh Kumar Bharti and Raja Roy},
+	keywords = {Calibration-curve method, Electronic referencing, Metabolomics, NMR spectroscopy, Nuclear magnetic resonance (NMR), Pharmaceutical analysis, Quantitative NMR (qNMR), Referencing technique, Standard-addition method, Validation},
+	abstract = {This review illustrates the need to use nuclear magnetic resonance (NMR) spectroscopy for the quantitative analysis of small molecules in their crude forms and in mixtures. We provide the basic concepts of quantitative NMR (qNMR), a brief description of important acquisition and processing parameters responsible for obtaining high-quality, reproducible NMR spectra in order to maximize accuracy, and the latest referencing techniques used for quantitative analysis. We also describe methods that are used for quantitative analysis including calibration-curve and standard-addition methods. Further, we briefly address validation of qNMR spectroscopy and its major applications in various scientific disciplines.}
+}