Similitude for buildings conditioned entirely by ambient energy

Decarbonizing and fortifying electric vehicle charging infrastructure through ambient energy harvesting

Organic thermoelectric generators hold great promise for powering wearable microelectronics, yet their performance is fundamentally constrained by the trade-off between electrical conductivity (σ) and the Seebeck coefficient (S). Herein, we develop a microfluidic spinning platform to fabricate PEDOT:PSS-based nonwoven fabrics with precisely engineered micro-/nanoscale physical and electronic structures, substantially enhancing thermoelectric performance. The intense shear field and in situ coagulation within microfluidic microchannels, synergized with H2SO4 treatment, promotes axial orientation and coil-to-linear conformational transition of PEDOT chains, achieving multiscale structural ordering for highly efficient charge transport in the resulting fibers. A subsequent controlled NaOH‑mediated dedoping process finely tunes the Fermi level and modulates energy‑dependent scattering, yielding a final σ of 2038 Scm-1 and an S of 29.7μVK-1. Such integrated modulation enables effective optimization of the classic σ-S trade-off, ultimately yielding a power factor of 179.8μWm-1K-2. Furthermore, by integrating the fabric with an electrospun PVDF-HFP radiative-cooling layer, we demonstrate a radiation-modulated fabric device capable of maintaining an in-plane temperature gradient (ΔT ≈ 20K) under natural sunlight and efficiently harvesting ambient solar-thermal energy. This study provides a versatile route for the fabrication of all-organic, flexible fabrics with high-performance thermoelectric functionality for wearable energy applications.

Triboelectric nanogenerators (TENGs) serve as multifunctional platforms for low-frequency ambient energy scavenging and self-powered sensing but often suffer from poor charge retention and mechanical durability. Herein, ternary composites (TCs) of MoS2 nanosheets integrated within a PVDF-TrFE/EMIM-TFSI (PMSI) matrix were developed to enhance charge retention by inducing a highly crystalline β-phase (∼85%). The EMIM-TFSI ionic liquid (IL) triggers electroactive β-phase nucleation in pristine PVDF-TrFE (∼61%) by enhancing polymer chain mobility, crystallinity, and dielectric properties. XPS binding-energy shifts reveal strong electrostatic interactions between MoS2 and PVDF-TrFE, inducing partial positive and negative charges on the Mo and F sites, respectively. Subsequently, the blue-shifted FTIR spectra suggest robust H-bonding between PVDF-TrFE (-CH2 groups) and TFSI- (-SO2, oxygen atoms), facilitating effective ionic-electronic coupling. A vertical contact-separation TENG with optimized PMSI@PDMS frictional layers delivers a VOC of ∼600 V, an ISC of ∼5.1 μA, and a power density of ∼4.8 W/m2. The TENG sustains stable output over 50,000 cycles, demonstrating robust durability for reliable energy harvesting. The TENG powers 120 LEDs, charges a capacitor to 8.3 V in 6 s, and operates as a self-powered interface for real-time digit recognition (0-9) and interactive gaming controllers (Simon Says). Driven by a targeted β-phase nucleation strategy, the PMSI-based TENG offers a scalable approach to developing high-output and reliable energy harvesters for expanding IoT ecosystems.

Responsive materials deform under external stimuli but often move unidirectionally, limiting their application in energy conversion and harvesting. Astonishingly, membranes composed of hygroscopic 2D nanoflakes, such as titanium carbide (MXene) and graphene oxide (GO), exhibit spontaneous and sustained oscillations when exposed to a steady flow of water vapor. While this behavior implies significant potential of the stacked nanoflake assemblies (SNA) for propulsion and electricity generation, the underlying mechanism of this autonomous reciprocating motion remains poorly understood, hindering its practical applications. Herein, we address this challenge by identifying the key factors governing the dynamics of moisture-driven oscillations, which finally fuse into three characteristic dimensionless parameters of the system. These parameters allow for the controllable tuning of the oscillation frequency, amplitude, and static bending angle. Furthermore, our study reveals that the oscillation is sustained by a negative feedback loop between moisture transport and mechanical motion. Guided by these insights, we achieve the rational design and optimization of SNA membranes for practical applications in continuous propulsion and energy harvesting from a weak ambient humidity gradient.

High-efficiency evaporation and concentration with exceptional salt resistance via a chitosan annular hydrogel evaporator.

Energy consumption is a critical concern for Internet of Things (IoT) platforms lacking abundant resources, particularly for swarm robotic systems that rely on numerous devices operating collaboratively over extended periods. This study presents a comprehensive design strategy for improving processing and communication to enhance system efficiency and reduce energy consumption. We incorporate energy harvesting (photovoltaic and RF), dynamic power management, and energy-efficient communication protocols (e.g., duty cycle, power control, data compression) into two complementary platforms built for swarm robotics: MCU-based nodes (TI MSP430 with LoRa transceiver), which serve as the experimental prototype for validating energy-aware communication, compression, and scheduling mechanisms; edge platforms (Jetson Nano and TX2), which are used for high-level power profiling and system-level evaluation, particularly for computation intensive workloads and comparative analysis. Our technique involves analyzing the device’s energy usage and harvesting processes, developing efficient communication protocols, and validating the system through simulations and hardware prototypes. Experimental results under outdoor and indoor conditions show that the device maintains an energy neutrality ratio well above unity, even with limited ambient energy. Key findings include significant reductions in energy per bit transmitted and reliable long-term operation. These insights pave the way for deploying swarms of autonomous IoT-based robots with minimal maintenance and maximal longevity.

Currently, 3D interfacial evaporators have attracted significant attention due to their superior evaporation performance. However, the shape of carbon-based 3D evaporators is often constrained by the original form of biomass materials, which limits their practical applications. Herein, we report a novel strategy for fabricating 3D solar-driven interfacial evaporators with arbitrary shapes (hemisphere, cone, flake, and Z-type) by integrating carbon powder derived from corn shuck (CS) with binders. The silver-doped corn-based carbon (Ag-CCS) material exhibits exceptional photothermal conversion efficiency, achieving surface temperatures of 153.3°C (dry) and 95.7°C (wet) under 1 sun illumination. Among the 3D evaporators, the Z-type design demonstrates the highest evaporation rate of 4.42 kg·m-2 h-1, attributed to its porous structure, hydrophilicity, low evaporation enthalpy of adsorbed water (1286.13 J g-1), and efficient ambient energy absorption and thermal management. Outdoor experiments further validate the Z-type evaporator's superior performance, with a maximum daily water production of 25.1kg m-2 and automatic salt-cleaning capability over 20 days. This work paves the way for the scalable fabrication of 3D carbon-based evaporators, offering a viable solution for seawater desalination.

This work focuses on the development and characterization of flexible triboelectric nanogenerators (TENGs) using low-cost polymers: high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyurethane (PU), and poly (methyl methacrylate) (PMMA). The materials were synthesized into thin films by a solvent casting method. Scanning Electron Microscopy (SEM) showed different surface morphologies, like ductile and brittle natures. Mechanical testing indicated PMMA has higher tensile strength (35 MPa) and better stiffness properties than TPU, which has better flexibility and elastic properties. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the presence of functional groups (C = O and C-O-C) in PMMA and TPU, which are important for charge transfer. X-ray diffraction showed that HDPE had the highest crystalline content (70%) and a large crystallite size (30.17 Å), which explained its effective tribo-negative material. PMMA and TPU had low crystalline materials (<1%), which enhanced any tribo-positive properties. Differential scanning calorimetry (DSC) suggested that a blend of TPU and PMMA had a melting temperature (Tm) of 175.8 °C and even had better thermal stability than TPU or PMMA polymers. A demonstration of the dielectric study illustrates that PMMA had the largest dielectric constant at high frequencies. A 3D-printed model was fabricated for testing the tribo-model (contact-separation), which produced a peak-to-peak voltage output of 155 V in a TPU/PMMA-HDPE TENG. The results show that intentionally combining the polymers and their various mechanical, thermal, and electrical characteristics was able to achieve a TENG that could be efficient, scalable, and low-cost.

Photocatalytic hydrophobic self-cleaning coatings are a promising means for protecting buildings from environmental damages; however, it still remains a big challenge to develop redox-active catalysts that green-efficiently decompose persistent organic pollutants, directly harnessing natural energy such as the kinetic energy of rainfall and solar energy without the need for high power consumption, while maintaining robust performance under all-weather conditions. Herein, we constructed a functional composite coating with piezo-photocatalytic and hydrophobic self-cleaning properties. Under simulated all-weather conditions, it exhibited excellent pollutant removal efficiency, achieving approximately 94.23% for RhB in 80 min and 94.20% and 90.56% for TC and OTC, respectively, within 5 h. Also, it demonstrated that under flow impact alone, the stress energy variations (maximum value of 3.968 × 10-5 per droplet), resulting from different heights and incident angles, significantly affected pollutant removal efficiency. The stress induced by water flow activated the piezoelectric response. The maximum droplet spreading radius reached up to 3.5 times the initial, enlarging the solid-liquid contact area by roughly 12.25 times, which greatly promoted mass transfer and oxygen access and amplified reactive oxygen species (ROS) generation as 22.55 μM •OH and 23.04 μM •O2- under water-flow conditions. In addition, it also exhibited outstanding mechanical durability and strong antibacterial activity of 95.79%. Atomic force microscopy (AFM) revealed a high fitted modulus of 56.7 GPa and an ultralow adhesion force of 91.1 nN, corroborating the coating's stiffness and hydrophobic self-cleaning capability. These properties stemmed from the microrough, channel-like architecture of the piezo-photocatalytic hydrophobic coating (PHSC) framework, which enhances contact stiffness and reduces fouling adhesion. Overall, this work presented a scalable and energy-efficient strategy for maintaining outdoor surface cleanliness and hygiene by passively utilizing ambient energy at the solid-liquid-gas interface.

The intermittent nature of ambient mechanical energy sources such as wind, vibration, and human motion poses a significant challenge for the stable operation of triboelectric nanogenerators (TENGs). To address this issue, this study presents a long-lasting operable triboelectric nanogenerator (LONG), a novel TENG system that utilizes an escapement-based mechanical regulation strategy to convert irregular and low-frequency mechanical inputs into stable and continuous electrical outputs. LONG integrates a rotational TENG system and an escapement mechanism to achieve a controlled and unidirectional rotational motion, thereby prolonging the energy-harvesting duration while minimizing the energy losses due to torque fluctuation. A freestanding-mode TENG, enhanced with electret materials fabricated via corona discharge are adopted to ensure efficient charge induction under low-torque conditions. Parametric studies reveal that key design variables, including spring tension, gear ratio, electret surface potential, and applied mass, significantly influence the output stability and voltage amplitude. The optimized LONG system demonstrated a peak voltage of 300 V, a current of 19 μA, and continuous operation exceeding 3 min from a single winding. Furthermore, its ability to power 125 LEDs, a thermo-hygrometer, and a dust collecting system validates its practical applicability in self-powered systems. Thus, this study introduces a robust design framework for mechanical regulation in TENGs, offering a new pathway for stable energy harvesting from irregular mechanical sources for wearable, environmental, and infrastructure-monitoring applications.

The pursuit of sustainable green energy has intensified the demand for self-powered materials, which are capable of efficiently converting ambient mechanical energy into electrical signals. Herein, polyvinylidene fluoride (PVDF) fabricated by electro-assisted fused deposition modeling (E-FDM) 3D printing is a firm candidate due to the exceptional electroactivity and excellent flexibility. Unlike conventional fabrication methods struggling with dimension control, a physics-informed neural network (PINN) framework was developed for the first time to predict and control PVDF fibers. PINN successfully integrates physical interpretability with data-driven flexibility, achieving high precision predictions (R2 = 0.97, MSE = 0.0016, MAE = 0.034) with improved morphological control. In particular, the results showed that PVDF 50 μm fibers exhibited more than 3-fold improvement in piezoelectric β-phase content when compared to the 3D-printed counterparts without the external electric field. Correspondingly, the PVDF sensor achieved an excellent sensitivity of 130.9 mV/kPa and a fast response time of 35 ms. The PINN-based approach offers a scalable manufacturing strategy for the fabrication of PVDF sensors with tailored electromechanical properties, paving the way for flexible electronics and intelligent sensing systems.

There is a growing demand for employing Internet of Things (IoT) devices and sensors to optimize efficiency and ensure more resilient production systems. However, the widespread use of batteries to power those devices raises concerns over charges running out and environmental concerns. Hence, energy harvesting devices have emerged as a sustainable alternative by converting ambient energy sources (e.g. light, heat, or vibration) into usable power. Among these, biophotovoltaics (BPVs) utilize photosynthetic microorganisms to generate electricity, offering advantages such as environmental compatibility, self-repair, and nighttime operation, representing an ideal device for continuous low-power applications. To address the low power output of BPVs, this study introduces a novel approach using latex-based living biocomposites technology incorporating the green microalga Chlorella vulgaris TISTR 8580 immobilized on ITO-PET electrodes using an acrylic latex binder to facilitate transparent, durable film that supports photosynthesis, cell adhesiveness and mass transfer. The fabricated electrodes have been proved to generate biological current. With the developed electrodes, the BPV performance achieved a maximum power density of 0.29 W m −2 , which was obtained from a 20:100 binder-to-cell volume ratio, almost five times outperforming the binder-free condition. This ratio balances a sufficient binder concentration to ensure cell retention without exceeding levels that hinder mass transport, biological activity, or introduce cytotoxicity. The integration of living biocomposites technology offers a promising improvement to conventional BPVs, intensifying device performance. and demonstrating practical sensor powering applications. • A latex-based living biocomposites electrode was developed for light energy harvesting biophotovoltaics (BPVs) device. • An acrylic polymer enhanced microbial adhesion while maintaining cell viability. • The acrylic polymer binder to Chlorella vulgaris cells volume ratio of 20:100 enhanced the power output of BPVs device. • The latex-based living biocomposites technology offers an additional efficient improvement for advancing BPVs technology.

The water hammer effect, an energy dissipation phenomenon in fluid pipelines triggered by transient fluid impact, possesses both destructive potential and exploitable energy. Addressing traditional energy harvesters’ narrow bandwidth and low spatial efficiency, this article designs a multisource heterogeneous ambient energy harvester. This device achieves efficient recovery of water hammer impact energy through the synergistic operation of piezoelectric, electromagnetic, and triboelectric mechanisms. The harvester employs a ring-symmetrical three-dimensional structure. By utilizing multiple sets of piezoelectric cantilever beams and a free inertia vibrator, combined with a modal decoupling strategy, it converts axial impact into radial multimodal vibrations, synchronously capturing tri-axial energy. Experimental validation confirmed the power density metrics and electromechanical conversion efficiency under multisource synergy. The piezoelectric volume energy density reached 36.65 mW/cm<inline-formula content-type="math/tex" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX" version="MathJax">$^3$</tex-math> </inline-formula>, while the electromagnetic and triboelectric surface power densities were 0.56 and 0.12 mW/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, respectively. The collaborative conversion efficiency has reached approximately 79.29%. This device can provide self-power for smart pipeline monitoring systems while concurrently reducing the risk of water hammer-induced damage, demonstrating significant engineering application value.

The last years have seen the increasing development of innovative railway pantographs based on smart materials and equipped with monitoring features based on wireless sensor nodes. In this scenario, one of the most important challenges is the power supply of pantograph sensors. Energy harvesting systems have been proposed for powering monitoring sensors in a variety of applications, including railway pantographs. These systems convert ambient energy sources into electrical energy. The use of energy harvesting systems coupled with storage devices, such as rechargeable batteries or supercapacitors, can be a very promising solution for making the sensors self-powered, thus avoiding the drawbacks associated with supplying from the main grid or disposable batteries. In this paper, the operating principles of the main technologies used for energy harvesting in railway pantographs are described in detail, together with some examples of laboratory prototypes and commercial devices. The proposed analysis focuses on the perspectives and challenges of various energy harvesting technologies and can help select the most suitable technology for the development of innovative sensorized pantographs.

Droplet triboelectric nanogenerators (D-TENGs) hold significant promise for ambient energy harvesting, yet their insufficient output limits practical application. This study proposes an interfacial engineering strategy employing fluorosilane-modified SiO2 (F-SiO2) to enhance the performance of conventional D-TENG based on polytetrafluoroethylene. The constructed F-SiO2-coated droplet TENG (FSD-TENG) exhibits superior hydrophobicity compared to conventional D-TENG, achieving a contact angle of 152.3°. The FSD-TENG achieves an open-circuit voltage of 201.7 V, which is 5.8 times higher than that of untreated devices, and delivers an output power of 2.1 mW, representing a 7-fold improvement over conventional D-TENG. Its practical potential is demonstrated by illuminating 300 LEDs upon a single droplet impact. Furthermore, the FSD-TENG's output characteristics exhibit a strong correlation with droplet pH. Leveraging multi-feature fusion of voltage and current signals, we developed a raindrop pH monitoring system. This system accurately identifies raindrop pH via FSD-TENG's output, with a neural network achieving 95% identification accuracy within pH 3-7 after 20 training epochs. Consequently, the FSD-TENG-based monitoring system not only provides novel data support for digital twin technology but also pioneers innovative pathways for environmental monitoring, showcasing a complete technological chain from fundamental research to practical implementation.

Wind energy harvesting based on triboelectric nanogenerators (TENGs) is a promising solution for powering distributed Internet of Things (IoT) nodes, yet its practical efficiency and stability are often hindered by the fluctuating and unpredictable nature of wind. Here, we propose a self-regulating TENG (SR-TENG) that leverages the synergistic effects of centrifugal, elastic, and frictional forces to automatically switch between non-contact and contact modes based on wind speed. This configuration achieves an ultra-low start-up wind speed of 0.86 m/s, ensures sustainable high-performance output across a broad wind speed range, and exhibits excellent durability with no observable performance degradation during 23,000 s of continuous operation at 375 rpm. Systematic structural optimization enables the SR-TENG to reach a peak open-circuit voltage of 140 V, a short-circuit current of 12.5 μA, and a transferred charge of 300 nC at 375 rpm. When integrated with a customized power management circuit, the system delivers a 30.39-fold increase in effective output power at a 1 MΩ load and a 4-fold faster charging rate for a 10 μF capacitor. For practical validation, the harvested ambient wind energy successfully powers a wireless temperature-humidity sensor for real-time cloud data transmission. These results highlight that the SR-TENG holds great potential for advanced wind energy harvesting and self-powered sensing applications in distributed IoT systems.

The rapid advancement of Tiny Machine Learning (TinyML) is enabling intelligent inference on highly resource- and energy-constrained Internet of Things (IoT) devices. However, sustaining computation over long operational lifetimes remains a major barrier to scalable, low-maintenance deployments, as battery replacement cost and environmental burden often dominate total cost of ownership. To address this challenge, we present a co-designed, hybrid green energy-harvesting TinyML framework that integrates photovoltaic (PV) and ambient radio-frequency (RF) energy sources with supercapacitor buffering and reinforcement-learning (RL) based inference scheduling for indoor air-quality monitoring applications. We first review indoor ambient energy sources and motivate PV + RF hybridisation to mitigate intermittency and improve availability. A dual-input powermanagement architecture with maximum power point tracking (MPPT), impedance-aware conditioning, and supercapacitor-based energy buffering is developed to support energy-neutral operation, with buffer voltage serving as an observable proxy for the node energy state. We then profile three representative TinyML models—Decision Tree, Random Forest, and a Tiny Neural Network— demonstrating the accuracy–energy trade-off that constrains harvesting-powered edge intelligence: 85.7%, 90.9%, and 92.2% accuracy at 5 mJ, 7 mJ, and 9 mJ per inference, respectively. Inference scheduling is formulated as a Markov decision process and solved using tabular Q-learning, which adapts inference execution based on capacitor voltage and historical harvested-power trends to balance predictive utility against brown-out risk. MATLAB simulations over a 12-hour stochastic indoor illumination cycle show that a 25cm2 PV tile under 500 lx lighting, supplemented with ambient RF at − 10dBm, delivers a mean harvested power of approximately 1.2mW. The proposed RL policy maintains the supercapacitor within safe operating limits, achieves near-continuous availability, and attains more than 95% probability of energy-neutral operation, corroborated via Monte Carlo analysis under environmental perturbations. Overall, the results establish a scalable methodology for self-sustaining, battery-less intelligent IoT nodes capable of continuous operation, advancing sustainable and autonomous environmental sensing networks.

Perception and action are unified by invariants in energy arrays that directly specify (are information about) lawful agent–environment relations or affordances. Invariants are defined by symmetry: relational structures that persist across transformations. Building on this ecological foundation that Turvey championed, we propose a framework for perception–action that uses continuous symmetry groups to formalize how energy arrays—optic, acoustic, and haptic—are structured via action. Using the special Euclidean group SE ( 3 ) as a working exemplar, we demonstrate how the six generators of this group directly map invariants (symmetries) in ambient energy arrays to the motor–generator subspaces that actualize affordances. We do this across canonical cases of locomotion, catching, climbing, and grasping. By situating Ecological Psychology within the scientific foundations of symmetry and continuous symmetry groups, this framework potentially clarifies and extends Turvey’s legacy, providing a formal and generalizable language for the direct theory of perception–action.

Triboelectric nanogenerators (TENGs) have emerged as promising energy-harvesting devices capable of converting ambient mechanical energy into electricity through contact electrification and electrostatic induction. However, achieving high output performance and operational stability remains challenging due to their limited charge density and inefficient interfacial charge transfer. In this review, we comprehensively discuss surface modification approaches, encompassing materials selection, advanced processing techniques, and diverse application strategies, which collectively elevate TENG performance. We categorize key material systems and highlight how tailored surface modifications such as nano/micro-structuring, chemical functionalization, dielectric engineering, and plasma treatments enhance charge generation and retention. Moreover, we analyze processing methodologies that optimize surface morphology and electronic properties, thereby improving frictional interactions and charge trapping efficiency. These enhancements directly translate into superior device metrics, including increased open-circuit voltage, current density, and energy conversion efficiency. Finally, we explore practical implementations across wearable sensors, IoT platforms, and flexible electronic systems, illustrating how surface-engineered TENGs advance the development of next-generation self-powered technologies. This review aims to provide a comprehensive framework that integrates materials, processing, and application perspectives, offering guidance for future research toward high-performance, reliable TENG systems.