Recycling technologies, policies, prospects, and challenges for spent batteries

The recycling of spent batteries is an important concern in resource conservation and environmental protection, while it is facing challenges such as insufficient recycling channels, high costs, and technical difficulties. To address these issues, a review of the recycling of spent batteries, emphasizing the importance and potential value of recycling is conducted. Besides, the recycling policies and strategies implemented in representative countries are summarized, providing legal and policy support for the recycling industry. Moreover, a comprehensive classification and comparison of recycling technologies identify the characteristics and current status of different approaches. The integrated recycling technology provides a better recycling performance with zero-pollution recycling of spent battery. Biorecycling technology is expected to gain a broad development prospect in the future owing to the superiority of energy-saving and environmental protection, high recycling efficiency, via microbial degradation, enzymatic degradation, etc. Consequently, as for the existing recycling challenges of waste batteries, developing new recycling technology and perfecting its recycling system is an indispensable guarantee for the sustainable development of waste battery. Meanwhile, theoretical support is offered for the recycling of spent batteries.

Solar-driven thermochemical conversion of H2O and CO2 into sustainable fuels

Solar-driven thermochemical conversion of H2O and CO2 into sustainable fuels, based on redox cycle, provides a promising path for alternative energy, as it employs the solar energy as high-temperature heat supply and adopts H2O and CO2 as initial feedstock. This review describes the sustainable fuels production system, including a series of physical and chemical processes for converting solar energy into chemical energy in the form of sustainable fuels. Detailed working principles, redox materials, and key devices are reviewed and discussed to provide systematic and in-depth understanding of thermochemical fuels production with the aid of concentrated solar power technology. In addition, limiting factors affecting the solar-to-fuel efficiency are analyzed; meanwhile, the improvement technologies (heat recovery concepts and designs) are summarized. This study therefore sets a pathway for future research works based on the current status and demand for further development of such technologies on a commercial scale.

Multisite photocatalytic depolymerization of lignin model compound utilizing full-spectrum light over magnetic microspheres

Photocatalytic depolymerization is a high value-added approach for utilization of lignin. In this study, magnetic microspheres of FeCoRu@SiO2-TiO2 were synthesized by a co-precipitation method. Doping with CoOx and RuOx was used to improve the response to visible light, and doping with TiO2 was used to improve the response to ultraviolet light (λ < 380 nm). The lignin model compound depolymerization rate was >90%. The electron paramagnetic resonance results showed that the reaction occurred in two steps (aerobic phase and oxygen-free phase). Most of the O2- was produced in the first step by cleavage of C-O bonds. The second step was inhibited in an oxygen-free atmosphere. This research provides a valid method for enhancing the photocatalytic properties using full-spectrum light and exploring the lignin photocatalytic depolymerization mechanism. Further research is required to develop the catalyst properties and performance to produce radicals.

Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions

Incomplete combustion of fossil fuels and biomass burning emit large amounts of soot particles into the troposphere. The condensation process is considered to influence the size (Dp) and mixing state of soot particles, which affects their solar absorption efficiency and lifetimes. However, quantifying aging evolution of soot remains hampered in the real world because of complicated sources and observation technologies. In the Himalayas, we isolated soot sourced from transboundary transport of biomass burning and revealed soot aging mechanisms through microscopic observations. Most of coated soot particles stabilized one soot core under Dp < 400 nm, but 34.8% of them contained multi-soot cores (nsoot ≥ 2) and nsoot increased 3-9 times with increasing Dp. We established the soot mixing models to quantify transformation from condensation- to coagulation-dominant regime at Dp ≈ 400 nm. Studies provide essential references for adopting mixing rules and quantifying the optical absorption of soot in atmospheric models.

Low-noise fluorescent detection of cardiac troponin I in human serum based on surface acoustic wave separation

Acute myocardial infarction (AMI) is a life-threatening disease when sudden blockage of coronary artery occurs. As the most specific biomarker, cardiac troponin I (cTnI) is usually checked separately to diagnose or eliminate AMI, and achieving the accurate detection of cTnI is of great significance to patients' life and health. Compared with other methods, fluorescent detection has the advantages of simple operation, high sensitivity and wide applicability. However, due to the strong fluorescence interference of biological molecules in body fluids, it is often difficult to obtain high sensitivity. In order to solve this problem, in this study, surface acoustic wave separation is designed to purify the target to achieve more sensitive detection performance of fluorescent detection. Specifically, the interference of background noise is almost completely removed on a microfluidic chip by isolating microbeads through acoustic radiation force, on which the biomarkers are captured by the immobilized detection probe. And then, the concentration of cTnI in human serum is detected by the fluorescence intensity change of the isolated functionalized beads. By this way, the detection limit of our biosensor calculated by 3σ/K method is 44 pg/mL and 0.34 ng/mL in PBS buffer and human serum respectively. Finally, the reliability of this method has been validated by comparison with clinical tests from the nephelometric analyzer in hospital.

Fabrication of 3D-printed molds for polydimethylsiloxane-based microfluidic devices using a liquid crystal display-based vat photopolymerization process: printing quality, drug response and 3D invasion cell culture assays

Microfluidic platforms enable more precise control of biological stimuli and environment dimensionality than conventional macroscale cell-based assays; however, long fabrication times and high-cost specialized equipment limit the widespread adoption of microfluidic technologies. Recent improvements in vat photopolymerization three-dimensional (3D) printing technologies such as liquid crystal display (LCD) printing offer rapid prototyping and a cost-effective solution to microfluidic fabrication. Limited information is available about how 3D printing parameters and resin cytocompatibility impact the performance of 3D-printed molds for the fabrication of polydimethylsiloxane (PDMS)-based microfluidic platforms for cellular studies. Using a low-cost, commercially available LCD-based 3D printer, we assessed the cytocompatibility of several resins, optimized fabrication parameters, and characterized the minimum feature size. We evaluated the response to both cytotoxic chemotherapy and targeted kinase therapies in microfluidic devices fabricated using our 3D-printed molds and demonstrated the establishment of flow-based concentration gradients. Furthermore, we monitored real-time cancer cell and fibroblast migration in a 3D matrix environment that was dependent on environmental signals. These results demonstrate how vat photopolymerization LCD-based fabrication can accelerate the prototyping of microfluidic platforms with increased accessibility and resolution for PDMS-based cell culture assays.

Re-defining wearable robots: a multidisciplinary approach towards a unified terminology

Effective communication is especially important in the wearable robots (WRs) community, which encloses a great variety of devices across different application domains, e.g., healthcare, occupational, and consumer. In this paper we present a vocabulary of terms with the aim to create a common understanding of terms and concepts among the different fields of expertise relevant in the WRs community. Our goal is to develop shared documentation that could serve as a reference to facilitate the use of accepted definitions in the field. The presented vocabulary is the result of different focus group discussions among experts in the field. The resulting document was then validated by presenting it to the WR community through an online survey. The results of the survey highlight a strong agreement in terms of acceptance of the vocabulary, its usefulness, and applicability of the proposed definitions as well as an overall appreciation for its purpose and target. This work represents a pilot study providing unique material for the WR community, encouraging the use of shared agreed definitions. The reported version of the vocabulary has been made available as a live document in a github repository, for public commenting and further improvements.

Removal of the rate table: MEMS gyrocompass with virtual maytagging

High-performance micro-electro-mechanical system (MEMS) gyrocompasses for north-finding systems have been very popular for decades. In this paper, a MEMS north-finding system (NFS) based on virtual maytagging (VM) is presented for the first time. In stark contrast to previous schemes of MEMS-based NFSs (e.g., carouseling, maytagging) and the abandoning rate table, we developed a honeycomb disk resonator gyroscope (HDRG) and two commercial accelerometers for azimuth detection. Instead of the physical rotation of the integrated turntable in traditional NFSs, the vibratory working modes of the HDRG are rotated periodically with electronic control to reduce the uncertainty in the azimuth. After systematically analyzing the principle of NFSs with VM, we designed tests to verify the practicability at the sensor level. A bias instability of 0.0078°/h can be obtained during one day with VM in an HDRG. We also implemented comparative north-finding experiments to further check our strategy at the system level. The accuracy in the azimuth can reach 0.204° for 5 min at 28.2° latitude with VM and 0.172° with maytagging. The results show that without any mechanical turning parts, VM technology makes it possible to develop high-precision handheld MEMS NFSs.

Synthetic biology tools for environmental protection

Synthetic biology transforms the way we perceive biological systems. Emerging technologies in this field affect many disciplines of science and engineering. Traditionally, synthetic biology approaches were commonly aimed at developing cost-effective microbial cell factories to produce chemicals from renewable sources. Based on this, the immediate beneficial impact of synthetic biology on the environment came from reducing our oil dependency. However, synthetic biology is starting to play a more direct role in environmental protection. Toxic chemicals released by industries and agriculture endanger the environment, disrupting ecosystem balance and biodiversity loss. This review highlights synthetic biology approaches that can help environmental protection by providing remediation systems capable of sensing and responding to specific pollutants. Remediation strategies based on genetically engineered microbes and plants are discussed. Further, an overview of computational approaches that facilitate the design and application of synthetic biology tools in environmental protection is presented.

Natural science and engineering instructors' knowledge and practice of brain-based instruction in Ethiopian higher education institutions

Currently, the Brain-Based Instructional approach has become an alternative instructional method in the schooling system of different countries of the world. This study explored the current situation of natural science and engineering instructors' knowledge and practices of brain-based instruction in Ethiopian higher education institutions. A descriptive survey research design with concurrent mixed methods was employed. Data collection tools were developed based on the twelve principles of brain-based learning theory and confirmed their validity and reliability. Survey questions were used to gather quantitative data from 512 randomly selected instructors. Qualitative data were collected through interviews with 14 purposely selected instructors. Classroom observation was also conducted to triangulate data obtained through interviews and survey questions. Quantitative data were analyzed using descriptive statistics, whereas qualitative data were analyzed thematically. The findings of this study depict that most natural science and engineering instructors have good knowledge of brain-based instruction but not transferable knowledge and skills. There is a clear gap between instructors' knowledge and classroom practice of brain-based instruction. Thus, to improve instructional practices, higher education institutions need to work strongly to narrow the existing variation. Implications and further recommendations are also suggested.

An accelerated sine mapping whale optimizer for feature selection

An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems.

Diya - A universal light illumination platform for multiwell plate cultures

Recent progress in protein engineering has established optogenetics as one of the leading external non-invasive stimulation strategies, with many optogenetic tools being designed for in vivo operation. Characterization and optimization of these tools require a high-throughput and versatile light delivery system targeting micro-titer culture volumes. Here, we present a universal light illumination platform - Diya, compatible with a wide range of cell culture plates and dishes. Diya hosts specially designed features ensuring active thermal management, homogeneous illumination, and minimal light bleedthrough. It offers light induction programming via a user-friendly custom-designed GUI. Through extensive characterization experiments with multiple optogenetic tools in diverse model organisms (bacteria, yeast, and human cell lines), we show that Diya maintains viable conditions for cell cultures undergoing light induction. Finally, we demonstrate an optogenetic strategy for in vivo biomolecular controller operation. With a custom-designed antithetic integral feedback circuit, we exhibit robust perfect adaptation and light-controlled set-point variation using Diya.

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.

Resource potential mapping of bifacial photovoltaic systems in India

Bifacial photovoltaic is one of the technologies that can spearhead Indian ambitions to achieve the 7th United Nations Sustainable Development Goals and Nationally Determined Contributions of COP 26. But, like all emerging technology, the lack of awareness and unavailability of extensive data, like technology potential maps that could aid people in identifying the advantages of newer technologies, has led to sluggish growth in the Indian market. To expedite the growth of bifacial PV in the Indian market, optimized resource potential maps of bifacial PV were developed from an experimentally validated view factor-based bifacial PV model. The annual average bifacial gain was found to vary between 2.5% and 22% at various locations in India. The effect of ground albedo and height of installation was also studied. Furthermore, the annual and seasonal power out of bifacial PV was higher than monofacial PV at any location in India.

Mechanism that determines the economics of 100% renewable power systems

Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems.

Effects of different drop height training on lower limb explosive and change of direction performance in collegiate Sanda athletes

The purpose of the present study was to examine the effects of 6 weeks of 40-, 60-, or 80-cm drop jump (DJ) training on lower limb explosive and change of direction (CoD) performance in collegiate Sanda athletes. Repeated-measure ANOVA revealed that there was a significant group × time interaction for standing long jump test (p = 0.006), counter movement jump test (p = 0.026), Illinois agility test (p = 0.003), square test (p = 0.018), Nebraska test (p = 0.027), t test (p = 0.032), and hexagon test (p = 0.012) due to the best performance observed at post-test compared with pre-test for DJ60 (effect size = 0.89-2.89), and the improvement was higher than that of the other groups. These findings suggest that 6 weeks of DJ training could improve the lower limb explosive and CoD performance in collegiate Sanda athletes and that 60 cm may be the optimal drop height.

Design and validation of depth camera-based static posture assessment system

Postural abnormalities have become a prevalent issue affecting individuals of all ages, resulting in a diminished quality of life. Easy-use and reliable posture assessment tools can aid in screening for and correcting posture deviation at an early stage. In this study, we present a depth camera-based static posture assessment system to screen for common postural anomalies such as uneven shoulders, pelvic tilt, bowlegs and knock-knees, forward head, scoliosis, and shoulder blade inclination. The system consists of an Azure Kinect camera, a laptop, and evaluation software. Our system accurately measures skeleton and posture indexes and shows favorable agreement with a golden standard optical infrared motion capture system. The findings indicate that the system is a low-cost posture assessment tool with high precision and accuracy, suitable for initial screening of postural abnormalities in individuals of all ages.

Research progress and future perspectives on electromagnetic wave absorption of fibrous materials

Electromagnetic waves have caused great harm to military safety, high-frequency electronic components, and precision instruments, and so forth, which urgently requires the development of lightweight, high-efficiency, broadband electromagnetic waves (EMW) absorbing materials for protection. As the basic fibrous materials, carbon fibers (CFs) and SiC fibers (SiCf) have been widely applied in EMW absorption due to their intrinsic characteristics of low density, high mechanical properties, high conductivity, and dielectric loss mechanism. Nevertheless, it has remained a great challenge to develop lightweight EMW-absorbing fibrous materials with strong absorption capability and broad frequency range. In this review, the fundamental electromagnetic attenuation mechanisms are firstly introduced. Furthermore, the preparation, structure, morphology, and absorbing performance of CFs and SiCf-based EMW absorbing composites are summarized. In addition, prospective research opportunities are highlighted toward the development of fibrous absorbing materials with the excellent absorption performance.

Advanced operation of heated fluidic resonators via mechanical and thermal loss reduction in vacuum

For simultaneous and quantitative thermophysical measurements of ultrasmall liquid volumes, we have recently developed and reported heated fluidic resonators (HFRs). In this paper, we improve the precision of HFRs in a vacuum by significantly reducing the thermal loss around the sensing element. A vacuum chamber with optical, electrical, and microfluidic access is custom-built to decrease the convection loss by two orders of magnitude under 10-4 mbar conditions. As a result, the measurement sensitivities for thermal conductivity and specific heat capacity are increased by 4.1 and 1.6 times, respectively. When differentiating between deionized water (H2O) and heavy water (D2O) with similar thermophysical properties and ~10% different mass densities, the signal-to-noise ratio (property differences over standard error) for H2O and D2O is increased by 9 and 5 times for thermal conductivity and specific heat capacity, respectively.

Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning

Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.

Active methods in electricity and magnetism courses: Influence of degree, academic level and gender on student performance

The performance of first-year students in electromagnetism (E&M) courses of different engineering degrees at a Spanish public university was measured using the Brief Electricity and Magnetism Assessment (BEMA), a standard research-based instrument to assess students' understanding after attending introductory courses in electricity and magnetism. In all cases, Flipped classroom (FC) built on information and communications technology was used. The objective of this paper is to analyse if the gain in the BEMA pre and post-test results is influenced by several factors such as the degree, the students' academic grade, and gender. Moreover, as some studies have shown that the students' retention of the concepts was significantly stronger in active learning than in traditional approaches, a third BEMA test was performed by the students to analyse the long-term retention gain dependence on the same factors. Students from different engineering degree programs were asked to complete two BEMA tests during the course and a third one after a few months. ANOVA tests were used to analyse the existence of significant differences in gain between student degree programs, student academic level and student gender. Results have shown no differences in the BEMA performance by degree program, but significant differences were found by academic level and gender. Retention did not depend on the degree course but on the academic level. Mean gain value by academic level, and gender was obtained and concluded that the best students presented the best gain results and that gain depends on the students' gender: males outperformed females in the BEMA tests, although there were no significant differences in the course grades. It is thus necessary to understand these differences and to implement measures in daily teaching work to improve women's performance.

Valorization of lignin-derived compounds into poly(3-hydroxybutyrate-co-3-hydroxyvalerate) by engineered Halomonas sp. Y3

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is a biopolyester with great potential, but its high production cost via the propionate-dependent pathway has hindered its development. Herein, we engineer Halomonas sp. Y3 to achieve efficient conversion of various LDCs into PHBV without propionate supplement. Initially, we successfully achieve PHBV production without propionate supplement by overexpressing threonine synthesis. The resulting biopolyester exhibits a 3 HV proportion of up to 7.89 mol%, comparable to commercial PHBV (8 mol%) available from Sigma Aldrich (403105). To further enhance PHBV production, we rationally design the reconstruction of aromatic compound catabolism. The engineered strain Y3_18 efficiently assimilates all LDCs containing syringyl (S), guaiacyl (G), and p-hydroxyphenyl-type (H) units. From 1 g/L of S-, G-, and H-type LDCs, Y3_18 produces PHBV at levels of 449 mg/L, 488 mg/L, and 716 mg/L, respectively, with yields of 44.9 % (g/g), 48.8 % (g/g), and 71.6 % (g/g). Moreover, to improve PHBV yield from lignin, we integrate laccase-secretion and PHBV production modules. This integration leads to the accumulation of 425.84 mg/L of PHBV with a yield of 21.29 % (g/g) and a 3 HV proportion of 6.38 mol%. By harnessing the capabilities of Halomonas sp. Y3, we demonstrate an efficient and sustainable approach for PHBV production from a variety of LDCs.

Economical production of Pichia pastoris single cell protein from methanol at industrial pilot scale

BACKGROUND

Methanol, synthesized from CO2, is a potentially sustainable one-carbon (C1) resource for biomanufacturing. The use of methanol as a feedstock to produce single cell protein (SCP) has been investigated for decades as an alternative to alleviate the high global demand for animal-derived proteins. The methylotrophic yeast Pichia pastoris is an ideal host for methanol-based SCP synthesis due to its natural methanol assimilation ability. However, improving methanol utilization, tolerance to higher temperature, and the protein content of P. pastoris are also current challenges, which are of great significance to the economical industrial application using methanol as a feedstock for SCP production.

RESULTS

In the present work, adaptive laboratory evolution (ALE) has been employed to overcome the low methanol utilization efficiency and intolerance to a higher temperature of 33 °C in P. pastoris, associated with reduced carbon loss due to the lessened detoxification of intracellular formaldehyde through the dissimilation pathway and cell wall rearrangement to temperature stress resistance following long-term evolution as revealed by transcriptomic and phenotypic analysis. By strengthening nitrogen metabolism and impairing cell wall synthesis, metabolic engineering further increased protein content. Finally, the engineered strain via multi-strategy produced high levels of SCP from methanol in a pilot-scale fed-batch culture at 33 °C with a biomass of 63.37 g DCW/L, methanol conversion rate of 0.43 g DCW/g, and protein content of 0.506 g/g DCW. SCP obtained from P. pastoris contains a higher percentage of protein compared to conventional foods like soy, fish, meat, whole milk, and is a source of essential amino acids, including methionine, lysine, and branched-chain amino acids (BCAAs: valine, isoleucine, leucine).

CONCLUSIONS

This study clarified the unique mechanism of P. pastoris for efficient methanol utilization, higher temperature resistance, and high protein synthesis, providing a P. pastoris cell factory for SCP production with environmental, economic, and nutritional benefits.

Direct recycling of spent Li-ion batteries: Challenges and opportunities toward practical applications

With the exponential expansion of electric vehicles (EVs), the disposal of Li-ion batteries (LIBs) is poised to increase significantly in the coming years. Effective recycling of these batteries is essential to address environmental concerns and tap into their economic value. Direct recycling has recently emerged as a promising solution at the laboratory level, offering significant environmental benefits and economic viability compared to pyrometallurgical and hydrometallurgical recycling methods. However, its commercialization has not been realized in the terms of financial feasibility. This perspective provides a comprehensive analysis of the obstacles that impede the practical implementation of direct recycling, ranging from disassembling, sorting, and separation to technological limitations. Furthermore, potential solutions are suggested to tackle these challenges in the short term. The need for long-term, collaborative endeavors among manufacturers, battery producers, and recycling companies is outlined to advance fully automated recycling of spent LIBs. Lastly, a smart direct recycling framework is proposed to achieve the full life cycle sustainability of LIBs.

Combining LCA-MFA models to identify China's plastic value chain environmental impact mitigation pathways

Characterizing material flows and environmental impacts of plastic value chain is crucial for sustainable plastic management. Here, we combine material flow analysis and life cycle assessment methods to map the flows of eight major plastics and investigate the multiple environmental impacts of China's plastic value chain. We find that packaging and textile sectors dominate plastic consumption and are responsible for the value chain environmental burdens, but with low recycling rates. Major environmental impacts are generated in plastic production and product manufacturing stages because of the consumption of coal-based feedstocks and electricity. We therefore set up six scenarios by considering carbon neutrality energy pathway, plastic recycling improvement, and technology updating, finding that the value chain environmental impact can be reduced by 14%-57% in 2060 under combined scenario. Particularly, carbon neutrality renewable energy pathway plays an important role. These findings provide valuable insights to identify key mitigation pathways for plastic value chain.

Techno-economic analysis of residential building heating strategies for cost-effective upgrades in European cities

The energy crisis in Europe requires cost-effective evaluations of residential heating strategies to reduce costs and mitigate greenhouse gas emissions. This research studied different heating systems in China and Europe. Based on heating energy surveys, simulation models were developed and further expanded for European cities. Monte Carlo analyses were conducted to understand the heating demand and utility costs in Rome, Madrid, and Athens. The sensitivity analysis found that electrifying heating systems with heat pumps can reduce household heating costs and mitigate European cities' dependence on natural gas. However, the high upfront investment may hinder the cost-effective deployment of high-performance heat pump systems. Building envelope retrofits can also provide plausible energy savings despite relatively long payback periods. Financial incentive analyses were conducted to quantify how fiscal measures can improve technologies' techno-economic performance. Finally, the paper provided policy recommendations on future building cost-effective retrofits and heating electrification in Europe.

3D free-assembly modular microfluidics inspired by movable type printing

Reconfigurable modular microfluidics presents an opportunity for flexibly constructing prototypes of advanced microfluidic systems. Nevertheless, the strategy of directly integrating modules cannot easily fulfill the requirements of common applications, e.g., the incorporation of materials with biochemical compatibility and optical transparency and the execution of small batch production of disposable chips for laboratory trials and initial tests. Here, we propose a manufacturing scheme inspired by the movable type printing technique to realize 3D free-assembly modular microfluidics. Double-layer 3D microfluidic structures can be produced by replicating the assembled molds. A library of modularized molds is presented for flow control, droplet generation and manipulation and cell trapping and coculture. In addition, a variety of modularized attachments, including valves, light sources and microscopic cameras, have been developed with the capability to be mounted onto chips on demand. Microfluidic systems, including those for concentration gradient generation, droplet-based microfluidics, cell trapping and drug screening, are demonstrated. This scheme enables rapid prototyping of microfluidic systems and construction of on-chip research platforms, with the intent of achieving high efficiency of proof-of-concept tests and small batch manufacturing.

High-throughput extraction on a dynamic solid phase for low-abundance biomarker isolation from biological samples

Liquid biopsy, in particular circulating tumor DNA (ctDNA) analysis, has paved the way for a new noninvasive approach to cancer diagnosis, treatment selection and follow-up. As a crucial step in the analysis, the extraction of the genetic material from a complex matrix needs to meet specific requirements such as high specificity and low loss of target. Here, we developed a new generation of microfluidic fluidized beds (FBs) that enable the efficient extraction and preconcentration of specific ctDNA sequences from human serum with flow rates up to 15 µL/min. We first demonstrated that implementation of a vibration system inducing flow rate fluctuations combined with a mixture of different bead sizes significantly enhanced bead homogeneity, thereby increasing capture efficiency. Taking advantage of this new generation of high-throughput magnetic FBs, we then developed a new method to selectively capture a double-stranded (dsDNA) BRAF mutated DNA sequence in complex matrices such as patient serum. Finally, as proof of concept, ligation chain reaction (LCR) assays were performed to specifically amplify a mutated BRAF sequence, allowing the detection of concentrations as low as 6 × 104 copies/µL of the mutated DNA sequence in serum.

Attitudes towards science in higher education: Validation of questionnaire among science teacher candidates and engineering students in Indonesia

In higher education, academic success and failure can be affected by students' affective states, such as their attitudes towards courses and learning. Many studies highlight the role of attitudes in the educational process resulting in a high demand for attitude assessments to plan comprehensive instructional strategies. The present study assessed and compared attitudes towards science in higher education. A total of 296 Indonesian undergraduate students from science teacher training and engineering majors participated in this study by completing the online Attitudes Towards Science Questionnaire (ATSQ). The validation analysis with confirmatory factor analysis and the Rasch model was performed to measure the instrument's properties resulting in a good fit model for the questionnaire's theoretical construct with acceptable individual fit items. Comparing the attitudes of science teacher candidates and engineering students, no significant differences were found in attitude towards science. Both groups revealed moderate attitudes, with higher responses to the science value variable, followed by enjoyment and confidence, anxiety and difficulty, and participation in science learning and activities. However, there were differences in the pattern of correlations between the variables, especially for anxiety and difficulty. The ATSQ has good psychometric properties and therefore can be used in research to assess attitudes towards science. It can stimulate further research in different contexts to confirm empirical evidence and promote attitude development in higher education.

Scalable manufacturing of gene-modified human mesenchymal stromal cells with microcarriers in spinner flasks

Due to their immunomodulatory properties and in vitro differentiation ability, human mesenchymal stromal cells (hMSCs) have been investigated in more than 1000 clinical trials over the last decade. Multiple studies that have explored the development of gene-modified hMSC-based products are now reaching early stages of clinical trial programmes. From an engineering perspective, the challenge lies in developing manufacturing methods capable of producing sufficient doses of ex vivo gene-modified hMSCs for clinical applications. This work demonstrates, for the first time, a scalable manufacturing process using a microcarrier-bioreactor system for the expansion of gene-modified hMSCs. Upon isolation, umbilical cord tissue mesenchymal stromal cells (UCT-hMSCs) were transduced using a lentiviral vector (LV) with green fluorescent protein (GFP) or vascular endothelial growth factor (VEGF) transgenes. The cells were then seeded in 100 mL spinner flasks using Spherecol microcarriers and expanded for seven days. After six days in culture, both non-transduced and transduced cell populations attained comparable maximum cell concentrations (≈1.8 × 105 cell/mL). Analysis of the culture supernatant identified that glucose was fully depleted after day five across the cell populations. Lactate concentrations observed throughout the culture reached a maximum of 7.5 mM on day seven. Immunophenotype analysis revealed that the transduction followed by an expansion step was not responsible for the downregulation of the cell surface receptors used to identify hMSCs. The levels of CD73, CD90, and CD105 expressing cells were above 90% for the non-transduced and transduced cells. In addition, the expression of negative markers (CD11b, CD19, CD34, CD45, and HLA-DR) was also shown to be below 5%, which is aligned with the criteria established for hMSCs by the International Society for Cell and Gene Therapy (ISCT). This work provides a foundation for the scalable manufacturing of gene-modified hMSCs which will overcome a significant translational and commercial bottleneck. KEY POINTS: • hMSCs were successfully transduced by lentiviral vectors carrying two different transgenes: GFP and VEGF • Transduced hMSCs were successfully expanded on microcarriers using spinner flasks during a period of 7 days • The genetic modification step did not cause any detrimental impact on the hMSC immunophenotype characteristics.

Learning to manipulate a whip with simple primitive actions - A simulation study

This simulation study investigated whether a 4-degrees-of-freedom (DOF) arm could strike a target with a 50-DOF whip using a motion profile similar to discrete human movements. The interactive dynamics of the multi-joint arm was modeled as a constant joint-space mechanical impedance, with values derived from experimental measurement. Targets at various locations could be hit with a single maximally smooth motion in joint-space coordinates. The arm movements that hit the targets were identified with fewer than 250 iterations. The optimal actions were essentially planar arm motions in extrinsic task-space coordinates, predominantly oriented along the most compliant direction of both task-space and joint-space mechanical impedances. Of the optimal movement parameters, striking a target was most sensitive to movement duration. This result suggests that the elementary actions observed in human motor behavior may support efficient motor control in interaction with a dynamically complex object.

Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting

This paper proposes a novel clustering and dynamic recognition-based auto-reservoir neural network (CDbARNN) for short-term load forecasting (STLF) of industrial park microgrids. In CDbARNN, the available load sets are first decomposed into several clusters via K-means clustering. Then, by extracting characteristic information of the load series input to CDbARNN and the load curves belonging to each cluster center, a dynamic recognition technology is developed to identify which cluster of the input load series belongs to. After that, the input load series and the load curves of the cluster to which it belongs constitute a short-term high-dimensional matrix entered into the reservoir of CDbARNN. Finally, reservoir node numbers of CDbARNN which are used to match different clusters are optimized. Numerical experiments conducted on STLF of an actual industrial park microgrid indicate the dominating performance of the proposed approach through several cases and comparisons with other well-known deep learning methods.

Harnessing human and machine intelligence for planetary-level climate action

The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.

Digital light processing 3D printing for microfluidic chips with enhanced resolution via dosing- and zoning-controlled vat photopolymerization

Conventional manufacturing techniques to fabricate microfluidic chips, such as soft lithography and hot embossing process, have limitations that include difficulty in preparing multiple-layered structures, cost- and labor-consuming fabrication process, and low productivity. Digital light processing (DLP) technology has recently emerged as a cost-efficient microfabrication approach for the 3D printing of microfluidic chips; however, the fabrication resolution for microchannels is still limited to sub-100 microns at best. Here, we developed an innovative DLP printing strategy for high resolution and scalable microchannel fabrication by dosing- and zoning-controlled vat photopolymerization (DZC-VPP). Specifically, we proposed a modified mathematical model to precisely predict the accumulated UV irradiance for resin photopolymerization, thereby providing guidance for the fabrication of microchannels with enhanced resolution. By fine-tuning the printing parameters, including optical irradiance, exposure time, projection region, and step distance, we can precisely tailor the penetration irradiance stemming from the photopolymerization of the neighboring resin layers, thereby preventing channel blockage due to UV overexposure or compromised bonding stability owing to insufficient resin curing. Remarkably, this strategy can allow the preparation of microchannels with cross-sectional dimensions of 20 μm × 20 μm using a commercial printer with a pixel size of 10 μm × 10 μm; this is significantly higher resolution than previous reports. In addition, this method can enable the scalable and biocompatible fabrication of microfluidic drop-maker units that can be used for cell encapsulation. In general, the current DZC-VPP method can enable major advances in precise and scalable microchannel fabrication and represents a significant step forward for widespread applications of microfluidics-based techniques in biomedical fields.

Self-sensing intelligent microrobots for noninvasive and wireless monitoring systems

Microrobots have garnered tremendous attention due to their small size, flexible movement, and potential for various in situ treatments. However, functional modification of microrobots has become crucial for their interaction with the environment, except for precise motion control. Here, a novel artificial intelligence (AI) microrobot is designed that can respond to changes in the external environment without an onboard energy supply and transmit signals wirelessly in real time. The AI microrobot can cooperate with external electromagnetic imaging equipment and enhance the local radiofrequency (RF) magnetic field to achieve a large penetration sensing depth and a high spatial resolution. The working ranges are determined by the structure of the sensor circuit, and the corresponding enhancement effect can be modulated by the conductivity and permittivity of the surrounding environment, reaching ~560 times at most. Under the control of an external magnetic field, the magnetic tail can actuate the microrobotic agent to move accurately, with great potential to realize in situ monitoring in different places in the human body, almost noninvasively, especially around potential diseases, which is of great significance for early disease discovery and accurate diagnosis. In addition, the compatible fabrication process can produce swarms of functional microrobots. The findings highlight the feasibility of the self-sensing AI microrobots for the development of in situ diagnosis or even treatment according to sensing signals.