Butler, F., Fiorentini, F., Eisenhardt, K. H. S., & Williams, C. K. (2025). Heterodinuclear Co(III)NA(I) catalysts for the Ring-Opening copolymerization of propene oxide and carbon dioxide. Macromolecules, 58(14), 7150–7160. https://doi.org/10.1021/acs.macromol.5c01529

Four Co(III)Na(I) catalysts (L1-4 Co(III)Na(I)) were synthesized and investigated for their potential for ring opening copolymerization (ROCOP) of PO and CO2 to form polycarbonates. The goal was to identify the optimal ligand coordination environment and operating conditions for the Co(III)/Na(I) metal system, evaluating catalyst performance under three different conditions. The ligands had ordered modifications focusing on the di-imine linker (ethyl and dimethyl propyl linkers) and the number of oxygen donors (five and six) in the ether sites. These variations were chosen since DFT calculations indicted that they influence the metal sites during the rate-determining and selectivity-determining steps.

“Reaction rates were monitored by operando ATR-IR spectroscopy, observing the increase in the intensity of a peak at 1750 cm–1, corresponding to the C=O stretch in the polycarbonate, and a peak at 1810 cm–1, corresponding to the C=O stretch in the cyclic carbonate side product. IR absorbance values were calibrated to conversion via integration of the 1H NMR spectrum of an aliquot taken at the end of the reaction. Semilogarithmic plots of ln([PO]t/[PO]0) versus time are linear, with the observed pseudo first order rate constant, kobs, calculated from the modulus of the gradient. Comparison of the observed catalytic activities reveals significant differences across the series, with L1Co(III)Na(I) performing the best…Catalyst activity, in terms of kobs and TOFPPC, decreased across the remainder of the series in the following order: L2Co(III)Na(I), L3Co(III)Na(I), and L4Co(III)Na(I).” 

Chai, K., Xia, W., Shen, R., Luo, G., Cheng, Y., Su, W., & Su, A. (2024). Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling. Chemical Engineering Science, 302, 120901. https://doi.org/10.1016/j.ces.2024.120901

In the synthesis of an important agrochemical intermediate, 2-amino-3-methylbenzoic acid (AMA), a heterogeneous continuous flow system was constructed for hydrogenation of 3-methyl-2-nitrobenzoic acid (MNA). This system was equipped with in situ IR monitoring and the acquired data were processed with an artificial neural network (ANN) model, yielding real-time concentrations for MNA and AMA. The data-rich experiments were used to compare multi-objective Bayesian optimization (MOBO) with kinetic modeling. The MOBO method efficiently defined the Pareto optimal parameter combinations resulting in a thorough understanding of the trade-off between yield and productivity. Kinetic modeling determined the hydrogenation activation energy and characterized the hydrogen adsorption as competitive dissociation.   Response surfaces generated from the kinetic model also contributed insight into yield and productivity optimization. 

 “ANN modeling was performed for processing inline FTIR spectra according to the reported literature. Several groups of MNA and AMA in methanol solution with different concentrations were prepared and their inline FTIR spectra data were collected as training set and validation set…during the training process, an architecture of one convolutional layer followed by dense layers was investigated. The spectra data was processed at the Conv1D convolutional layer for characteristic extraction to screen the weights of the data. Then, data dimension reduction was performed through different functions in the dense layers. Finally, the output layer outputs the predicted concentrations of MNA and AMA.” 

Xia, W., Zhan, Y., Xia, J., Luo, G., Su, W., Chai, K., & Su, A. (2025). Sustainable Continuous-Flow Catalysis and Bayesian Optimization of Biomass-Derived HMF Hydrogenation over RuPt@g-C3N4. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5638748

Biomass-derived 5-hydroxymethylfurfural (HMF) can be hydrogenated to 2,5-furandimethanol (BHMF), which is an important starting molecule for synthesizing biodegradable polymers, fuel additives, and high-value fine chemicals. This synthesis is challenging in that there are a number of competing hydrogenation pathways, which are influenced by catalyst, solvent and reaction variables that affect selectivity and yield. Due to enhanced control of reactions conditions, there are significant advantages in the use of continuous flow reactors for selective HMF hydrogenation. Thus, the goal of this project was to develop an effective, ecofriendly continuous flow system for HMF hydrogenation to BHMF that uses a RuPt@g-C3N4 catalytic system and water as the solvent. The development methodology employed Bayesian optimization of reaction conditions and inline FTIR based quantitative tracking of reaction dynamics.  The authors comment that the use of Bayesian optimization for both reaction condition screening and catalyst composition design provides an effective approach to optimizing biomass-derived chemical platform molecule synthesis.

  “The concentrations of HMF and BHMF in the reaction solution were monitored in real-time using inline FTIR spectroscopy…To investigate the hydrogenation mechanism, experiments were performed under optimal conditions in a continuous-flow fixed-bed reactor packed with RuPt@g-C3N4... no distinct peaks of reaction intermediates were observed. This likely reflects the high intrinsic activity of RuPt bimetallic sites and the presence of transient intermediates mainly at the catalyst–substrate interface at low coverage…”

Wei, D., Ma, K., Ni, L., Mebarki, A., Fu, G., & Jiang, J. (2025). Thermal safety upgrading: Batch-to-flow transition of acid-catalyzed tert-butyl peroxypivalate synthesis. Chemical Engineering Journal, 523, 168103. https://doi.org/10.1016/j.cej.2025.168103

The synthesis of peroxyesters has inherent thermal safety issues due to the exothermic nature of the reaction and the presence of unstable compounds. In the work described herein, a one pot acid catalyzed acylation to form tert-butyl peroxypivalate (TBPP) was converted from the semi-batch process to a flow process to improve safety. Density function theory (DFT) calculations and in situ IR characterization were used to better understand the mechanism of this acylation. For the semi-batch reaction, calorimetric measurements via DSC, ARC and rection calorimetry were performed to fully understand the thermal behavior of the reaction. Process temperature and dosing rate were found to be the major influence on heat release and highest risk level was associated with conditions that produced the highest yield. 

“…the semi-batch process was transformed to a flow process. The optimization was conducted to maximize space-time yield and minimize E-factor using multi-objective Bayesian optimization and Pareto front was determined…a previously proposed risk assessment method m-ITHI [modified inherent thermal runaway hazard index] was adopted to quantify and compare the thermal risk between semi-batch process and flow process. The assessment result showed that the utilization of the microreactor reduced the thermal runaway hazard from severe (Class V) to moderate (Class II). However, enhanced safety brought by the microreactor did not guarantee the inherent safety of TBPP synthesis process and thermal risk varied along with the process conditions in the microreactor…R-factor was proposed for the first time to quantify the risk variation caused by reaction conditions in flow processes.”

Angulo, A., & McMullen, J. P. (2025). Data-Driven modeling for the enhanced understanding for the crystallization of an active pharmaceutical ingredient. Organic Process Research & Development, 29(6), 1432–1439. https://doi.org/10.1021/acs.oprd.5c00036

Dynamic Response Surface Methodology (DRSM) models were applied to develop a thorough insight into the crystallization procedure for the drug Belzutifan, focusing on understanding the effects of antisolvent addition rate and temperature. 22 full-factorial DoE was performed on the antisolvent crystallization in an OptiMax workstation, and ReactIR was utilized to acquire time-resolved spectra of the supernatant concentration. Calibration of the IR data was performed using offline supernatant samples collected using an EasySampler system and the concentrations were measured using UPLC. An automated partial least squares (PLS) chemometric model was trained to obtain concentration profiles from the IR spectra. From this information, DRSM provided dynamic models to characterize the supernatant concentration as a function of temperature and antisolvent addition. The particle size distribution (PSD) of the crystals was measured as a function of crystallization temperature and antisolvent addition rate. The DRSM model generated was successful at predicting the concentration dynamics as a function of antisolvent fraction, addition rate, and temperature for the DoE training data set as well as a separate validation experiment.

“The results presented in this work show a data-driven modeling approach for the crystallization of an active pharmaceutical ingredient (API) using process analytical technologies (PAT) and the dynamic response surface methodology (DRSM)…. This approach enables users to gain valuable insights into how the factors of antisolvent addition rate and crystallization temperature dynamically influence concentration profiles without requiring a mechanistic or knowledge-based model, all without the necessity of conducting additional experiments. Moreover, we have shown that DRSM is an adequate tool to predict concentration profiles by running a crystallization within the design space of the DoE.”

Akturk, I., Mackey, J. S., Sundarkumar, V., Murbach, G., Joglekar, G., Csathy, A. T., Wehrle, F. J., Thompson, D. H., & Nagy, Z. K. (2026). Toward agile, distributed pharmaceutical manufacturing: continuous End-to-End integration of reaction, purification, and formulation for Lomustine via the MiniPharm platform. Organic Process Research & Development, 30(3), 595–618. https://doi.org/10.1021/acs.oprd.5c00314

This work focuses on the development of the MiniPharm platform to produce the oncology drug lomustine through continuous integrated manufacturing. The researchers joined the drug substance and drug product operations by integrating two telescoped reaction steps, a continuous solvent‑switch distillation, two‑stage MSMPR crystallization, and drop‑on‑demand capsule printing. Development for each of these operations required in depth investigation of reaction kinetics, solubility modeling, PAT monitoring, and process simulations. Through process intensification efforts that included modifications to the reactors, distillation, and crystallization units, as well as integration of automated control and in-line PAT for operation efficiency, the researchers report achieving an overall process yield for lomustine of 69%. 

In initial batch studies, in situ FTIR was used to identify characteristic peaks for starting materials, intermediates and product including cyclohexamine, 2‑chloroethyl isocyanate, the 1‑(2‑chloroethyl)‑3‑cyclohexylurea intermediate, tert‑butyl nitrite, and lomustine. These spectra were used to support kinetic modeling, residence‑time selection, and verification of conversion in the telescoped flow reactors. During experiments with the integrated operational units, the MIR probe was installed in the MSMPR crystallizer to monitor solute concentration, supersaturation, and steady‑state behavior. An in situ microscopic imaging system provided real-time imaging of nucleation, crystal growth, turbidity changes, and potential oiling‑out. The dual PAT strategy in which MIR measured dissolved species, while visual imaging monitored crystal formation was a key to effective continuous crystallization and the successful, fully integrated end‑to‑end workflow.

“To enable real-time monitoring of the reaction and crystallization processes, two PAT tools were employed: Mettler Toledo ReactIR 702L Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) and the Easy-Viewer 100 inline imaging probe. For the batch reaction experiments, ATR-FTIR was used to collect spectra of chemical species at different concentrations and identify the main peaks for the reactants and products. For the flow synthesis, the FTIR probe was immersed in a round-bottom flask placed after the second reactor. The repeated flow synthesis trials confirmed high and consistent conversion rates. For the integrated continuous end-to-end runs, the FTIR probe was repurposed for crystallization monitoring by placing it directly in the MSMPR crystallizer. The EasyViewer 100 was used alongside to visually monitor crystal formation, turbidity, and growth within the MSMPR”.

Abdoun, S., Bour, C., Mayer, R. J., & Vayer, M. (2026). Electrochemical N -Propargylation of N -Heterocycles via decarboxylation of allenoic acids. ACS Organic & Inorganic Au. https://doi.org/10.1021/acsorginorgau.5c00117

Electrosynthesis is investigated as a mean to synthesize C-N bonds without the need for the toxic reagents and waste associated with classical coupling methods. 2,3‑allenoic acids are proposed as potentially effective precursors since their anodic oxidation generates allenyl/propargyl radicals that undergo rapid second oxidation to highly electrophilic cations capable of selective N‑propargylation. The work establishes a mild, additive‑free electrochemical protocol that provides N‑propargylated products across pyrazoles, triazoles, tetrazoles, benzotriazoles, amides, carboxylic acids, and alcohols in 25–82% yield, with strong atom economy and Faradaic efficiencies of 40-70%. Mechanistic studies indicate that the reaction is governed by electrochemical control rather than substrate concentration. Key to understanding the reaction mechanism is the use of ReactIR, which measures real‑time substrate consumption and leads to determining initial rates. These data show the reaction is zero‑order in both the allenoic acid and the nucleophile, while the rate increases linearly with applied current. This indicates that anodic electron transfer is the rate‑limiting step. Along with cyclic voltammetry and DFT, the kinetic evidence supports a pathway in which oxidative decarboxylation forms an allenyl radical that is immediately oxidized to a propargyl cation, which undergoes kinetically controlled C-N bond formation. The investigators conclude by stating that they have established an electrochemical method to propargylate N‑heterocycles using 2,3‑allenoic acids under simple, additive‑free conditions. The reaction employs an inexpensive carbon anode and does not require external oxidants, bases, and dehydrating agents. This investigation illustrates how mechanistic understanding can guide the design of electrosynthetic approaches that generate reactive intermediates for efficient C–N bond construction.

“… kinetic studies were performed by in situ IR spectroscopy. To establish the time-dependent concentration of 1a [allenoic acid] during the kinetic measurement, the linear relationship of the IR absorbance at 1689 cm–1, which corresponds to the C═O band of 1a, and the concentration of 1a was used. The disappearance of 1a over time was then applied to determine initial reaction rates k, which was experimentally measured with different initial concentrations of 1a and 2a [pyrazole]. The initial rates of k were independent of the concentrations of 1a and 2a, suggesting the reaction to be of zero partial order in both allenoic acid 1a and pyrazole 2a. Yet, the initial rate exhibits a linear correlation with the applied current, indicating that the anodic electron transfer process constitutes the rate-limiting step.”

Yang, Y., Guan, Z., Wang, R., Wang, W., Zhang, M., Su, X., & Shao, Y. (2026). Optimization of the Solid-Phase oligonucleotide detritylation reaction using In-Line IR spectroscopy. Organic Process Research & Development, 30(5), 1369–1377. https://doi.org/10.1021/acs.oprd.6c00103

In‑line Mid-IR spectroscopy (ReactIR) is used to optimize detritylation during solid‑phase oligonucleotide synthesis and is shown to provide a more reagent‑efficient alternative to classical UV monitoring. Initially, offline IR measurement of the major components in the detritylation mixture was performed that identified distinct absorption peaks for toluene (1495 cm⁻¹), anhydrous acetonitrile (2253 cm⁻¹), DCA‑monomer (1765 cm⁻¹), and DCA‑dimer (1740 cm⁻¹). An approach for quantitative analysis using real‑time monitoring was established since the IR intensities of the DCA‑monomer and DCA‑dimer displayed strong linearity with concentration in the 3–10% DCA range. 

From kinetics and mechanistic understanding of the detritylation reaction, the relative concentrations of the DCA-monomer and DCA dimer closely relate to the progress of detritylation, and DCA‑dimer formation is the rate‑determining step of detritylation. DCA exists predominantly as a chemically active dimer in toluene, whereas in acetonitrile it shifts to an inactive monomeric form. Detritylation proceeds efficiently when the DCA inactive monomer has fully converted into the active dimer. This work correlated in‑line IR tracking of these DCA structural changes with traditional UV detection of dimethoxytrityl cation (DMT⁺), a UV active species released during the detritylation step of oligonucleotide solid‑phase synthesis. This correlation showed that the decay of the inactive DCA‑monomer IR signal down to the baseline, defined as node III, signified the optimal reaction end-point. At this point, product purity, depurination levels, and overall yields were equivalent to those obtained using UV‑based control, however, these was as much as a 40.4% reduction in detritylation reagent consumed. To validate the robustness and generality of this IR‑guided strategy, oligonucleotide sequences with diverse structural and chemical features were synthesized using this node III criterion. In all sequences, product quality remained consistent, depurination side reactions were effectively suppressed, and reagent usage decreased by 22.7–46.7%. 

“Compared to classical in-line UV monitoring, in-line IR technology overcomes the limitations of UV monitoring, including a narrow signal response range, delayed monitoring, and inadequate monitoring of the microreaction process. It enables real-time tracking of the microkinetic changes of key components in detritylation, reduces reagent consumption, and promotes the green and sustainable development of solid phase synthesis. This study provides a universal strategy for improving the precision of solid-phase synthesis process control and enabling full process-refined production of oligonucleotide.”

Pucihar, U., Vračar, P., Bitenc, M., & Kopač, T. (2025). Self-optimization of Claisen-Schmidt condensation in an automated microflow reaction system using machine learning. Computers & Chemical Engineering, 201, 109261. https://doi.org/10.1016/j.compchemeng.2025.109261

Using the Claisen-Schmidt aldol condensation reaction between 2-methoxybenzaldehyde and acetone as an example, this work demonstrates the development and application of a novel self-optimization system. The authors report that a multi-objective algorithm, TSEMO (Thompson sampling efficient multi-objective optimization), is used in combination with in-line MIR (ReactIR) and on-line UHPLC PAT on a supervisory control and data acquisition (SCADA) controlled microfluidic reactor, used for continuous variable optimization. Optimal reaction conditions were established including temperature, flow rates, and residence times and the system was validated through kinetic modeling. 

“ …the self-optimization process outperformed traditional chemical reaction methods by reaching target concentrations (set goals) faster, with fewer chemicals, and minimal manual intervention. This not only enhances the environmental sustainability and economic efficiency of the process but also shows potential for broader application in reaction engineering. The close alignment of experimental data with theoretical predictions, falling within a ±10 % deviation, confirms the system’s ability to optimize without prior knowledge of reaction kinetics…”

Zou, X., Xie, T., Xu, J., Bai, H., Wang, D., Ke, X., Zhao, W., & Lv, M. (2026). Unveiling Autocatalytic Kinetics in the Short-Chain Polyaddition of Poly(amic acid) by In Situ FTIR. Industrial & Engineering Chemistry Research, 65(10), 5452–5459. https://doi.org/10.1021/acs.iecr.6c00046

There are two stages in the polyaddition of aromatic diamines and dianhydrides (PAA), rapid short-chain formation stage (stage 1) and a subsequent slow chain-growth stage (stage 2). Virtually all kinetic studies to date focus on the second stage kinetics using a variety of techniques.  Though these methods are effective with low-reactivity monomers, they do not capture the highly exothermic stage 1of more reactive monomers. This leads to simulation deviations of 20−30% in the first few minutes of reaction, undermining process control, scale‑up reliability, and safety. Thus, real‑time data-rich monitoring is critical to capture the proposed autocatalytic behavior controlling this early stage.  

In this work, in situ FTIR is used to provide the information necessary for a more thorough understanding of the kinetics in the early-stage polyaddition of poly(amic acid) (PAA). This is typically considered to be a second-order kinetic process, in which the reaction rate is proportional to the product of the diamine and dianhydride concentrations. Theoretical models indicate that these reactions demonstrate pronounced autocatalytic characteristics, in which reaction products accelerate the subsequent reaction rates, and that intermediates cause the autocatalytic effect.  A model for the autocatalytic kinetics of highly reactive aromatic diamine–dianhydride systems is developed that includes mechanism hypotheses, rate equation derivation, data fitting, and thermodynamic analysis. The model provides insight into how components such as temperature and monomer structure control the amount of autocatalysis. The data reveal that the short‑chain polyaddition follows a second‑order irreversible autocatalytic kinetic model, driven primarily by proton catalysis from the newly formed –COOH groups. Temperature and monomer electrophilicity jointly modulate the extent of autocatalysis, with BTDA showing the strongest catalytic acceleration and s‑BPDA the weakest. 

“We employ in situ FTIR spectroscopy to quantitatively investigate the early-stage polyaddition of 4,4′-diaminodiphenylmethane (MDA) with three dianhydrides: 3,3′,4,4′-benzophenone tetracarboxylic dianhydride (BTDA), 3,3′,4,4′-biphenyltetracarboxylic dianhydride (s-BPDA), and 2,3,3′,4′-biphenyltetracarboxylic dianhydride (a-BPDA). The isolated characteristic band of MDA at 818 cm^–1 can serve as an effective probe for quantitatively monitoring the variation of the MDA concentration. Kinetic analysis of the normalized band intensities (A-818 cm^–1, T-818 cm^–1, and S-818 cm^–1) reveals that the reaction follows second-order irreversible autocatalytic kinetics. This combined in situ FTIR and modeling approach provides the first quantitative insights into how dianhydride electrophilicity, molecular structure, and temperature synergistically govern the extent of autocatalysis, thereby offering critical experimental and theoretical guidance for the precise control and optimization of PAA synthesis.”

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• Zou, X., Xie, T., Xu, J., Bai, H., Wang, D., Ke, X., Zhao, W., & Lv, M. (2026). Unveiling Autocatalytic Kinetics in the Short-Chain Polyaddition of Poly(amic acid) by In Situ FTIR. Industrial & Engineering Chemistry Research, 65(10), 5452–5459. https://doi.org/10.1021/acs.iecr.6c00046
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