Air Quality Monitoring for Vulnerable Groups in Residential Environments Using a Multiple Hazard Gas Detector.

This paper presents a smart "e-nose" device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multiple hazard gas detector (MHGD) system that is mounted on a motor vehicle-based robot which can be remotely controlled. First, we optimized the sensor array for the classification of three hazardous gases, including cigarette smoke, inflammable ethanol, and off-flavor from spoiled food, using an e-nose with a mixing chamber. The mixing chamber can prevent the impact of environmental changes. We compared the classification results of all combinations of sensors, and selected the one with the highest accuracy (98.88%) as the optimal sensor array for the MHGD. The optimal sensor array was then mounted on the MHGD to detect and classify the target gases without a mixing chamber but in a controlled environment. Finally, we tested the MHGD under these conditions, and achieved an acceptable accuracy (70.00%).

Exploring Strategies To Bias Sequence in Natural and Synthetic Oligomers and Polymers.

Millions of years of biological evolution have driven the development of highly sophisticated molecular machinery found within living systems. These systems produce polymers such as proteins and nucleic acids with incredible fidelity and function. In nature, the precise molecular sequence is the factor that determines the function of these macromolecules. Given that the ability to precisely define sequence emerges naturally, the fact that biology achieves unprecedented control over the unit sequence of the monomers through evolved enzymatic catalysis is incredible. Indeed, the ability to engineer systems that allow polymer synthesis with precise sequence control is a feat that technology is yet to replicate in artificial synthetic systems. This is the case because, without access to evolutionary control for finely tuned biological catalysts, the inability to correct errors or harness multiple competing processes means that the prospects for digital control of polymerization have been firmly bootstrapped to biological systems or limited to stepwise synthetic protocols. In this Account, we give an overview of strategies that have been used over the last 5 years in efforts to program polymer synthesis with sequence control in the laboratory. We also briefly explore how the use of robotics, algorithms, and stochastic chemical processes might lead to new understanding, mechanisms, and strategies to achieve full digital control. The aim is to see whether it is possible to go beyond bootstrapping to biological polymers or stepwise chemical synthesis. We start by describing nonenzymatic techniques used to obtain sequence-controlled natural polymers, a field that lends itself to direct application of insights gleaned from biology. We discuss major advances, such as the use of rotaxane-based molecular machines and templated approaches, including the utilization of biological polymers as templates for purely synthetic chains. We then discuss synthetic polymer chemistry, whose array of techniques allows the production of polymers with enormous structural and functional diversity, but so far with only limited control over the unit sequence itself. Synthetic polymers can be subdivided into multiple classes depending on the nature of processes used to synthesize them, such as by addition or condensation. Consequently, varied approaches for sequence control have been demonstrated in the area, including but not limited to click reactions, iterative solid-phase chemistry, and exploiting the chemical affinity of the monomers themselves. In addition to those, we highlight the importance of environmental bias in possible control of polymerization at the single-unit level, such as using catalyst switching or external stimuli. Even the most successful experimental sequence control approach needs appropriate tools to verify its scope and validity; therefore, we devote part of the present Account to possible analytical approaches to sequence readout, starting with well-established tandem mass spectrometry techniques and touching on those more applicable to specific classes of processes, such as diffusion-ordered NMR spectroscopy. Finally, we discuss progress in modeling and automation of sequence-controlled polymers. We postulate that developments in analytical chemistry, bioinformatics, and computer modeling will lead to new ways of exploring the development of new strategies for the realization of sequence control by means of sequence bias. This is the case because treating the assembly of polymers as a network of chemical reactions will enable the development of control strategies that can bias the outcome of the polymer assembly. The grand aim would be the synthesis of complex polymers in one step with a precisely defined digital sequence.

Emergent Properties of Giant Vesicles Formed by a Polymerization-Induced Self-Assembly (PISA) Reaction.

Giant micrometer sized vesicles are of obvious interest to the natural sciences as well as engineering, having potential application in fields ranging from drug delivery to synthetic biology. Their formation often requires elaborate experimental techniques and attempts to obtain giant vesicles from chemical media in a one-pot fashion have so far led to much smaller nanoscale structures. Here we show that a tailored medium undergoing controlled radical polymerization is capable of forming giant polymer vesicles. Using a protocol which allows for an aqueous reaction under mild conditions, we observe the macroscale consequences of amphiphilic polymer synthesis and the resulting molecular self-assembly using fluorescence microscopy. The polymerization process is photoinitiated by blue light granting complete control of the reaction, including on the microscope stage. The self-assembly process leads to giant vesicles with radii larger than 10 microns, exhibiting several emergent properties, including periodic growth and collapse as well as phototaxis.

Unusual kinetics of poly(ethylene glycol) oxidation with cerium(IV) ions in sulfuric acid medium and implications for copolymer synthesis.

The cerium(IV)-alcohol couple in an acidic medium is an example of a redox system capable of initiating free radical polymerization. When the alcohol has a polymeric nature, the outcome of such a process is a block copolymer, a member of a class of compounds possessing many useful properties. The most common polymer with a terminal -OH group is poly(ethylene glycol) (PEG); however, the detailed mechanism of its reaction with cerium(IV) remains underexplored. In this paper, we report our findings for this reaction based on spectrophotometric measurements and kinetic modeling. We find that both the reaction order and the net rate constant for the oxidation process depend strongly on the nature of the acidic medium used. In order to account for the experimental observations, we postulate that protonation of PEG decreases its affinity for some of the cerium(IV)-sulfate complexes formed in the system.

Straightforward synthesis route to polymersomes with simple molecules as precursors.

We demonstrate an easy-to-implement experimental emulsion polymerization protocol whose outcome is an amphiphilic copolymer capable of forming vesicles in an aqueous phase. The protocol does not require prior purification of chemicals or the exclusion of oxygen. Using n-butyl acrylate as the monomer, we employ a redox initiation system composed of cerium(IV) ions and poly(ethylene glycol) (PEG), optimizing the performance of this redox couple such that the reaction can be conducted in air. The PEG-based chain radicals produced during initiation attack the monomer molecules, resulting in an amphiphilic product, which brings the synthesis of a vesicle-forming polymer to a level where no complicated equipment is required and may have implications for origins of life research.

Realistic parameters for simple models of the Belousov-Zhabotinsky reaction.

We use experimental results to estimate the values of parameters of simple models describing the time evolution of the Belousov-Zhabotinsky reaction proceeding in droplets surrounded by hydrocarbons. The equations with fitted parameters correctly describe the period of oscillations for a large class of experimental conditions at which the reaction is performed.