WP1 Kena Theory
1.1. A rigorous general construction for KENA theory was realized using (Taylor) series expansion arguments for dynamical systems on a hypergraph structure,
1.2 This enabled:
- exact computation of coefficients in the KENA series
- the study of the role of experimental resolution, resolvability of species, data dimension, hidden species
- a universal mechanism of low-dimensional response for arbitrarily large networks
1.3 The exponential filtering power of indices and role of the number of experiments was demonstrated through case studies.
1.4 A methodology to use KENA for data with sudden discontinuities (e.g. phase transitions) was formulated
1.5 KENA Theory was validated on experimental data from the literature. A KENA-derived data dimension analogue was in turn used to obtain similar results, and published as a tutorial with case studies.
A paper on the full KENA theory was drafted.
WP2 KENA Spectral decompositions
2.1 Natural spectral decompositions were directly derived using results from 1.1.
2.2 Numerical schemes were implemented for decompositions.
2.3 And applied to real and synthetic UV-VIS data
2.2-2.3 revealed that agnostic extraction of a decomposition from data is nontrivial: different power series can fit the same experimental data remarkably well, especially with added noise.
This limits the interpretative power of the decomposition, and additional constraints need to be derived to enhance this procedure, which will be pursued in followup work.
WP3 Flow reactor implementations
3.1-3.2 4 syringe pumps were built and a variety of reactors were designed, 3d printed and optimized. A simple portable setup was realized as well as a glass chip UV-VIS setup.
It was concluded that more broadly applicable - but more expensive - techniques (IR, RAMAN, MS) need to be pursued to get the most out of KENA. (followup)
3.3 An ambitious ongoing collaboration to study the Formose reaction using new data analysis tools was started, which has confirmed our new hypotheses on the nature of this reaction.
New data analysis methodologies were applied to systems in the host lab, including two papers (in review) on supramolecular assembly and a chemical oscillator (in preparation).
WP4 Expanding KENA
WP4 involved characterizing how notions of dimension related to kinetic exponents, how these relate to CRN structure, and to experimental data, and was assessed in four instances.
4.a An approach for linear networks was developed that retains and eliminates candidate reactions through successive datasets (experiments),
4.b a mysterious oscillator in the host group with unidentified network was studied. A theory relaing oscillatory networks to their structure was developed and published.
This theory enabled the identification and assesment of key candidate hypotheses of the experiments (in preparation).
4.c discoveries made in 3.3 on supramolecular chemistry could be combined with mathematical insights from the structural theory in 4.b which led to the formulation of a
a theory of copolymerization and laws relating polymer sequences to reaction mechanisms.
4.d Extensions of the KENA concept were formulated that employ on-device protocols to produce higher-dimensional data and validated against synthetic data.
Funding was secured to pursue this new KENA concept.
Main Achievements:
10 articles prepared, among which 2 accepted, 4 preprinted, 3 in review.
KENA delivered several mature new theories, which each have been directly applied to experimental data, solved open questions and helped identify new questions
- Data Dimension theory
- Kinetic Exponent Theory
- Dimensional Theory of Phases and Psuedophases
- Theory of Oscillating Reactions in Reaction Networks via Parameter-rich Kinetics
