Complementary Characteristics Research Articles

Modeling and Grid-Connected Control of Wind-Solar-Storage Combined Power Generation System

Aiming at the complementary characteristics of wind energy and solar energy, a wind-solar-storage combined power generation system is designed, which includes permanent magnet direct-drive wind turbines, photovoltaic arrays, battery packs and corresponding converter control strategies. Simulation analysis is carried out by Matlab/Simulink platform, and the results show that the model of wind and solar storage system is correct and effective, and the grid connection control can be completed.

Effect of ruthenium(II)-bipyridine complex photosensitizer on the panchromatic light absorption and electron transfer in N719-dye sensitized photoanodes

It is known that the traditional N719 photosensitizer shows an insufficient light absorption from 400 to 500 nm in the visible region, which often limits the light harvesting efficiency (LHE) of its sensitized photoanodes and accordingly depresses the photoelectric conversion efficiency (PCE) of the resultant dye-sensitized solar cells (DSSCs). In this work, a type of [Ru (bpy)2 (dcbpy)](PF6)2 (labeled as Ru1) dyes was synthesized and used as a co-sensitizer for N719-dye sensitized photoanodes. Results show that Ru1 dye has a characteristic absorption peak within the range of 400–500 nm, accompanying with a higher molar extinction coefficient than N719 ones, which makes it well compensates for the insufficient LHE of N719 dye in this region. Moreover, the difference between the highest occupied molecular orbitals of Ru1 and N719 dyes can support a sufficient force for N719 dyes' regeneration. As a result, the DSSCs with Ru1/N719 composite photosensitizer show a 19%-improved PCE compared to the ones with N719 dyes. A series of tests and analysis indicate that the enhancement of PCE is mainly attributed to the complementary light absorption characteristics of N719/Ru1 in the visible light region, and a partial contribution to PCE could be due to the improved charge transfer within the photoanodes and the depressed competitive light absorption by I3− ions via energy band engineering.

An exploration of solar-induced chlorophyll fluorescence (SIF) factors simulated by SCOPE for capturing GPP across vegetation types

Solar-induced chlorophyll fluorescence (SIF) has been regarded as proxy data of vegetation photosynthesis; thus, it is assimilated into the terrestrial carbon cycle modeling. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model is one of the most utilized models of SIF simulation. However, the currently incomplete understanding of SCOPE SIF factors and the lack of exploring how SCOPE works under different vegetation types would deteriorate further carbon cycle research. Herein, this study disentangled decisive SIF factors in the SCOPE model; then, a sample SIF dataset (SynSIF), with spatial resolutions of both 0.02∘ and 0.05∘, was simulated through SCOPE model using factors above. Then this study validated how far SCOPE simulating SIF could capture GPP, compared with other SIF datasets. The results showed that: (1) There are five decisive SIF factors in SCOPE model, including plant status (leaf chlorophyll content and leaf area index) and meteorological parameters (incoming shortwave radiation, air temperature, and atmospheric vapor pressure). (2) The linear relationship of SynSIF-GPP outachieved other SIF datasets across all six vegetation types in southern South America, Asia, and Africa, improving R2 averagely by 0.33, 0.28, and 0.15, respectively. (3) SynSIF in Oceania and Europe, revealing GPP better in shrublands (with SynSIF-GPP R2 increasing by 0.15 and 0.16, respectively) and grasslands (with SynSIF-GPP coefficients increasing by 0.14 and 0.06, respectively), illustrated spatially complementary characteristics with GOSIF across varying vegetation types. Thus, we anticipate that this study could provide more complete information for SCOPE simulating SIF in different biome research when estimating the terrestrial carbon cycle.

Detecting beats in the photoplethysmogram: benchmarking open-source algorithms

The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best. Objective: This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology. Approach: Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F 1 score, which combines sensitivity and positive predictive value. Main results: Eight beat detectors performed well in the absence of movement with F 1 scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise with F 1 scores of 55%–91%; poorer in neonates than adults with F 1 scores of 84%–96% in neonates compared to 98%–99% in adults; and poorer in atrial fibrillation (AF) with F 1 scores of 92%–97% in AF compared to 99%–100% in normal sinus rhythm. Significance: Two PPG beat detectors denoted ‘MSPTD’ and ‘qppg’ performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.

A soft actor-critic deep reinforcement learning method for multi-timescale coordinated operation of microgrids

This paper develops a multi-timescale coordinated operation method for microgrids based on modern deep reinforcement learning. Considering the complementary characteristics of different storage devices, the proposed approach achieves multi-timescale coordination of battery and supercapacitor by introducing a hierarchical two-stage dispatch model. The first stage makes an initial decision irrespective of the uncertainties using the hourly predicted data to minimize the operational cost. For the second stage, it aims to generate corrective actions for the first-stage decisions to compensate for real-time renewable generation fluctuations. The first stage is formulated as a non-convex deterministic optimization problem, while the second stage is modeled as a Markov decision process solved by an entropy-regularized deep reinforcement learning method, i.e., the Soft Actor-Critic. The Soft Actor-Critic method can efficiently address the exploration–exploitation dilemma and suppress variations. This improves the robustness of decisions. Simulation results demonstrate that different types of energy storage devices can be used at two stages to achieve the multi-timescale coordinated operation. This proves the effectiveness of the proposed method.

Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface (BCI) is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery (MI) and mental arithmetic (MA) tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS.

Owner Identity, National Institutional Systems and Outward FDI Location: The Case of Chinese MNEs

We examine the relationship between majority owner identity, national institutional systems and Chinese MNEs' outward FDI location choices. We reason that particular owner types will prefer to locate in institutional systems with complementary institutional characteristics. Drawing upon the comparative capitalism literature that classifies institutional systems into nine configurations, we predict that owner identity significantly affects the selection of foreign investment destinations. Using a two-decade sample of 8,000 OFDI investment decisions, we explore the probabilities that particular owner types will select a specific institutional configuration as an OFDI destination. Our results show the likelihood of choosing a destination varies greatly across configurations. In particular, we find a strong correlation between owner identity and preferred institutional system in three types of enterprise. This study contributes to new understandings of how owner identity shapes the MNEs' ODFI destination and institutional system relationship.

Phytomanagement of Pb/Zn/Cu tailings using biosolids-biochar or -humus combinations: Enhancement of bioenergy crop production, substrate functionality, and ecosystem services.

The extreme characteristics of mine tailings generally prohibit microbial processes and natural plant growth. Consequently, vast and numerous tailings sites remain barren for decades and highly susceptible to windblown dust and water erosion. Amendment-assisted phytostabilization is a cost-effective and ecologically productive approach to mitigate the potential transport of residual metals. Due to the contrasting and complementary characteristics of biosolids (BS) and biochar (BC), co-application might be more efficient than individually applied. Studies considering BS and BC co-application for multi-metal tailings revegetation are scarce. As tailings revegetation is a multidimensional issue, clearly notable demand exists for a study that provides a comprehensive understanding on the co-application impact on interrelated properties of physicochemical, biological, mineral nitrogen availability, metal immobilization, water-soil interactions, and impacts on plant cultivation and biomass production. This 8-month greenhouse study aimed at investigating the efficacy of co-application strategies targeting BS and carbon-rich amendments (BC or humic substances (HS)) to phytomanage a slightly alkaline Pb/Zn/Cu tailings with bioenergy crops (poplar, willow, and miscanthus). A complementary assessment linking revegetation effectiveness to ecosystem services (ES) provision was also included. Owing to their rich nutrient and organic matter contents, BS had the most pronounced influence on most of the measured properties including physicochemical, enzyme activities, NH4+-N and NO3--N availability, immobilization of Zn, Cu, and Cd, and biomass production. Co-applying with BC exhibited efficient nutrient release and was more effective than BS alone in reducing metal bioavailability and uptake particularly Pb. Poplar and willow exhibited more superior phytostabilization efficiency compared to miscanthus which caused acidification-induced metal mobilization, yet BC and BS co-application was effective in ameliorating this effect. Enhancement of ES and substrate quality index mirrored the positive effect of amendment co-application and plant cultivation. Co-applying HS with BS resulted in improved nutrient cycling while BC enhanced water purification and contamination control services.

The spatial profile of skin indentation shapes tactile perception across stimulus frequencies

Multiple human sensory systems exhibit sensitivity to spatial and temporal variations of physical stimuli. Vision has evolved to offer high spatial acuity with limited temporal sensitivity, while audition has developed complementary characteristics. Neural coding in touch has been believed to transition from a spatial to a temporal domain in relation to surface scale, such that coarse features (e.g., a braille cell or corduroy texture) are coded as spatially distributed signals, while fine textures (e.g., fine-grit sandpaper) are encoded by temporal variation. However, the interplay between the two domains is not well understood. We studied tactile encoding with a custom-designed pin array apparatus capable of deforming the fingerpad at 5 to 80 Hz in each of 14 individual locations spaced 2.5 mm apart. Spatial variation of skin indentation was controlled by moving each of the pins at the same frequency and amplitude, but with phase delays distributed across the array. Results indicate that such stimuli enable rendering of shape features at actuation frequencies up to 20 Hz. Even at frequencies > 20 Hz, however, spatial variation of skin indentation continues to play a vital role. In particular, perceived roughness is affected by spatial variation within the fingerpad even at 80 Hz. We provide evidence that perceived roughness is encoded via a summary measure of skin displacement. Relative displacements in neighboring pins of less than 10 µm generate skin stretch, which regulates the roughness percept.

Random forest rock type classification with integration of geochemical and photographic data

Systematic manual and algorithmic classification workflows to characterize rock types are increasingly applied in the mineral exploration and mining industry, leveraging large systematically collected datasets. The aim of these are robust and repeatable classifications to aid more traditional visual logging practices. This study uses random forest algorithms to examine the impacts of integrating distinct datasets with complementary characteristics; chemistry to enable compositional distinctions, and photography to enable textural distinctions. We use a random forest classifier to examine the accuracy metrics of models producing rock type classifications using these two data types independently and integrated together. Prediction accuracy, measured using 10-fold cross validation, was 87% for geochemical-only inputs, 85% for photographic-only inputs, and 90% for mixed inputs from both datasets. A mining and exploration project in the Late Miocene to early Pliocene porphyry belt in Chile is the site of this case study, where datasets were systematically acquired using in-field methods on historical drill-cores. Results indicate that classification of lithology is improved by integration of photography-based and composition-based feature inputs. We infer that the benefits of integration would increase in proportion with increasing compositional similarity between rock types. This approach might also be applied to similar geological problems, such as alteration or metallurgical classifications; and with somewhat distinct datatypes, such as geochemical interval data and photographic metric extraction from coincident intervals in core photos.

Spatiotemporal Complementary Characteristics of Large-Scale Wind Power, Photovoltaic Power, and Hydropower

With the increasing proportion of renewable energy in power generation, the mixed utilization of multiple renewable energy sources has gradually become a new trend. Using the natural complementary characteristics of wind power, photovoltaic, and hydropower to evaluate the complementary potential of various energy sources has become a hot issue in the research of mixed utilization. Given that traditional complementarity research can only assess the complementarity between two energy sources, this paper proposes a method to simultaneously determine the complementarity between three large-scale renewable energy sources. Firstly, a three-dimensional vector represents the complementary characteristic combination of any two energy sources. The complementary characteristics of the three energy sources were obtained by finding the optimal solution for the three-dimensional vector. Finally, power stations were selected, located in different spatial areas on the world’s largest renewable energy base in Qinghai, China, as the research object to analyze and verify the complementary characteristics of wind-power–photovoltaic-power–hydropower at different spatiotemporal scales. The results show significant differences in the complementary characteristics of different time scales. The farther the spatial distance between different power stations, the stronger their complementarity. The best complementary index on the monthly and daily scales was improved by 8.49% and 6.51%. This study provides a new focus for the future evaluation of the complementary utilization potential of large-scale joint renewable energy in different world regions.

A capacity configuration planning method for regional integrated energy system based on bi-level optimization

Equipment capacity configuration is one of the key processes in regional integrated energy system planning. In this paper, the research is carried out from two aspects: the capacity optimization of regional integrated energy system and the dispatching optimization between different types of equipment. Multi-energy complementary characteristics and energy cascade utilization of regional integrated energy system is considered, and a bi-level optimization model of capacity configuration is proposed. In the upper level optimization process, the fast and elitist multi-objective genetic algorithm based on non-dominated sorting (NSGA-II) is used to calculate the optimal configuration of the system equipment, while the mixed integer linear programming algorithm (MILP) is used to deal with the optimal dispatching operation in the lower level process. An industrial park is taken as an example, and the validity of the proposed method is verified.

Staged co‐gasification characteristics of pyrolyzed semi‐coke and antibiotic filter residue under oxy‐fuel condition

AbstractPyrolyzed semi‐coke (SC) is a by‐product of coal‐graded utilization, while the clean and efficient utilization of excess powdery pyrolyzed SC has given a huge challenge. The antibiotic filter residue (AFR) is a kind of biomass solid wastes, which also requires proper disposal or resource utilization. Utilizing the fuel complementary characteristics of pyrolyzed SC and AFR, oxy‐fuel gasification co‐processing can not only effectively realize the resource utilization of solid wastes but also achieve significant CO2 reduction boosting carbon neutrality. Here, the Aspen Plus software was used to simulate the staged oxy‐fuel co‐gasification characteristics of SC and AFR. The simulation results show that with the increase of the SC proportion in blends from 0% to 100%, the lower heating value is raised from 9.48 to 11.26 MJ·m−3, the gas yield is increased from 1.39 to 2.55 m3·kg−1, and the cold gas efficiency grows by 39.28%. The rise of oxygen volume percentage improves the lower heating value in the produced gas and volume fraction of H2, but the contents of CO and CO2, together with the gas yield and cold gas efficiency, show decreasing trends. Raising gasification temperature within a reasonable range is beneficial for enhancing the gasification performance and improving the syngas quality. The raised oxygen equivalent ratio increases the content of CO2 but leads to the decline of CO and H2. For sample AFR, as the oxygen equivalent ratio is varied from 0.6 to 0.8, the volume fraction of CO2 increases by 14.19%, while the contents of CO and H2 reduce by 5.34% and 8.77%, respectively. The present study offers effective support for the co‐disposal and resource utilization of powdery pyrolyzed SC and AFR.

Probabilistic adaptive power pinch analysis for islanded hybrid energy storage systems

Hybrid energy storage systems involve the integration of multifarious energy storage technologies which have complementary operational characteristics. Although the incorporation of hybrid energy storage systems can suppress renewable energy intermittency and improve energy supply; control and coordination become more challenging. By integrating more assets and more functions in the system, the complexity becomes prohibiting and hence new versatile and scalable methods must be employed. The authors have in the past proposed a graphical energy management strategy based on the power pinch analysis framework to address this challenge but was contingent on average energy demand and supply profile that reflects only a single scenario case study. This paper proposes a probabilistic adaptive power pinch analysis paradigm for energy management of isolated hybrid energy storage systems with uncertainty, which is implemented in a model predictive receding horizon. The proposed method uses multistate stochastic power grand composite curves realised from the integration of distinct energy demand and supply profiles randomly sampled from historic data via Monte Carlo simulation. Thus, in a predictive horizon, the multiple possibilities which the state of the energy storage can attain must jointly satisfy a probabilistic chance constraint factor with necessary decisions inferred in the control horizon to negate uncertainty. The proposed probabilistic method was further improved by a correction mechanism that minimises the mean squared error between the actual and predicted state of charge of the battery. The performance of probabilistic power pinch analysis was tested on a hybrid energy storage system with renewable energy sources, a battery, a fuel cell and an electrolyser. The results clearly demonstrate improved robustness to Gaussian uncertainty than a day ahead power pinch analysis reference as over-dis/charging the battery and carbon emission were reduced by 98 %, 22 % and 100 % respectively but, necessitates allocating more hydrogen resources. A battery degradation model was also used to evaluate the performance of the probabilistic power pinch analysis energy management strategies.

Capacity optimization and feasibility assessment of solar-wind hybrid renewable energy systems in China

The solar-wind hybrid renewable energy systems, including wind farm, photovoltaic (PV) plant, concentrated solar power (CSP) plant, electric heater, battery, and bidirectional inverter, are analyzed in 36 typical locations in China. The effects of wind and solar energy resources on power supply reliability and economy and the optimal installed capacities are analyzed. The regions in China recommended for the development of the systems are given. Results show that the levelized cost of energy (LCOE) of system in Huade can be as low as 0.1 $/kWh when 90% of annual load demand is met. The wind speed and solar irradiation have a major effect while the complementary characteristics of wind and solar energy have an auxiliary effect on power supply reliability and cost of the system. Compared with the system in Tongliao, the LCOE of system in Qiqihar with lower wind speed and solar irradiation intensity is reduced by 9.8% due to the better complementary characteristics of wind and solar energy. For systems in locations with different wind and solar energy resources, the wind farm or PV plant is still the technology with the greatest cost advantage but the worst power supply reliability. The electric heater with thermal energy storage and power cycle is an essential factor to greatly improve power supply reliability economically. Meanwhile, one of the solar field and battery should be integrated into the system according to the direct normal irradiance conditions. When high power supply reliability is pursued, the CSP plant is recommended to be introduced in most regions. Moreover, the commercial promotion of CSP plant brings the most significant economic benefits to the system. As the CSP plant cost is reduced by 30%, the LCOE of system is reduced by 20.4%.

SVIn2: A multi-sensor fusion-based underwater SLAM system

This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.

Study on thermal performance of solar methanol reforming MCFC-GT-ST-CHP system

In recent years, fuel cells have begun to attract the attention of many countries in the world. Based on Aspen Plus, this paper constructs a distributed energy system about solar assisted Methanol reforming-Molten Carbonate Fuel Cell (MR-MCFC), which is combined with GT-ST power generation system and heating system. The results show that the MCFC efficiency, total electrical efficiency and total thermal efficiency of the system are 44.12%, 58.62% and 90.36%, respectively. The research results will provide a theoretical basis for exploring a new method of highly complementary utilization of solar energy and chemical energy. By analyzing the variable working conditions of the system, the complementary utilization affinity characteristics of the solar MR-MCFC and the integrated system are revealed. We will continue to innovate and optimize the system in order to provide reference for the further development of the CHP (Combined Heat and Power) system.

Multipoint Layout Planning Method for Multienergy Sources Based on Complex Adaptive System Theory

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy.

Hierarchical Scheduling Control Method for Cascade Hydro-PV-Pumped Storage Generation System

High photovoltaic penetration in a power system has significantly challenged its safety and economic operation. To use the complementary characteristics of various renewable energy sources (RESs) fully, a novel hierarchical scheduling control (HSC) method is presented to accommodate the variability and uncertainty of a cascade hydro-PV-pumped storage (CH-PV-PS) generation system. Considering the optimization functions and execution requirements of the CH-PV-PS system, the HSC method is divided into two layers: the dynamic optimization layer and the static optimization layer. The static optimization layer focuses on the economy of the CH-PV-PS system, and the dynamic optimization layer focuses on the safety of the CH-PV-PS system. In the first layer, that is, the static optimization layer, the objectives of the day-ahead and hour-ahead schedules are established, and a heuristic algorithm is combined with a linear programming algorithm to optimize the energy allocation. Considering the uncertainty of the PV power output and hour-ahead load, a real-time schedule is established in the second layer; that is, in the second layer, the dynamic optimization layer, real-time scheduling and prediction of active output are established. Model predictive control methods are introduced to correct for prediction bias at different time scales in order to fully utilize the control capability of hydropower generation. A CH-PV-PS real-world system in Southwest China is chosen as a case study. In the three scenarios, where only PV fluctuations are considered, the simulation results reveal that, compared with the traditional open-loop optimized and hierarchical open-loop optimization methods, the HSC method reduces the average relative deviation of PV and increases the system economics. After a large amount of RESs are connected to the power grid, the HSC method provides a solution for improving the consumption of RESs.

Recording Temperature with Magnetic Supraparticles.

Small-sized temperature indicator additives autonomously record temperature events via a gradual irreversible signal change. This permits, for instance, the indication of possible cold-chain breaches or failure of electronics but also curing of glues. Thus, information about the materials' thermal history can be obtained upon signal detection at every point of interest. In this work, maximum-temperature indicators with magnetic readout based on micrometer-sized supraparticles (SPs) are introduced. The magnetic signal transduction is, by nature, independent of the materials' optical properties. This facilitates the determination of valuable temperature information from the inside, that is, the bulk, even of dark and opaque macroscopic objects, which might differ from their surface. Compared to state-of-the-art optical temperature indicators, complementary magnetic readout characteristics ultimately expand their applicability. The conceptualized SPs are hierarchically structured assemblies of environmentally friendly, inexpensive iron oxide nanoparticles and thermoplastic polymer. Irreversible structural changes, induced by polymer softening, yield magnetic interaction changes within and between the hierarchic sub-structures, which are distinguishable and define the temperature indication mechanism. The fundamental understanding of the SPs' working principle enables customization of the particles' working range, response time, and sensitivity, using a toolbox-like manufacturing approach. The magnetic signal change is detected self-referenced, fast, and contactless.