Assessment of the Thermal Degradation of Sodium Lauroyl Isethionate Using Predictive Isoconversional Kinetics and a Temperature-Resolved Analysis of Evolved Gases

Sodium lauroyl isethionate is a popular, milder alternative to traditional soaps and surfactants in personal care formulations. Product performance, efficiency, color, and odor, however, can be compromised by thermal degradation at elevated manufacturing temperatures. Prediction of isothermal degradation rates in both air and N2 for a range of process conditions are determined using the Friedman isoconversional method. The thermal degradation levels in air are found to be 28 times higher than those in N2 over 5 h at 240 °C. Manufacturing under inert conditions, with maximum temperatures of 250 °C, is therefore necessary to avoid degradation levels significantly greater than 1 wt %. Using TGA-FTIR, the evolved gases from the degradation of sodium lauroyl isethionate are identified to be water, carbon dioxide, carbon disulfide, sulfur dioxide, as well as alkyl and carbonyl species. The ensuing temperature-dependent analysis can be used to minimize evolution of undesirable or hazardous gases in isethionate manufacturing processes

Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling

Reaction optimisation and understanding is fundamental for process development and is achieved using a variety of techniques. This paper explores the use of self-optimisation and experimental design as a tandem approach to reaction optimisation. A Claisen-Schmidt condensation was optimised using a branch and fit minimising algorithm, with the resulting data being used to fit a response surface model. The model was then applied to find new responses for different metrics, highlighting the most important for process development purposes

Process-Focused Synthesis, Crystallization, and Physicochemical Characterization of Sodium Lauroyl Isethionate

There is a notable lack of published data concerning sodium cocoyl isethionate despite widespread application in the personal care industry. A specific homologue, sodium lauroyl isethionate (SLI), was therefore synthesized, purified by recrystallization, and then subjected to a detailed physicochemical examination. A purity of 98% was achieved via repeat recrystallization in methanol. A turbidimetric solubility analysis was then executed to identify both its crystallizability and metastable zone width as a function of temperature. Thermogravimetric analysis yielded decomposition onsets of 330 °C for the purified SLI. A dynamic vapor sorption study also demonstrated reversibility in the 2.3% mass gained when it was exposed to sustained humidity of 87%. Surface tension measurements of purified SLI yielded a critical micellar concentration (CMC) of 5.4 mM and a plateau surface tension of 38 mN/m at 20 °C. Both values are lower than the previously reported values for SLI in water, thus indicating the performance benefits of purified isethionates in personal care formulations. The single step synthesis was chlorine-, catalyst-, and solvent-free, thus improving process efficiency, safety, and throughput over existing SLI syntheses. The succeeding physicochemical analysis crucially provides much needed insight into the purification, properties, and perform ance of isethionate ester surfactants, all of which are strongly applicable to their commercial manufacture from biorenewable sources

Competitive Adsorption of Interfacially Active Nanoparticles and Anionic Surfactant at the Crude Oil–Water Interface

The interfacial activity of poly(N-isopropylacrylamide) (pNIPAM) nanoparticles in the absence and presence of an anionic surfactant (sodium dodecyl sulfate, SDS) was studied at a crude oil–water interface. Both species are interfacially active and can lower the interfacial tension, but when mixed together, the interfacial composition was found to depend on the aging time and total component concentration. With the total component concentration less than 0.005 wt %, the reduced interfacial tension by pNIPAM was greater than SDS; thus, pNIPAM has a greater affinity to partition at the crude oil–water interface. However, the lower molecular weight (smaller molecule) of SDS compared to pNIPAM meant that it rapidly partitioned at the oil–water interface. When mixed, the interfacial composition was more SDS-like for low total component concentrations (≤ 0.001 wt %), while above, the interfacial composition was more pNIPAM-like, similar to the single component response. Applying a weighted arithmetic mean approach, the surface-active contribution (%) could be approximated for each component, pNIPAM and SDS. Even though SDS rapidly partitioned at the oil–water interface, it was shown to be displaced by the pNIPAM nanoparticles, and for the highest total component concentration, pNIPAM nanoparticles were predominantly contributing to the reduced oil–water interfacial tension. These findings have implications for the design and performance of fluids that are used to enhance crude oil production from reservoirs, particularly highlighting the aging time and component concentration effects to modify interfacial tensions

Accelerated Chemical Reaction Optimization using Multi-Task Learning

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations need to be executed in the presence of polar functionality necessary for fragment-protein binding. New technologies such as high-throughput experimentation and self-optimization have the potential to revolutionize synthetic approaches to challenging target molecules in FBDD. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions, however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multi-task Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected during related historical optimization campaigns to accelerate the optimization of new reactions - this was performed for Suzuki-Miyaura and C–N couplings. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions (both continuous and categorical variables) of unseen experimental C–H activation reactions with differing substrates, demonstrating up to a 98 % cost reduction when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, where efficient utilization of precious starting materials is particularly important. This work represents a step-change in the utilization of previously obtained reaction data and machine learning with the ultimate goal of accelerated reaction optimization

Accelerated Chemical Reaction Optimization Using Multi-Task Learning.

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C–H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C–H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization