Soft Systems Research Articles

Flexible Triboelectric Sensor based on Catalyst‐Diffusion Self‐Encapsulated Conductive Liquid‐Metal‐Silicone Ink for Somatosensory Soft Robotic System

AbstractThe combination of fluidity and metallic conductivity has attracted considerable attention to liquid metal (LM), but its development remains challenging due to enormous surface tension. Here, vinyl‐terminated silicone oil and platinum catalyst are added to LM to reduce its surface tension, which develops a special type of liquid‐metal‐silicone (LMS) ink with a catalyst diffusion effect. Combined with an embedded three‐dimentional (3D) printing method, the LMS ink is printed on the support matrix, and the catalyst diffuses outward along the print path to cure the silicone around it, directly constructing self‐encapsulated conductive composites with excellent conductivity and self‐encapsulated flexible tactile sensors based on triboelectric nanogenerator (TENG). The sensor exhibits excellent sensitivity (0.308 V kPa−1), high linearity (≈0.99), and good durability (over 10 000 cycles). Furthermore, when used in flexible wearable electronics, the sensor demonstrates a good performance with an accuracy of ≈96% in classifying different human postures using a convolutional neural network. Finally, through embedded 3D printing with LMS ink and silicone ink, a somatosensory soft robotic gripper with complex cavity structures is designed and manufactured in one step, achieving the all‐in‐one integration of sensors and actuators. This study shows great application potential in flexible electronics and soft robotic systems.

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Bounded diffusing diffusivities: Brownian yet non-Gaussian diffusion

Brownian yet non-Gaussian diffusion has been recently reported in a huge number of biological and soft matter systems. Meanwhile, an archetypal theoretical model called ‘diffusing diffusivities’ is proposed to interpret it. Based on this spirit of diffusing diffusivities, we extend the original diffusing diffusivities (with the unbounded exponential distribution) to the case that the diffusivity is constructed by a bounded stochastic process, i.e., corresponding diffusivities (with certain upper and lower bounds) obeying bounded power-law distribution. We demonstrate that Brownian yet non-Gaussian diffusion can be reproduced by this bounded diffusing diffusivities, via numerical simulations and analytic derivations. Specifically, the short-time distribution of displacement, as the indicator of the Brownian yet non-Gaussian diffusion, is derived analytically by means of superstatistical approach. This short-time distribution is distinct from the Laplace distribution that appears in the original model. The long-time Gaussian displacement distribution is obtained by utilizing the subordination concept. The bounded diffusing diffusivity here may be beneficial to further understanding the diffusive process of particles in complex and inhomogeneous environments. Our work enriches the diffusing diffusivity family and sheds new light on the concept of the Brownian yet non-Gaussian diffusion under stochastic process.

Freestanding Hydrogen-Bonded Organic Framework Membrane for Efficient Wound Healing.

Hydrogen-bonded organic frameworks (HOFs) are emerging as multifunctional materials with exceptional biocompatibility, abundant active sites, and tunable porosity, which are highly beneficial for advanced wound care. However, a significant challenge involves transforming pristine HOFs powders into lightweight, ultrathin, freestanding membranes compatible with soft biological systems. Herein, the study successfully develops shape-adaptive HOF-based matrix membranes (HMMs) using a polymer-assisted liquid-air interface technique. The HMMs conform seamlessly to tissues of different sizes and shapes, effectively stopping bleeding, and provide high water-vapor permeability. Notably, both in vitro and in vivo studies with mice wound models demonstrated that these tissue-conformable HMMs significantly accelerate wound healing by modulating the inflammatory environment of the injured tissue and promoting rapid re-epithelialization. Furthermore, RNA-seq analysis and mechanistic studies revealed that HMMs effectively reduce inflammation and facilitate the tissue transition from the proliferative stage to the remodeling stage of skin development. This work not only opens up new avenues for advanced wound care materials but also establishes a foundation for hybridizing HOFs with polymers for a wide range of potential applications.

Managing Organizational Culture Integration Post-Acquisition: Lessons from PT 'X' in the Oil and Gas Sector

In the upstream oil and gas industry, mergers and acquisitions (M&A) are often used to enhance access to limited resources and improve competitiveness in the global market. However, a major challenge arises in post-acquisition organizational cultural integration, which can impede the success of this process. At PT "X," the acquisition of PT "Y" and PT "Z" highlighted a cultural gap between a company oriented towards familial values and companies with more professional and structured cultures. Although numerous studies have addressed the importance of cultural integration in M&A, few have delved deeply into cases in Indonesia's oil and gas sector. This research employs a qualitative descriptive methodology with a Soft System Methodology (SSM) approach. The findings reveal that cultural differences, particularly in communication styles and work systems, led to tensions and decreased performance. The study further indicates that the lack of cultural assessment in the early stages of the acquisition exacerbated these issues. The research implications underscore the need for a more systematic and planned cultural integration strategy to ensure post-acquisition success.

A Systematic Literature Review of Soft Skills in Information Technology Education.

This research addresses the importance of the soft skills approach, which encompasses problem-solving, collaboration, interpersonal and communication skills for higher education in the information technology (IT) field. IT graduate students continue to face difficulties in meeting the employability criteria of the global information technology sector due to mismatching capabilities, such as the discrepancy between the technical knowledge obtained in academia and the practical skills expected by employers. This systematic literature review used PRISMA guidelines for data collection. Papers were examined using the inclusion-exclusion criteria, which included concentrating on full-text studies about soft skills in higher education published in English between 2018 and 2024. The keywords used by the inclusion and exclusion criteria are soft skills, higher education, university, undergraduate, graduate, IT, information technology, software, computer science, programming, information systems and IS. The SCOPUS search engine platform found 2088 documents, and the (WOS) database obtained 1383 documents. To comprehend the significance of soft skills in the field and its effect on graduates' employability, 69 papers were carefully examined. The rapid change following Industrial Revolution 4.0 has transformed the working environment, challenging new IT graduates to be competent in the working environment. This study highlights the importance of soft skills and self-awareness in university education, revealing that current curricula must adapt to the rapidly changing job market, especially post Industry 4.0. The literature review indicates that despite high technical competence, graduates lack essential soft skills like communication, teamwork, and problem-solving, creating a gap between new graduates and industry expectations. Hopefully, this study's results will contribute to understanding the functionality and necessity of soft skills in the behavioral sciences literature. To bridge the skills gap between industry demands and technical proficiency, academic institutions should incorporate creative teaching approaches prioritizing soft skills like problem-solving, teamwork, and communication. Universities, recent graduates, and companies must work together to modify courses to meet the needs of a job market that is changing quickly.

Drop-on-Demand 3D Printing of Programable Magnetic Composites for Soft Robotics

Soft robotics have become increasingly popular as a versatile alternative to traditional robotics. Magnetic composite materials, which respond to external magnetic fields, have attracted significant interest in this field due to their programmable two-way actuation and shape-morphing capabilities. Additive manufacturing (AM), also known as 3D printing, allows for the incorporation of different functional composite materials to create active components for soft robotic systems. However, current AM methods have limitations, especially when it comes to printing smart composite materials with high functional material content. This is a key requirement for enhancing responsiveness to external stimuli. Commonly used AM methods for smart magnetic composites, such as direct ink writing (DIW), confront challenges in achieving discontinuous printing, and enabling multi-material control at the voxel level, while some AM techniques are not suitable for producing composite materials. To address these limitations, we employed high-viscosity drop-on-demand (DoD) jetting and developed versatile programmable magnetic composites filled with micron-sized hard magnetic particles. This method bridges the gap between conventional ink-jetting and DIW, which require inks with viscosities at opposite ends of the spectrum. This high-viscosity DoD jetting enables continuous, discontinuous, and non-contact printing, making it a versatile and effective method for printing functional magnetic composites even with micron-sized fillers. Furthermore, we demonstrated stable magnetic domain programming and two-way shape-morphing actuations of printed structures for soft robotics. In summary, our work highlights high-viscosity DoD jetting as a promising method for printing functional magnetic composites and other similar materials for a wide range of applications.

Integrated Approach To Material Parameterization And Inflation Control Of Hyperelastic Membrane For Artificial Left Ventricle Development

Abstract In transitioning from conventional mechanical to biomedical applications, the choice of materials becomes critical. While hard materials like steel offer durability, their compatibility with biological systems, particularly blood, is limited due to the risk of cell damage. To address this, our research focuses on soft membrane-based systems to minimize haemolysis and optimize blood flow. By prioritizing biocompatibility and gentle interactions, our approach aims to enhance the safety and effectiveness of biomedical devices. Through innovative solutions in material selection and system design, we aim to advance cardiovascular medicine and contribute to the broader field of biomedical engineering.

Fracture-driven power amplification in a hydrogel launcher.

Robotic tasks that require robust propulsion abilities such as jumping, ejecting or catapulting require power-amplification strategies where kinetic energy is generated from pre-stored energy. Here we report an engineered accumulated strain energy-fracture power-amplification method that is inspired by the pressurized fluidic squirting mechanism of Ecballium elaterium (squirting cucumber plants). We realize a light-driven hydrogel launcher that harnesses fast liquid vapourization triggered by the photothermal response of an embedded graphene suspension. This vapourization leads to appreciable elastic energy storage within the surrounding hydrogel network, followed by rapid elastic energy release within 0.3 ms. These soft hydrogel robots achieve controlled launching at high velocity with a predictable trajectory. The accumulated strain energy-fracture method was used to create an artificial squirting cucumber that disperses artificial seeds over metres, which can further achieve smart seeding through an integrated radio-frequency identification chip. This power-amplification strategy provides a basis for propulsive motion to advance the capabilities of miniaturized soft robotic systems.

Revolutionizing efficiency: The path to sustainable oilfield services in Indonesia's energy landscape – The case of Pertamina drilling services

PurposeThis study explores strategic and technological approaches for sustainable oil production in Indonesia, focusing on policy frameworks, eco-innovation, and supply chain resilience to align with national and global standards. It also analyzes the organizational transition of Pertamina Drilling Service Indonesia (PDSI) to better navigate industry challenges with the outcome of a framework sustainable practices. Design/methodology/approachUtilizing Soft Systems Methodology (SSM) to analyze primary qualitative data, the research examines PDSI's adaptation to depict the current business environment and challenges both domestically and globally, addressing industry demands. It focuses on operational objectives, strategic asset management, regulatory compliance, and market positioning. SSM offers a comprehensive framework for understanding and modeling the organizational changes within PDSI. FindingsThe transition to a functional organizational structure has led to enhanced operational efficiency, clearer role definition, and improved employee retention and development strategies at PDSI. The study also highlights the industry's challenges, including high risks, capital intensity, environmental concerns, and fluctuating oil prices, and how PDSI is strategically responding to these challenges. Research limitations/implicationsThe study faces limitations due to reliance on specific data sources and the inherent complexity of the oil and gas sector. However, it offers valuable insights into the dynamics of organizational change in a high-risk industry. Practical implicationsThe findings provide a roadmap for PDSI in addressing operational efficiency, human resource management, and organizational agility. This includes aligning business expansion with environmental sustainability and enhancing competitiveness through technological innovation. Social implicationsBy focusing on strategic employee development and retention, the study underscores the importance of human capital in the oil and gas industry, impacting social aspects of business operations. Originality/valueThis research contributes to the understanding of organizational transitions in the oil and gas sector. It offers a unique perspective on how PDSI can achieve sustainable growth and industry leadership through a functional organizational structure, underlined by a comprehensive framework emphasizing efficiency, adaptability, and continuous improvement.

Assessing anthropogenic influences on the water quality of Gomati River using an innovative weighted fuzzy soft set based water pollution rating system

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health.

Large-torque and fast-response electrothermal-motivated liquid crystal elastomer twisted yarn actuators

Analogous to biological muscles, twisted yarns with the capacity to output torsional torque and reversible rotation are of significant value in the development of soft robotic systems. However, how to achieve large torque while efficiently integrating electrical control elements to the soft actuators remains scientific and engineering challenges. Here, by concentric twisting a flexible silver-plated polyamide strand (SPS) with multi-layer liquid crystal elastomer fibers (LCEFs), a twisted yarn actuator with the skin-core structure that exhibits outstanding rotational motion under the electrical current control is fabricated. It has been proved that this multi-layered twisting structure efficiently improves the actuating performance. The yarn actuator can produce a peak torque of 33.4 N m kg−1 which outperforms reported commercial rotating motors and a specific power density of 21.2 W kg−1 that is comparable to the human skeletal muscle. As conceptual applications, electrical soft rotors and a novel flexible lifter have been successfully constructed based on the twisted yarn actuator, demonstrating precise torque responsiveness and controllability. Furthermore, a smart crawling fabric embedded with the twisted yarn actuators has been weaving fabricated which can crawl forward continuously and achieve the crawling stroke of 19.9 mm in 55 s and a maximum speed of 1.1 mm s−1. The research provides insights for designing soft robots employing fabric structures.

A novel approach to thermal insulation modelling in soft and medium vacuum insulation systems

The accuracy of vacuum-dependent models for predicting thermal performance of Multi-Layer Insulation (MLI) and other layered insulation systems is critical for the development of novel solutions in the aerospace and energy sectors, particularly long distance superconductors and cryogenic transfer lines. This paper presents a review of the current state of the art in cryogenic vacuum insulation systems and their associated modelling techniques and test methods. Current modelling techniques, namely the Lockheed and McIntosh MLI models, are compared to cryogenic, boil-off calorimeter test data for 3 types of MLI from the current literature. Both current models provide acceptable accuracy at high vacuum pressures but deviate from the test data when gas conduction becomes the dominant heat transfer mechanism (Kn≤1). Neither of the current models follow the characteristic S-curve observed by researchers during insulation tests. This paper presents the introduction of a novel modelling approach for layered insulation systems though changes to the current state of the art, specifically at soft (pvac≤7.5×105 mTorr) and medium vacuum (pvac≤7.5×102 mTorr) pressures, by substituting the gas conduction term in both equations with alternative terms based on the system Knudsen number (Kn) and molecule mean free path (l). This results in a stronger pressure dependence across the vacuum regime. Both modified models exhibited the characteristic S-curve with significantly reduced errors over the entire range.

Automated data-driven discovery of material models based on symbolic regression: A case study on the human brain cortex

We introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data. Leveraging symbolic regression, our approach generates elegant hyperelastic models that achieve accurate data fitting with parsimonious mathematic formulas, while strictly adhering to hyperelasticity constraints such as polyconvexity/ellipticity. Our investigation spans three distinct hyperelastic models—invariant-based, principal stretch-based, and normal strain-based—and highlights the versatility of symbolic regression. We validate our new approach using synthetic data from five classic hyperelastic models and experimental data from the human brain cortex to demonstrate algorithmic efficacy. Our results suggest that our symbolic regression algorithms robustly discover accurate models with succinct mathematic expressions in invariant-based, stretch-based, and strain-based scenarios. Strikingly, the strain-based model exhibits superior accuracy, while both stretch-based and strain-based models effectively capture the nonlinearity and tension-compression asymmetry inherent to the human brain tissue. Polyconvexity/ellipticity assessment affirm the rigorous adherence to convexity requirements both within and beyond the training regime. However, the stretch-based models raise concerns regarding potential convexity loss under large deformations. The evaluation of predictive capabilities demonstrates remarkable interpolation capabilities for all three models and acceptable extrapolation performance for stretch-based and strain-based models. Finally, robustness tests on noise-embedded data underscore the reliability of our symbolic regression algorithms. Our study confirms the applicability and accuracy of symbolic regression in the automated discovery of isotropic hyperelastic models for the human brain and gives rise to a wide variety of applications in other soft matter systems. Statement of significanceOur research introduces a pioneering data-driven framework that revolutionizes the automated identification of hyperelastic constitutive models, particularly in the context of soft matter systems such as the human brain. By harnessing the power of symbolic regression, we have unlocked the ability to distill intricate physical phenomena into elegant and interpretable mathematical expressions. Our approach not only ensures accurate fitting to sparse data but also upholds crucial hyperelasticity constraints, including polyconvexity, essential for maintaining physical relevance.

First results of a newly-built hard-X-ray/soft-Gamma spectrometer imaging system: on the aspect of plasma disruptions

Abstract A new Hard X-ray and soft gamma-ray spectrometer imaging System (HXS) has been built for the 2-Dimensional measurements of plasma emitted photons on the Experimental Advanced Superconducting Tokamak (EAST). The system uses a 2-D Cadmium Zinc Telluride (CZT) detector and an integrated electronics, which is as a whole shielded by a tungsten box with a pinhole and is tangential to the toroidal field. Three classes of typical energy spectra have been summarized in differently experimental scenarios over the past campaigns. After performing tomography calculations, the local emissivity contours have been obtained in different energy ranges which obviously show the asymmetry on the plasma cross section. The spatial perturbation structure is similar to the magnetohydrodynamics (MHD) modes with low mode numbers. In particular, the runaway island found by an infrared camera [R. Jaspers et al, Phys. Rev. Lett. 72, 4093 (1994)] is also measured by the HXS. There exists a reversal population on the energy spectra in both slide-away and strong neutral beam injection (NBI) shots. It is consistently observed that the count rate is increased in the low energy range before the plasma disruptions. Calculations in phase space indicate that the accelerated momentum flux can be deflected back to the low energy region by the large pitch-angle scattering. In the post-disruption phase the plasma current is not replaced by runaway electrons due to tearing modes or transiently bursting instabilities. This paper constructs the basics for the proper use of HXS for Hard X-ray and Soft Gamma-ray measurements in future investigations of plasma disruptions.

Coordinated Planning of Soft Open Points and Energy Storage Systems to Enhance Flexibility of Distribution Networks

With the large-scale penetration of distributed generation (DG), the volatility problems of active distribution networks (ADNs) have become more prominent, which can no longer be met by traditional regulation means and need to be regulated by introducing flexible resources. Soft open points (SOP) and energy storage systems (ESS) can regulate the tidal currents on spatial and temporal scales, respectively, to improve the flexibility of ADN. To this end, in-depth consideration of DG admission is given to establish flexibility assessment indicators from the power side of ADN. The conditional deep convolution generative adversarial network (C-DCGAN) is used to generate the output scenario of DG. On this basis, the SOP and ESS two-layer planning models, which take account of the potential for improvement in the flexibility of ADN, are constructed. Among them, the upper layer is the site selection and volume determination layer, which considers the economy of the system with the optimization objective of minimizing the annual integrated cost; the lower layer is the operation optimization layer, which considers the flexibility of the system and takes the highest average daily flexibility level as the optimization objective. The planning model is solved using genetic algorithm-particle swarm optimization (GA-PSO) and second-order cone programming (SOCP). The case analysis verifies the rationality and effectiveness of the planning model.

The Art of Soft Systems Inquiry: Retracing the Footsteps of Churchman and Checkland

AbstractIn this paper I reflect upon soft-systems inquiry. To do this I revisit Churchman’s discourse on the philosophical ideas underpinning inquiring systems and then discuss the practical application of soft inquiry pioneered by Checkland. This body of work suggested ontological models of organisational behavior were inappropriate and only by adopting an epistemological perspective would provide the basis for understanding complex systems that characterise human society. These ideas motivated a new way of addressing complexity. This work created a difficulty for action research since to be faithful to this discovery meant that any method of inquiry should be one that could cope with such complexity. One outcome was Soft Systems Methodology (SSM). Paradoxically the success of SSM has restrained further discussion of these ideas. The purpose of this paper is to revisit the ideas of Churchman and Checkland as a means of returning to first principles. In this paper I reassess their ideas and those of Husserl and Gadamer and suggest there is more to be gained from this rich seam of systems thinking.

Geometrical incompatibility regulated pattern selection and morphological evolution in growing spherical soft tissues

Surface morphological patterns are widely observed in natural systems, which are closely correlated to vital biological functions and inspire surface morphology designs in soft matter systems. Geometrical incompatibility widely exists in biological tissues across different length scales and plays an important role in growth-induced pattern selection and morphological evolution of soft tissues. However, the underlying physical mechanism of growth-induced pattern formation and post-buckling evolution in geometrically incompatible spherical soft tissues remain elusive. Here, the effect of geometrical incompatibility on the growth-induced pattern selection and post-buckling evolution are investigated through swelling experiment, theoretical analysis and numerical simulation. The results show that not only the instability pattern but also the instability threshold can be regulated by manipulating geometric incompatibility. Notably, when the geometrical incompatibility parameter exceeds a critical value, spontaneous instability is observed before growth. With continuous growth, the core–shell soft sphere buckles into a periodic buckyball pattern and evolves toward a bean-shaped pattern, and then undergoes a wrinkle-to-fold transition into a labyrinth topography. Our results demonstrate, both experimentally and theoretically, that geometrical incompatibility can guide the growth-induced pattern formation and morphological evolution effectively. This study not only enhances our understanding of the growth-induced pattern selection and morphological evolution in spherical soft tissues, but also provides an inspiring insight for the fabrication of morphological patterns on curved surfaces.

Anomalous relaxation of coarsening foams with viscoelastic continuous phases.

We investigate the ultraslow structural relaxation of ageing foams with rheologically tunable continuous phases. We probe the bubble dynamics associated with pressure-driven foam coarsening using differential dynamic microscopy, which allows characterising the sample dynamics in reciprocal space with imaging experiments. Similar to other out-of-equilibrium jammed soft systems, these foams exhibit compressed exponential relaxations, with a ballistic-like linear dependency of the relaxation rate on the scattering wavevector. By tuning the rheology of the continuous phase, we observe changes in the relaxation shape, where stiffer matrices yield larger compression exponents. Our results corroborate recent real-space observations obtained using bubble tracking, providing a comprehensive overview of structural relaxation in these complex systems, both in direct and reciprocal space.

Ion‐Specific Interactions Engender Dynamic and Tailorable Properties in Biomimetic Cationic Polyelectrolytes

AbstractBiomaterials such as spider silk and mussel byssi are fabricated by the dynamic manipulation of intra‐ and intermolecular biopolymer interactions. Organisms modulate solution parameters, such as pH and ion co‐solute concentration, to effect these processes. These biofabrication schemes provide a conceptual framework to develop new dynamic and responsive abiotic soft material systems. Towards these ends, the chemical diversity of readily available ionic compounds offers a broad palette to manipulate the physicochemical properties of polyelectrolytes via ion‐specific interactions. In this study, we show for the first time that the ion‐specific interactions of biomimetic polyelectrolytes engenders a variety of phase separation behaviors, creating dynamic thermal‐ and ion‐responsive soft matter that exhibits a spectrum of physical properties, spanning viscous fluids to viscoelastic and viscoplastic solids. These ion‐dependent characteristics are further rendered general by the merger of lysine and phenylalanine into a single, amphiphilic vinyl monomer. The unprecedented breadth, precision, and dynamicity in the reported ion‐dependent phase behaviors thus introduce a broad array of opportunities for the future development of responsive soft matter; properties that are poised to drive developments in critical areas such as chemical sensing, soft robotics, and additive manufacturing.