Mechanism of the Ferrocyanide-Iodate-Sulfite Oscillatory Chemical Reaction

Role of Fast and Slow Inhibitors in Oscillatory Rhythm Design

In biological or abiotic systems, rhythms occur, owing to the coupling between positive and negative feedback loops in a reaction network. Using the Semenov-Whitesides oscillatory network for thioester hydrolysis as a prototype, we experimentally and theoretically analyzed the role of fast and slow inhibitors in oscillatory reaction networks. In the presence of positive feedback, a single fast inhibitor generates a time delay, resulting in two saddle-node bifurcations and bistability in a continuously stirred tank reactor. A slow inhibitor produces a node-focus bifurcation, resulting in damped oscillations. With both fast and slow inhibitors present, the node-focus bifurcation repeatedly modulates the saddle-node bifurcations, producing stable periodic oscillations. These fast and slow inhibitions result in a pair of time delays between steeply ascending and descending dynamics, which originate from the positive and negative feedbacks, respectively. This pattern can be identified in many chemical relaxation oscillators and oscillatory models, e.g., the bromate-sulfite pH oscillatory system, the Belousov-Zhabotinsky reaction, the trypsin oscillatory system, and the Boissonade-De Kepper model. This study provides a novel understanding of chemical and biochemical rhythms and suggests an approach to designing such behavior.

Provenance of life: Chemical autonomous agents surviving through associative learning

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning systems have been widely studied in cognitive science and artificial intelligence but are most commonly implemented in highly complex or carefully engineered systems, such as animal brains, artificial neural networks, DNA computing systems, and gene regulatory networks, among others. The ability to encode environmental information and use it to make simple predictions is a benchmark of biological resilience and underpins a plethora of adaptive responses in the living hierarchy, spanning prey animal species anticipating the arrival of predators to epigenetic systems in microorganisms learning environmental correlations. Given the ubiquitous and essential presence of learning behaviors in the biosphere, we aimed to explore whether simple, nonliving dissipative structures could also exhibit associative learning. Inspired by previous modeling of associative learning in chemical networks, we simulated simple systems composed of long- and short-term memory chemical species that could encode the presence or absence of temporal correlations between two external species. The ability to learn this association was implemented in Gray-Scott reaction-diffusion spots, emergent chemical patterns that exhibit self-replication and homeostasis. With the novel ability of associative learning, we demonstrate that simple chemical patterns can exhibit a broad repertoire of lifelike behavior, paving the way for in vitro studies of autonomous chemical learning systems, with potential relevance to artificial life, origins of life, and systems chemistry. The experimental realization of these learning behaviors in protocell or coacervate systems could advance a new research direction in astrobiology, since our system significantly reduces the lower bound on the required complexity for autonomous chemical learning.

The combined effect of thermal-acid hydrolysis, periodate oxidation, and iodine species removal on the properties of native tapioca (Manihot esculenta Crantz) starch

Through a four-step top-down approach, native tapioca starch (NTS) was thermally acid-hydrolyzed, periodate-oxidized with subsequent removal of iodine species (i.e., IO₄(-), IO₃(-), I(-), and I₂), and dialdehyde tapioca starch (DTS) alcohol-precipitation. The percent yield was ∼91%. Analyses confirmed the presence of aldehydic functionalities (∼71%), effectual iodine species removal (∼98%), and enhanced water-solubility (∼96.57%). Besides, the combined treatment significantly reduced the M_w (∼57.81 kDa) and ameliorated homogeneity as well as thermal stability (T_max ∼ 667.15 °C). Structural-spectral characterization also confirmed the presence of aldehydic functionality, polymorphic transition (C- to A-type), and a higher degree of crystallinity (∼91.77%), the latter further corroborated by thermal analysis. The morphological study revealed that the combined treatment reduced size (∼393.55-nm-diameter and ∼5.22-μm-length) and changed shape into rod-like crystals. DTS showed considerably and significantly low cytotoxicity to HaCaT cells in vitro at the concentrations assayed over the test period (24 h). DTS's conformation was most stable at -289 kcal/mol and -151.7 au heat formation and minimum potential energies, respectively. Overall, these results demonstrated that the combined treatment had no deleterious effects on NTS's properties, thus yielded DTS with ideal properties for multifarious uses.

Chemoplasmonic Oscillation: A Chemomechanical Energy Transducer

Chemical oscillations and waves are nonequilibrium systems that sustain a steady state with constant energy input of reactants like the life systems. Most of these oscillations are theoretically and fundamentally exploited but how to mimic the energy convolution of biological systems remains elusive. Here we develop a chemomechanical energy transducer (CoMET) based on gold nanoparticles (Au NPs) and thermo-/pH-responsive polymers, which transforms the trapped chemical energy into a tangible mechanical oscillation probed by extinction spectra. Our results show that the mechanical movement of Au NPs characterized by the chemoplasmonic oscillation follows exactly the pH oscillation and can be tuned by changing the temperature and the injection rate of the reductants. It is revealed that the energy input of the redox potentials which later converts to the collective (dis-)aggregation of Au NPs is the main driving force of the chemoplasmonic oscillation. The energy efficiency (∼34%) and force generation (∼28 pN) of this CoMET outperforms many biochemomechanical systems, which offers an alternative means to power the nanomechanics and nanomachines.

Unraveling the diverse nature of electric field induced spatial pattern formation in Gray-Scott model

We have considered a Gray-Scott kind of model chemical reaction-diffusion system that comprises ionic reactants and auto-catalysts to investigate the possibilities of mobility induced spatial pattern formation under the influence of an external electric field. Our study reveals that applying a uni-directional electric field can deform the already existing Turing patterns obtained due to diffusion driven instability, but cannot produce mobility driven instability and consequent spatial patterns in the absence of diffusion driven instability for a Gray-Scott like system. However, application of the electric field along two mutually perpendicular directions produces a mobility induced pattern in the absence of any differences in the diffusivities of the corresponding chemical reactants. Additionally, we have shown a systematic way to predict the range of absolute values of the pair of electric field intensities along two directions that will lead to spatially heterogeneous patterns in the absence of diffusion driven instability. Our study further demonstrates that the stability of the patterns formed and the nature of the patterns evolved varies with the increasing level of electric field intensities. The insights gained from this study will allow us to develop future experimental strategies to produce diverse range of stable and unique spatial patterns.