Quantitative reaction monitoring using parahydrogen-enhanced benchtop NMR spectroscopy

The parahydrogen-induced polarisation (PHIP) NMR signal enhancement technique is used to study H2 addition to Vaska's complex (trans-[IrCl(CO)(PPh3)2]) with both standard high-field (9.4 T) NMR and benchtop (1 T) NMR detection. Accurate and repeatable rate constants of (0.84 ± 0.03) dm3 mol-1 s-1 and (0.89 ± 0.03) dm3 mol-1 s-1 were obtained for this model system using standard high-field and benchtop NMR, respectively. The high-field NMR approach is shown to be susceptible to systematic errors associated with interference from non-hyperpolarised signals, which can be overcome through a multiple-quantum filtered acquisition scheme. This challenge is avoided when using benchtop NMR detection because the non-hyperpolarised signals are much weaker due to the lower magnetic field, enabling the use of a simpler and more efficient single RF pulse detection scheme. Method validation against several experimental parameters (NMR relaxation, %pH2 enrichment and temperature) demonstrates the robustness of the benchtop NMR approach but also highlights the need for sample temperature control throughout reaction monitoring. A simple temperature equilibration protocol, coupled with use of an insulated sample holder while manipulating the sample outside the spectrometer, is found to provide sufficient temperature stabilisation to ensure that accurate and repeatable rate constants are obtained. Finally, the benchtop NMR reaction monitoring protocol is applied to the analysis of a complex mixture, where multiple reaction products form simultaneously. H2 addition to a mixture of three Vaska's complex derivatives was monitored, revealing the presence of competitive reaction pathways within the mixture.

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Horseradish peroxidase-catalyzed polyacrylamide gels: monitoring their polymerization with BSA-stabilized gold nanoclusters and their functional validation in electrophoresis

Polyacrylamide gel (PAG) is extensively used as a matrix for biomolecular analysis and fractionation. However, the traditional polymerization catalyst system N,N,N',N'-tetramethylethylenediamine (TEMED)/ammonium persulphate (APS) of PAG presents non-negligible toxicity. Herein, we utilized the green and efficient bio-enzyme horseradish peroxidase (HRP) to catalyze the gel polymerization of polyacrylamide. At the same time, the efficacy of this gel system in separating nucleic acids and proteins was confirmed by applying the gel system in electrophoresis. This study aims to explore a higher biosafety polyacrylamide gel polymerization catalytic system which can be applied to electrophoresis technology. Furthermore, in order to differentiate between the bio-enzymatic catalytic system and the traditional toxic catalytic system during polymerization, aggregation-induced luminescence (AIE) of bovine serum albumin-stabilized gold nanoclusters (BSA-Au NCs) was used to monitor the polymerization reaction of the system. The results indicated that the fluorescence intensity of the polymeric system containing BSA-Au NCs increased with the polymerization of the monomers. Subsequently, we assessed whether certain components of nucleic acid electrophoresis and protein electrophoresis such as sodiumdodecylsulfate (SDS) and TBE buffer (Tris-boric acid, EDTA, pH 8.3) would affect the polymerization of the polyacrylamide gels catalyzed by the biological enzymes. The experimental conditions were also optimized to explore the optimal concentration of the ternary system of HRP, H2O2 and ACAC. Our results suggested that the bioenzyme-catalyzed system could be a feasible alternative to the TEMED/APS-catalyzed system, which also could provide new insights into the methods of monitoring the polymerization system.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

High Throughput Multidimensional Kinetic Screening in Continuous Flow Reactors

An automated high throughput multidimensional reaction screening platform based on an inline Fourier-transform infrared spectroscopy is presented. By combining flow chemistry, machine automation and inline analysis, the platform is able to screen reactions in multidimensions (residence time, monomer concentration, degree of polymerization, reaction temperature and monomer conversion) rapidly and efficiently way. Kinetic data libraries associated with high data precision (absolute error <4 %), high reproducibility and high data density are built with ease from the platform. To test the method, we screened the reversible addition-fragmentation chain transfer polymerization of methyl acrylate in unmatched detail, and the ring opening metathesis polymerization of methyl-5-norbornene-2-carboxylate. The method we introduce is a key step in providing "big data" for data driven research in the future, and already at present allows for precise prediction of reaction outcomes within the high-dimensional chemical parameter space that is screened.

Continuous-Flow Laboratory SAXS for In Situ Determination of the Impact of Hydrophilic Block Length on Spherical Nano-Object Formation during Polymerization-Induced Self-Assembly

In situ small-angle X-ray scattering (SAXS) is a powerful technique for characterizing block-copolymer nano-object formation during polymerization-induced self-assembly. To work effectively in situ, it requires high intensity X-rays which enable the short acquisition times required for real-time measurements. However, routine access to synchrotron X-ray sources is expensive and highly competitive. Flow reactors provide an opportunity to obtain temporal resolution by operating at a consistent flow rate. Here, we equip a flow-reactor with an X-ray transparent flow-cell at the outlet which facilitates the use of a low-flux laboratory SAXS instrument for in situ monitoring. The formation and morphological evolution of spherical block copolymer nano-objects was characterized during reversible addition fragmentation chain transfer polymerization of diacetone acrylamide in the presence of a series of poly(dimethylacrylamide) (PDMAm) macromolecular chain transfer agents with varying degrees of polymerization. SAXS analysis indicated that during the polymerization, highly solvated, loosely defined aggregates form after approximately 100 s, followed by expulsion of solvent to form well-defined spherical particles with PDAAm cores and PDMAm stabilizer chains, which then grow as the polymerization proceeds. Analysis also indicates that the aggregation number (Nagg) increases during the reaction, likely due to collisions between swollen, growing nanoparticles. In situ SAXS conducted on PISA syntheses using different PDMAm DPs indicated a varying conformation of the chains in the particle cores, from collapsed chains for PDMAm47 to extended chains for PDMAm143. At high conversion, the final Nagg decreased as a function of increasing PDMAm DP, indicating increased steric stabilization afforded by the longer chains which is reflected by a decrease in both core diameter (from SAXS) and hydrodynamic diameter (from DLS) for a constant core DP of 400.

Advances in continuous polymer analysis in flow with application towards biopolymers

Biopolymers, polymers derived from renewable biomass sources, have gained increasing attention in recent years due to their potential to replace traditional petroleum-based polymers in a range of applications. Among the many advantages of biopolymers can be included their biocompatibility, excellent mechanical properties, and availability from renewable feedstock. However, the development of biopolymers has been limited by a lack of understanding of their properties and processing behaviours. Continuous analysis techniques have the potential to hasten progress in this area by providing real-time insights into the properties and processing of biopolymers. Significant research in polymer chemistry has focused on petroleum-derived polymers and has thus provided a wealth of synthetic and analytical methodologies which may be applied to the biopolymer field. Of particular note is the application of flow technology in polymer science and its implications for accelerating progress towards more sustainable and environmentally friendly alternatives to traditional petroleum-based polymers. In this mini review we have outlined several of the most prominent use cases for biopolymers along with the current state-of-the art in continuous analysis of polymers in flow, including defining and differentiating atline, inline, online and offline analysis. We have found several examples for continuous flow analysis which have direct application to the biopolymer field, and we demonstrate an atline continuous polymer analysis method using size exclusion chromatography.

Graphical abstract

Rapid Online Analysis of Photopolymerization Kinetics and Molecular Weight Using Diffusion NMR

Online, high-throughput molecular weight analysis of polymerizations is rare, with most studies relying on tedious sampling techniques and batchwise postanalysis. The ability to track both monomer conversion and molecular weight evolution in real time could underpin precision polymer development and facilitate study of rapid polymerization reactions. Here, we use a single time-resolved diffusion nuclear magnetic resonance (NMR) experiment to simultaneously study the kinetics and molecular weight evolution during a photopolymerization, with in situ irradiation inside the NMR instrument. As a model system, we used a photoinduced electron transfer reversible addition-fragmentation chain transfer (PET-RAFT) polymerization. The data allow diffusion coefficients and intensities to be calculated every 14 s from which the polymer size and monomer conversion can be extracted. Key to this approach is (1) the use of shuffled gradient amplitudes in the diffusion NMR experiment to access reactions of any rate, (2) the addition of a relaxation agent to increase achievable time resolution and, (3) a sliding correction that accounts for viscosity changes during polymerization. Diffusion NMR offers a uniquely simple, translatable handle for online monitoring of polymerization reactions.

Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation

Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these...

Convenient and accurate insight into solution-phase equilibria from FlowNMR titrations

Solution phase titrations are made easy by multi-nuclear FlowNMR spectroscopy with automated, continuous titre addition to give accurate insights into Brønsted acid/base, hydrogen bonding, Lewis acid/base and metal/ligand binding equilibria under native conditions.

Recent Applications of Benchtop Nuclear Magnetic Resonance Spectroscopy

Benchtop nuclear magnetic resonance (NMR) spectroscopy uses small permanent magnets to generate magnetic fields and therefore offers the advantages of operational simplicity and reasonable cost, presenting a viable alternative to high-field NMR spectroscopy. In particular, the use of benchtop NMR spectroscopy for rapid in-field analysis, e.g., for quality control or forensic science purposes, has attracted considerable attention. As benchtop NMR spectrometers are sufficiently compact to be operated in a fume hood, they can be efficiently used for real-time reaction and process monitoring. This review introduces the recent applications of benchtop NMR spectroscopy in diverse fields, including food science, pharmaceuticals, process and reaction monitoring, metabolomics, and polymer materials.

High-Throughput Routes to Biomaterials Discovery

Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.

Engineering aspects of FlowNMR spectroscopy setups for online analysis of solution-phase processes

In this article we review some fundamental engineering concepts and evaluate components and materials required to assemble and operate safe and effective FlowNMR setups that reliably generate meaningful results.

Low-field benchtop NMR spectroscopy: status and prospects in natural product analysis†

INTRODUCTION

Since a couple of years, low-field (LF) nuclear magnetic resonance (NMR) spectrometers (40-100 MHz) have re-entered the market. They are used for various purposes including analyses of natural products. Similar to high-field instruments (300-1200 MHz), modern LF instruments can measure multiple nuclei and record two-dimensional (2D) NMR spectra.

OBJECTIVE

To review the commercial availability as well as applications, advantages, limitations, and prospects of LF-NMR spectrometers for the purpose of natural products analysis.

METHOD

Commercial LF instruments were compared. A literature search was performed for articles using and discussing modern LF-NMR. Next, the articles relevant to natural products were read and summarised.

RESULTS

Seventy articles were reviewed. Most appeared in 2018 and 2019. Low costs and ease of operation are most often mentioned as reasons for using LF-NMR.

CONCLUSION

As the spectral resolution of LF instruments is limited, they are not used for structure elucidation of new natural products but rather applied for quality control (QC), forensics, food and health research, process control and teaching. Chemometric data handling is valuable. LF-NMR is a rapidly developing niche and new instruments keep being introduced.

Benchtop Low-Frequency 60 MHz NMR Analysis of Urine: A Comparative Metabolomics Investigation

Metabolomics techniques are now applied in numerous fields, with the ability to provide information concerning a large number of metabolites from a single sample in a short timeframe. Although high-frequency (HF) nuclear magnetic resonance (NMR) analysis represents a common method of choice to perform such studies, few investigations employing low-frequency (LF) NMR spectrometers have yet been published. Herein, we apply and contrast LF and HF ¹H-NMR metabolomics approaches to the study of urine samples collected from type 2 diabetic patients (T2D), and apply a comparative investigation with healthy controls. Additionally, we explore the capabilities of LF ¹H-¹H 2D correlation spectroscopy (COSY) experiments regarding the determination of metabolites, their resolution and associated analyses in human urine samples. T2D samples were readily distinguishable from controls, with several metabolites, particularly glucose, being associated with this distinction. Comparable results were obtained with HF and LF spectrometers. Linear correlation analyses were performed to derive relationships between the intensities of 1D and 2D resonances of several metabolites, and R² values obtained were able to confirm these, an observation attesting to the validity of employing 2D LF experiments for future applications in metabolomics studies. Our data suggest that LF spectrometers may prove to be easy-to-use, compact and inexpensive tools to perform routine metabolomics analyses in laboratories and ‘point-of-care’ sites. Furthermore, the quality of 2D spectra obtained from these instruments in half an hour would broaden the horizon of their potential applications.

Online tracing of molecular weight evolution during radical polymerization via high-resolution FlowNMR spectroscopy

High-resolution FlowNMR was coupled to a continuous flow reactor to monitor polymer molecular weight evolution online by diffusion ordered NMR spectroscopy.

Rapid production of block copolymer nano-objects via continuous-flow ultrafast RAFT dispersion polymerisation

Continuous-flow reactors are exploited for conducting ultrafast RAFT dispersion polymerisation for the preparation of diblock copolymer nanoparticles.

Enabling technologies in polymer synthesis: accessing a new design space for advanced polymer materials

This review discusses how developments in laboratory technologies can push the boundaries of what is achievable using existing polymer synthesis techniques.

Automated Polymer Synthesis Platform for Integrated Conversion Targeting Based on Inline Benchtop NMR

An automated polymer synthesis platform based on an inline low-field nuclear magnetic resonance spectrometer is developed. Flow chemistry and automated inline analyses are an excellent combination for automated kinetic screening and for self-optimizing reactions with programmable conversion targeting. By monitoring monomer conversion over a continuous range of reactor residence times, the platform is able to construct kinetic profiles of polymerizations in an accurate and efficient way. The machine-assisted self-optimization routine allows the reaction to be stopped at any given preselected conversion, giving rise to unprecedented reproducibility in polymer synthesis.