Deciphering complexity in Pd-catalyzed cross-couplings.

Understanding complex reaction systems is critical in chemistry. While synthetic methods for selective formation of products are sought after, oftentimes it is the full reaction signature, i.e., complete profile of products/side-products, that informs mechanistic rationale and accelerates discovery chemistry. Here, we report a methodology using high-throughput experimentation and multivariate data analysis to examine the full signature of one of the most complicated chemical reactions catalyzed by palladium known in the chemical literature. A model Pd-catalyzed reaction was selected involving functionalization of 2-bromo-N-phenylbenzamide and multiple bond activation pathways. Principal component analysis, correspondence analysis and heatmaps with hierarchical clustering reveal the factors contributing to the variance in product distributions and show associations between solvents and reaction products. Using robust data from experiments performed with eight solvents, for four different reaction times at five different temperatures, we correlate side-products to a major dominant N-phenyl phenanthridinone product, and many other side products.

Amine-modified polyionic liquid supports enhance the efficacy of PdNPs for the catalytic hydrogenation of CO2 to formate.

Palladium nanoparticles stabilised by aniline modified polymer immobilised ionic liquid is a remarkably active catalyst for the hydrogenation of CO2 to formate; the initial TOF of 500 h-1 is markedly higher than either unmodified catalyst or its benzylamine and N,N-dimethylaniline modified counterparts and is among the highest to be reported for a PdNP-based catalyst.

MOF-based heterogeneous catalysis in continuous flow via incorporation onto polymer-based spherical activated carbon supports.

We present an approach to harnessing the tuneable catalytic properties of complex nanomaterials for continuous flow heterogeneous catalysis by combining them with the scalable and industrially implementable properties of carbon pelleted supports. This approach, in turn, will enable these catalytic materials, which largely currently exist in forms unsuitable for this application (e.g. powders), to be fully integrated into large scale, chemical processes. A composite heterogeneous catalyst consisting of a metal-organic framework-based Lewis acid, MIL-100(Sc), immobilised onto polymer-based spherical activated carbon (PBSAC) support has been developed. The material was characterised by focused ion beam-scanning electron microscopy-energy dispersive X-ray analysis, powder X-ray diffraction, N2 adsorption, thermogravimetric analysis, atomic absorption spectroscopy, light scattering and crush testing with the catalytic activity studied in continuous flow. The mechanically robust spherical geometry makes the composite material ideal for application in packed-bed reactors. The catalyst was observed to operate without any loss in activity at steady state for 9 hours when utilised as a Lewis acid catalyst for the intramolecular cyclisation of (±)-citronellal as a model reaction. This work paves the way for further development into the exploitation of MOF-based continuous flow heterogeneous catalysis.

Analysis of Adsorbed Polyphosphate Changes on Milled Titanium Dioxide, Using Low-Field Relaxation NMR and Photoelectron Spectroscopy.

In this study, changes in the adsorbed amount and surface structure of sodium hexametaphosphate (SHMP) were investigated for aluminum-doped TiO2 pigment undergoing milling. Relaxation NMR was utilized as a potential at-line technique to monitor the effect of milling on surface area and surface chemistry, while XPS was used primarily to consider the dispersant structure. Results showed that considerable amounts of weakly adsorbed SHMP could be removed with washing, and the level of dispersant removal increased with time, highlighting destructive effects of sustained high-energy milling. Nonetheless, there were no significant chemical changes to the dispersant, although increases to the bridging oxygen (BO) peak full width at half-maximum (FWHM) suggested some chemical degradation was occurring with excess milling. Relaxation NMR revealed a number of important features. Results with unmilled material indicated that dispersant adsorption could be tracked with pseudo-isotherms using the relative enhancement rate (Rsp), where the Rsp decreased with dispersant coverage, owing to partial blocking of the quadrupolar surface aluminum. Milled samples were also tracked, with very accurate calibrations of surface area possible from either T1 or T2 relaxation data for systems without dispersant. Behavior was considerably more complicated with SHMP, as there appeared to be an interplay between the dispersant surface coverage and relaxation enhancement from the surface aluminum. Nevertheless, findings highlight that relaxation NMR could be used as a real-time technique to monitor the extent of milling processes, so long as appropriate industrial calibrations can be achieved.

Development and Experimental Validation of a Dispersity Model for In Silico RAFT Polymerization.

The exploitation of computational techniques to predict the outcome of chemical reactions is becoming commonplace, enabling a reduction in the number of physical experiments required to optimize a reaction. Here, we adapt and combine models for polymerization kinetics and molar mass dispersity as a function of conversion for reversible addition fragmentation chain transfer (RAFT) solution polymerization, including the introduction of a novel expression accounting for termination. A flow reactor operating under isothermal conditions was used to experimentally validate the models for the RAFT polymerization of dimethyl acrylamide with an additional term to accommodate the effect of residence time distribution. Further validation is conducted in a batch reactor, where a previously recorded in situ temperature monitoring provides the ability to model the system under more representative batch conditions, accounting for slow heat transfer and the observed exotherm. The model also shows agreement with several literature examples of the RAFT polymerization of acrylamide and acrylate monomers in batch reactors. In principle, the model not only provides a tool for polymer chemists to estimate ideal conditions for a polymerization, but it can also automatically define the initial parameter space for exploration by computationally controlled reactor platforms provided a reliable estimation of rate constants is available. The model is compiled into an easily accessible application to enable simulation of RAFT polymerization of several monomers.

A Brief Introduction to Chemical Reaction Optimization.

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis.

The optimization of multistep chemical syntheses is critical for the rapid development of new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient multistep syntheses, owing to chemical interdependencies between the steps. Herein, we develop an automated continuous flow platform for the simultaneous optimization of telescoped reactions. Our approach is applied to a Heck cyclization-deprotection reaction sequence, used in the synthesis of a precursor for 1-methyltetrahydroisoquinoline C5 functionalization. A simple method for multipoint sampling with a single online HPLC instrument was designed, enabling accurate quantification of each reaction, and an in-depth understanding of the reaction pathways. Notably, integration of Bayesian optimization techniques identified an 81 % overall yield in just 14 h, and revealed a favorable competing pathway for formation of the desired product.

Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation.

For the discovery of new candidate molecules in the pharmaceutical industry, library synthesis is a critical step, in which library size, diversity, and time to synthesise are fundamental. In this work we propose stopped-flow synthesis as an intermediate alternative to traditional batch and flow chemistry approaches, suited for small molecule pharmaceutical discovery. This method exploits the advantages of both techniques enabling automated experimentation with access to high pressures and temperatures; flexibility of reaction times, with minimal use of reagents (µmol scale per reaction). In this study, we integrate a stopped-flow reactor into a high-throughput continuous platform designed for the synthesis of combinatory libraries with at-line reaction analysis. This approach allowed ∼900 reactions to be conducted in an accelerated timeframe (192 hours). The stopped flow approach used ∼10% of the reactants and solvents compared to a fully continuous approach. This methodology demonstrates a significantly improved synthesis success rate of smaller libraries by simplifying the implementation of cross-reaction optimisation strategies. The experimental datasets were used to train a feed-forward neural network (FFNN) model providing a framework to guide further experiments, which showed good model predictability and success when tested against an external set with fewer experiments. As a result, this work demonstrates that combining experimental automation with machine learning strategies can deliver optimised analyses and enhanced predictions, enabling more efficient drug discovery investigations across the design, make, test and analysis (DMTA) cycle.

An automated computational approach to kinetic model discrimination and parameter estimation.

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

On-Demand Electrochemical Synthesis of Tetrakisacetonitrile Copper(I) Triflate and Its Application in the Aerobic Oxidation of Alcohols.

An on-demand electrochemical synthesis of copper(I) triflate under both batch and continuous flow conditions has been developed. A major benefit of the electrochemical methodology is that the only byproduct of the reaction is hydrogen gas, which obviates the need for workup and purification, and water is not incorporated into the product. Upon completion of the electrochemical synthesis, solutions are directly transferred or dispensed into reaction mixtures for the catalytic oxidation of benzyl alcohol with no requirement for workup or purification.

Autonomous optimisation of a nanoparticle catalysed reduction reaction in continuous flow.

An automated continuous flow reactor system equipped with inline analysis, was developed and applied in the self-optimisation of a nanoparticle catalysed reaction. The system was used to optimise the experimental conditions of a gold nanoparticle catalysed 4-nitrophenol reduction reaction, towards maximum conversion in under 2.5 hours. The data obtained from this optimisation was then used to generate a kinetic model, allowing us to predict the outcome of the reaction under different conditions. By combining continuous flow nanoparticle synthesis with this approach, the development timeline for these emerging catalysts could be significantly accelerated.

Acid number, viscosity and end-point detection in a multiphase high temperature polymerisation process using an online miniaturised MEMS Fabry-Pérot interferometer.

Recent advances in the latest generation of MEMS (micro-electro-mechanical system) Fabry-Pérot interferometers (FPI) for near infrared (NIR) wavelengths has led to the development of ultra-fast and low cost NIR sensors with potential to be used by the process industry. One of these miniaturised sensors operating from 1350 to 1650 nm, was integrated into a software platform to monitor a multiphase solid-gas-liquid process, for the production of saturated polyester resins. Twelve batches were run in a 2 L reactor mimicking industrial conditions (24 h process, with temperatures ranging from 220 to 240 °C), using an immersion NIR transmission probe. Because of the multiphase nature of the reaction, strong interference produced by process disturbances such as temperature variations and the presence of solid particles and bubbles in the online spectra required robust pre-processing algorithms and a good long-term stability of the probe. These allowed partial least squares (PLS) regression models to be built for the key analytical parameters acid number and viscosity. In parallel, spectra were also used to build an end-point detection model based on principal component analysis (PCA) for multivariate statistical process control (MSPC). The novel MEMS-FPI sensor combined with robust chemometric analysis proved to be a suitable and affordable alternative for online process monitoring, contributing to sustainability in the process industry.

Process Monitoring of Moisture Content and Mass Transfer Rate in a Fluidised Bed with a Low Cost Inline MEMS NIR Sensor.

PURPOSE: The current trend for continuous drug product manufacturing requires new, affordable process analytical techniques (PAT) to ensure control of processing. This work evaluates whether property models based on spectral data from recent Fabry-Pérot Interferometer based NIR sensors can generate a high-resolution moisture signal suitable for process control. METHODS: Spectral data and offline moisture content were recorded for 14 fluid bed dryer batches of pharmaceutical granules. A PLS moisture model was constructed resulting in a high resolution moisture signal, used to demonstrate (i) endpoint determination and (ii) evaluation of mass transfer performance. RESULTS: The sensors appear robust with respect to vibration and ambient temperature changes, and the accuracy of water content predictions (±13 % ) is similar to those reported for high specification NIR sensors. Fusion of temperature and moisture content signal allowed monitoring of water transport rates in the fluidised bed and highlighted the importance water transport within the solid phase at low moisture levels. The NIR data was also successfully used with PCA-based MSPC models for endpoint detection. CONCLUSIONS: The spectral quality of the small form factor NIR sensor and its robustness is clearly sufficient for the construction and application of PLS models as well as PCA-based MSPC moisture models. The resulting high resolution moisture content signal was successfully used for endpoint detection and monitoring the mass transfer rate.

Statistical quantification of sub-sampling representativeness and uncertainty for waste-derived solid recovered fuel (SRF): Comparison with theory of sampling (ToS).

The level of uncertainty during quantification of hazardous elements/properties of waste-derived products is affected by sub-sampling. Understanding sources of variability in sub-sampling can lead to more accurate risk quantification and effective compliance statistics. Here, we investigate a sub-sampling scheme for the characterisation of solid recovered fuel (SRF) - an example of an inherently heterogeneous mixture containing hazardous properties. We used statistically designed experiments (DoE) (nested balanced ANOVA) to quantify uncertainty arising from material properties, sub-sampling plan and analysis. This was compared with the theoretically estimated uncertainty via theory of sampling (ToS). The sub-sampling scheme derives representative analytical results for relatively uniformly dispersed properties (moisture, ash, and calorific content: RSD ≤ 6.1 %). Much higher uncertainty was recorded for the less uniformly dispersed chlorine (Cl) (RSD: 18.2 %), but not considerably affecting SRF classification. The ToS formula overestimates the uncertainty from sub-sampling stages without shredding, possibly due to considering uncertainty being proportional to the cube of particle size (FE ∝ d3), which may not always apply e.g. for flat waste fragments. The relative contribution of sub-sampling stages to the overall uncertainty differs by property, contrary to what ToS stipulates. Therefore, the ToS approach needs adaptation for quantitative application in sub-sampling of waste-derived materials.

A Hybridised Optimisation of an Automated Photochemical Continuous Flow Reactor.

A new hybridized algorithm that combines process optimisation with response surface mapping was developed and applied in an automated continuous flow reaction. Moreover, a photochemical cascade CSTR was developed and characterised by chemical actinometry, showing photon flux density of ten times greater than previously reported in batch. The success of the algorithm was then evaluated in the aerobic oxidation of sp³ C-H bonds using benzophenone as photosensitizer in the newly developed photo reactor.

Electron spectroscopy of ionic liquids: experimental identification of atomic orbital contributions to valence electronic structure.

The atomic contributions to valence electronic structure for 37 ionic liquids (ILs) are identified using a combination of variable photon energy XPS, resonant Auger electron spectroscopy (RAES) and a subtraction method. The ILs studied include a diverse range of cationic and anionic structural moieties. We introduce a new parameter for ILs, the energy difference between the energies of the cationic and anionic highest occupied fragment orbitals (HOFOs), which we use to identify the highest occupied molecular orbital (HOMO). The anion gave rise to the HOMO for 25 of the 37 ILs studied here. For 10 of the ILs, the energies of the cationic and anionic HOFOs were the same (within experimental error); therefore, it could not be determined whether the HOMO was from the cation or the anion. For two of the ILs, the HOMO was from the cation and not from the anion; consequently it is energetically more favourable to remove an electron from the cation than the anion for these two ILs. In addition, we used a combination of area normalisation and subtraction of XP spectra to produce what are effectively XP spectra for individual ions; this was achieved for 10 cations and 14 anions.

Direct Synthesis of Multiplexed Metal-Nanowire-Based Devices by Using Carbon Nanotubes as Vector Templates.

We present the synthesis of metal nanowires in a multiplexed device configuration using single-walled carbon nanotubes (SWNTs) as nanoscale vector templates. The SWNT templates control the dimensionality of the wires, allowing precise control of their size, shape, and orientation; moreover, a solution-processable approach enables their linear deposition between specific electrode pairs in electronic devices. Electrical characterization demonstrated the successful fabrication of metal nanowire electronic devices, while multiscale characterization of the different fabrication steps revealed details of the structure and charge transfer between the material encapsulated and the carbon nanotube. Overall the strategy presented allows facile, low-cost, and direct synthesis of multiplexed metal nanowire devices for nanoelectronic applications.

Kinetic Treatments for Catalyst Activation and Deactivation Processes based on Variable Time Normalization Analysis.

Progress reaction profiles are affected by both catalyst activation and deactivation processes occurring alongside the main reaction. These processes complicate the kinetic analysis of reactions, often directing researchers toward incorrect conclusions. We report the application of two kinetic treatments, based on variable time normalization analysis, to reactions involving catalyst activation and deactivation processes. The first kinetic treatment allows the removal of induction periods or the effect of rate perturbations associated with catalyst deactivation from kinetic profiles when the quantity of active catalyst can be measured. The second treatment allows the estimation of the activation or deactivation profile of the catalyst when the order of the reactants for the main reaction is known. Both treatments facilitate kinetic analysis of reactions suffering catalyst activation or deactivation processes.

Characterisation of polyphosphate coated aluminium-doped titania nanoparticles during milling.

This paper investigates the characterisation of alumina-doped titania nanoparticles, milled under high-shear over time, in the presence of sodium hexametaphosphate (SHMP) dispersant. Transmission electron microscopy (TEM) indicated that prolonged milling times led to the formation of 10 nm particle fines which were electrostatically attracted to larger particles, where no change in the crystal structure was observed. Primary particle sizes measured by dynamic light scattering (DLS) and TEM were in agreement and showed no change in primary particle size (∼250 nm) with respect to milling time, however, there was a clear reduction in the magnitude of the slow mode decay associated to aggregates. The TiO2 was found to have an isoelectric point (iep) in the range of pH 3-4.5, where an increase in milling time led to a lower pHiep, indicative of an increase in SHMP coverage, which was further supported by an intensification in phosphorus content measured by X-ray fluorescence (XRF). Phosphorus content and zeta potential analysis before and after centrifugal washing showed that SHMP was partially removed or hydrolysed for the longer milled pigment samples, whereas no change was observed for shorter milled samples. Relaxation NMR was also performed, where enhanced relaxation rates at longer milling times were associated partially to increases in surface area and exposure of Al sites, as well as physicochemical changes to SHMP density and structure. It is thought that extended milling times may lead to hydrolysis or other structural changes of the dispersant from the high energy milling conditions, allowing easier removal after washing for longer milled pigments.

Experimental validation of calculated atomic charges in ionic liquids.

A combination of X-ray photoelectron spectroscopy and near edge X-ray absorption fine structure spectroscopy has been used to provide an experimental measure of nitrogen atomic charges in nine ionic liquids (ILs). These experimental results are used to validate charges calculated with three computational methods: charges from electrostatic potentials using a grid-based method (ChelpG), natural bond orbital population analysis, and the atoms in molecules approach. By combining these results with those from a previous study on sulfur, we find that ChelpG charges provide the best description of the charge distribution in ILs. However, we find that ChelpG charges can lead to significant conformational dependence and therefore advise that small differences in ChelpG charges (<0.3 e) should be interpreted with care. We use these validated charges to provide physical insight into nitrogen atomic charges for the ILs probed.