Living Cationic Polymerizations Utilizing an Automated Synthesizer: High‐Throughput Synthesis of Polyoxazolines

Living cationic ring‐opening polymerizations of 2‐ethyl‐2‐oxazoline and purification of the resulting polymers were performed utilizing an automated synthesizer. Eight polymers (500 mg scale) as well as 40 polymers (150 mg scale) were synthesized in parallel to investigate the reproducibility and the living character of the polymerizations. The poly(2‐ethyl‐2‐oxazoline)s obtained such were characterized by means of ¹H NMR spectroscopy, MALDI‐TOF mass spectrometry and online gel permeation chromatography.

     

Instrumentation for Combinatorial and High‐Throughput Polymer Research: A Short Overview

Starting in biochemistry and pharmaceutical chemistry, combinatorial methods, automated synthesis and high‐throughput characterization are being further developed for organic synthesis and polymer research. The development is strongly driven by the achievements in biochemistry and pharmaceutical chemistry and the need to minimize the time‐to‐market for novel polymeric products. The success of high‐throughput methodologies in polymer science is partially limited by the commercially available hardware (synthesizers, workstations, robots, online‐characterization instruments, etc.) and software. A short overview of commercially available equipment for polymer research is provided in this Review.

     

Statistical inference for a Wiener-based degradation model with imperfect maintenance actions under different observation schemes

This paper studies the statistical inference in a degradation model with imperfect maintenance. Technological or industrial devices subject to degradation undergo maintenance actions that reduce their degradation level. The underlying degradation process is a Wiener process with drift. Maintenance effects are assumed to be imperfect, described by an Arithmetic Reduction of Degradation ($AR{D}_1$) model. The system is regularly inspected and the degradation levels are measured. Four different observation schemes are considered so that degradation levels can be observed between maintenance actions as well as just before or just after maintenance times. The paper studies the estimation of the model parameters under the four observation schemes. Maximum likelihood estimators are derived for each scheme. The quality of the estimations is assessed and the observation schemes are compared through an extensive simulation and performance study.     

Some statistical challenges in automated driving systems

Automated driving systems are rapidly developing. However, numerous open problems remain to be resolved to ensure this technology progresses before its widespread adoption. A large subset of these problems are, or can be framed as, statistical decision problems. Therefore, we present herein several important statistical challenges that emerge when designing and operating automated driving systems. In particular, we focus on those that relate to request-to-intervene decisions, ethical decision support, operations in heterogeneous traffic, and algorithmic robustification. For each of these problems, earlier solution approaches are reviewed and alternative solutions are provided with accompanying empirical testing. We also highlight open avenues of inquiry for which applied statistical investigation can help ensure the maturation of automated driving systems. In so doing, we showcase the relevance of statistical research and practice within the context of this revolutionary technology.     

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm (OCA) with that of a heuristic rules-based algorithm (HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that (i) capacity under the HRA (denoted as C_H) is smaller than capacity under the OCA; (ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA; (iii) when the inflow rate is smaller than C_H, the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost; (iv) when the inflow rate is larger than C_H, the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.     

Automated Parallel Temperature Optimization and Determination of Activation Energy for the Living Cationic Polymerization of 2‐Ethyl‐2‐oxazoline

Sixteen parallel polymerization reactions of 2‐ethyl‐2‐oxazoline have been performed at different temperatures in an automated synthesizer that allowed individual heating of each reactor. During the reactions samples were taken automatically, which were characterized by means of both online GPC and offline GC, in order to optimize the reaction temperature and to determine the activation energy of the polymerization.

     

Potentials and Limitations of Automated Parallel Emulsion Polymerization

The application of automated parallel synthesizer robots for the investigation of polymerization processes is of major interest at present. In this contribution we describe the application of the emulsion polymerization of styrene and vinyl acetate. The preparations of emulsions and latexes were investigated in detail and compared to “conventional” stirred tank reactors. In particular the influence of the vortex mixing as well as the limitations regarding solid content and reactor fouling are addressed.

     

All-Aqueous,Surfactant-Free,and pH-Driven Nanoformulation Methods of Dual-Responsive Polymer Nanoparticles and their Potential use as Nanocarriers of pH-Sensitive Drugs

All-aqueous, surfactant-free, and pH-driven nanoformulation methods to generate pH- and temperature-responsive polymer nanoparticles (NPs) are described. Copolymers comprising a poly(methyl methacrylate) (PMMA) backbone with a few units of 2-(dimethylamino)ethyl methacrylate (DMAEMA) are solubilized in acidic buffer (pH 2.0) to produce pH-sensitive NPs. Copolymers of different molar mass (2.3–11.5 kg mol⁻¹) and DMAEMA composition (7.3–14.2 mol%) are evaluated using a “conventional” pH-driven nanoformulation method (i.e., adding an aqueous polymer solution (acidic buffer) into an aqueous non-solvent (basic buffer)) and a robotized method for pH adjustment of polymer dispersions. Dynamic light scattering, zeta-potential (ζ), and sedimentation-diffusion analyses suggest the formation of dual-responsive NPs of tunable size (from 20 to 110 nm) being stable for at least 28 days in the pH and temperature intervals from 2.0 to 6.0 and 25 to 50 °C, respectively. Ultraviolet-visible spectroscopic experiments show that these NPs can act as nanocarriers for the pH-sensitive dipyridamole drug, expanding its bioavailability and potential controlled release as a function of pH and temperature. These approaches offer alternative strategies to prepare stimuli-responsive NPs, avoiding the use of harmful solvents and complex purification steps, and improving the availability of biocompatible polymer nanoformulations for specific controlled release of pH-sensitive cargos.