This paper examines how circular economy ecosystems (CEEs) are enacted by investigating systemic interdependencies within emergent circular plastics ecosystems (CPEs). We present a qualitative case study on the plastics industry and show how systemic interdependencies, regulation, technology, markets, consumer behaviour, and collaboration form mutually reinforcing yet contradictory dynamics that shape plastic circularity outcomes. The findings reveal a set of core paradoxes at the heart of circular plastics transitions. Regulation operates as a layered and sometimes conflicting force, technological pathways are contested, market structures remain fragile and fragmented, and consumer participation is inconsistent. At the same time, collaboration proves fragile and difficult to sustain. Taken together, these systemic interdependencies generate what we term the “recycling for nothing paradox”, in that despite significant investments in the recycling, reuse, and reduction of plastics, misalignments across the ecosystem dilute or neutralize progress and limit the transformative impact. Conceptually, this paper advances CEE research by framing CPEs as enacted, adaptive, and contested processes embedded in material-specific contexts. Practically, this paper underscores the need to address the systemic contradictions rather than isolated drivers of plastics circularity to enable the effective transition towards CPEs and their value chains.

High-purity 1-butene is a crucial feedstock for the plastics industry and is currently produced by homogeneously catalyzed ethylene dimerization. Processes of this scale can benefit from heterogeneous catalysis, especially if reactants are delivered in the gas phase, to allow product recovery in flow. However, catalysts for gas-phase ethylene dimerization are exceedingly rare and generally show very low activity or reduced selectivity. Here, we report the use of a scalable, robust MOF catalyst (nickel-exchanged CFA-1, Ni-CFA-1) for the continuous gas-phase dimerization of ethylene to produce 1-butene. Operating under solvent-free conditions in a packed-bed reactor containing the MOF catalyst preactivated with a simple and straightforward approach, MeNi-CFA-1 delivers excellent selectivity (96%), turnover frequencies greater than 800,000 mol ethylene·mol Ni-1·h-1, and total turnover numbers of 1.23 × 108 mol ethylene·mol Ni-1, all exceeding the values observed for the commercial homogeneous catalyst. This flow process produces 49.4 kg 1-butene·g MOF-1 without requiring catalyst reactivation. The elimination of solvent allows a significantly higher concentration of ethylene near the Ni active sites, while the flow process drives away the butene product, thus suppressing undesired isomerization and oligomerization byproducts. Overall, this work highlights how MOFs can facilitate reactivity inconceivable for a molecular analogue and bridge the gap between molecular precision and industrial practicality, broadly illustrating the value of MOFs for the development of novel, selective, and scalable heterogeneous processes for the production of commodity chemicals.

As an early cradle of China’s plastics industry and a typical megacity, Shanghai’s urban rivers face increasingly severe microplastic pollution. This study selected the Suzhou River, known as Shanghai’s “mother river,” as its research subject. It systematically investigated the pollution characteristics of microplastics in the water and sediments, as well as the heavy metals carried on their surfaces. The abundance, shape, particle size, color, and polymer composition of microplastics were analyzed. SEM–EDS was employed for semiquantitative analysis of surface-bound heavy metals on microplastics. Results: The average microplastic abundance in the Suzhou River water was 2.18 ± 0.76 n·L−1, whereas the average microplastic abundance in the sediments was 939.29 ± 401.26 n·kg−1, indicating a relatively high pollution level in the sediments. Microplastics predominantly comprise fragments, fibers, and films, with polypropylene (PP), polyethylene (PE), and polyethylene terephthalate (PET) as the primary polymer types. EDS analysis detected 11 heavy metals on microplastic surfaces: Ti, Cr, Fe, Zn, Ga, As, Cd, In, Sn, Hg, and Pb. Critically, fragmented MPs were the primary carriers of multiple heavy metals, containing up to 7 different elements in sediments, including toxic Pb and Hg. Compared to water bodies, the metal spectrum loaded in sediments is more complex. It highlights their role as long-term reservoirs for co-pollutants. These findings demonstrate that MPs, especially fragments accumulated in sediments, may serve as significant vectors for the persistent storage and potential bioaccessible transfer of toxic heavy metals in urban aquatic ecosystems, posing a distinct long-term ecological risk that complicates sediment remediation efforts.

To design and develop an AI-based plastic surgery recommendation system using 3D photographs and psychological questionnaire surveys, aiming to provide personalized treatment solutions for the plastic surgery industry. Based on artificial intelligence technology, this study utilized patients' 3D photographs and psychological questionnaire results as training samples to construct a personalized AI-based plastic surgery recommendation system. This system comprehensively considers factors, such as patients' anxiety levels, economic status, and psychological expectations. The study selected 5543 cases of plastic surgery outpatients aged 18-55 years, collected their 3D photographs and questionnaire data, and used these for AI system training. The software predicted treatment projects and compared them with doctors' predictions to validate the system's accuracy and patient satisfaction. Third-party doctors evaluated the system's safety, ultimately developing an efficient and accurate plastic surgery recommendation system. The economic downturn in the post-COVID-19 era significantly impacted psychological health and the plastic surgery industry. Factors, such as age, education level, income, and gender, had significant effects on patients' psychological state and treatment willingness. The AI system integrated patients' psychological state, gender, income, and physical characteristics, providing personalized plastic surgery treatment suggestions and achieving a 93.25% patient satisfaction rate. These results indicate that the AI system offers comparable accuracy and safety to physicians while improving satisfaction, meaning that it could enhance clinical decision-making efficiency. The AI-based personalized plastic surgery recommendation system offers an innovative solution for the industry, enhancing the accuracy of treatment suggestions and patient satisfaction, thereby promoting sustainable development. In the post-pandemic era, the plastic surgery industry should focus on patients' physical, psychological, and economic factors to achieve personalized services. Experiment/New Technology. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Synergistic assault of DEHP and MPs: Unmasking the ER stress-triggered autophagic injury male fertility.

Advances in the detection of azodicarbonamide and the metabolic product semicarbazide.

Screening terpene-based eutectic solvents for bisphenol A extraction from plastic-packed dairy products and water storage tanks: COSMO-RS and quantum chemistry calculations.

Phenol exposure promotes tumor-related signaling and blood vessel formation through the extracellular signal-regulated kinase/p38/hypoxia-inducible factor-1α pathway in cellular and zebrafish models.

With increasing material variations and demands for process transparency in injection molding, it is essential that sensors evolve beyond machine/cavity condition sensing to material behavior monitoring. A promising solution is to supplement conventional pressure (P) and temperature (T) sensors with a capacitance (C) sensor capable of characterizing in-mold material dielectric properties in real time, thereby monitoring its shrinkage behavior. Unlike traditional data-driven approaches that predict only final shrinkage and require large datasets, this research aims to achieve in-process shrinkage estimation by integration with the capacitive sensor physical model. The method is validated through acrylonitrile butadiene styrene (ABS) and polypropylene (PP) experiments at different pressure settings, showing a low root mean square error of less than 11 μ m for our 2 mm thick part. Through online shrinkage estimation, the P-C correlation reveal the pressure effects on part dimension, thus facilitating pressure setting optimization. With real-time insight into packing effectiveness (under-, well-, over-packed), this approach is more efficient than conventional quality checks method. In addition, abnormal P–C correlations are investigated and found to be associated with injection molding leakage malfunctions. By thoroughly exploring the C-P-T sensor’s potential for shrinkage estimation, process monitoring, setting optimization, and leakage detection, this work sheds new light on the plastic industry automation. • Proposed capacitive method for in-process shrinkage estimation in injection molding. • Estimation method validated via mold-open shrinkage of two distinct materials. • Online thickness estimation enables efficient pressure setting optimization. • Abnormal capacitance-pressure correlation linked to leakage anomalies.

Microplastics, particularly those originating from industrial wastewater, have silently become a significant threat to marine and freshwater ecosystems, posing a serious risk to aquatic fauna through ingestion, bioaccumulation, and potential toxic effects. Microplastics in wastewater, mistaken for food, can severely disrupt the digestive and reproductive systems of aquatic species. This study assessed the effects of microplastics in wastewater on two freshwater species, Poecilia reticulata (Guppies) and Clarias gariepinus (The African Catfish) in order to understand their ecological impact and potential risks to aquatic and human health. The toxicity of plastic industry effluent was evaluated using acute toxicity tests (96-hour LC50) with varying concentrations: 20–100 mg/L each for Guppy and African catfish. Statistical analysis was conducted using ANOVA and Duncan Multiple Range Test (DMRT) in SPSS (version 20.0), and toxicity factor was generated using probit analysis with statistical significance set at p < 0.05. The LC50 values were 107.27 mg/L and 81.50 mg/L for Guppy and African Catfish respectively, that African Catfish were 1.32 times more susceptible to plastic industrial effluent. Mortality rates increased with effluent concentration. Bioaccumulation analysis revealed high levels of Pb, Cu, Ni, Hg, Cr, and Cd in the organs of fishes. Hepatic toxicity markers (AST, ALT, and ALP) rose with higher concentrations and prolonged exposure. The comet assay test showed significant increases in tail length, tail DNA, and olive moment, indicating genetic damage. These findings highlight the severe impact of plastic wastewater on aquatic life.

Abstract Lactic acid is a valuable organic compound with wide applications in the pharmaceutical, food, and biodegradable plastics industries. As Côte d’Ivoire seeks to strengthen its industrial capacity and promote sustainable practices, the development of efficient and environmentally benign methods for the recovery and purification of lactic acid produced from locally available resources is increasingly important. This study reports the development and optimization of a sustainable, cost‐effective process for recovering lactic acid from fermented molasses using n ‐butanol as the extraction solvent. Successful extraction was confirmed by nuclear magnetic resonance (NMR) spectroscopy, which demonstrated the presence of monomeric and dimeric lactic acid with minimal impurities. Enantiomeric analysis revealed a near‐racemic mixture (51.4% d‐ lactic acid and 48.6% l‐ lactic acid), highlighting the heterofermentative nature of the strain, and this composition remained stable even after 6 months. Racemic lactic acid can be exploited for the synthesis of amorphous poly(lactic acid), a preferred candidate for the production of biodegradable materials, drug delivery vehicles, and low‐strength scaffolding material for tissue regeneration. This would expand the potential applications of the product in the pharmaceutical, biomedical, and cosmetic industries. The extraction process was optimized through response surface methodology (RSM), identifying ammonium sulfate concentration, solvent‐to‐broth ratio, and pH as significant parameters influencing recovery. The quadratic model exhibited strong predictive performance ( R 2 = 0.9996) with no significant lack of fit, validating the reliability of the model. Optimal conditions (57.5% ammonium sulfate, solvent‐to‐broth ratio of 4.12, and pH 1.24) improved lactic acid recovery. These findings contribute to the valorization of local agro‐industrial waste, promoting greener biotechnological processes, and providing a scalable approach to high‐value products in Côte d’Ivoire, aligning with the nation’s goals of sustainable development and circular economy.

Abstract The demand for optically pure lactic acid (LA) is increasing due to its applications in the food, pharmaceutical, and biodegradable plastics industries. This study evaluated l ‐LA, d ‐LA, and dl ‐LA production using Lacticaseibacillus casei , L. coryniformis ATCC 25600, and Lactiplantibacillus plantarum , respectively, cultivated in laboratory‐scale bioreactors with controlled pH and temperature. Agroindustrial byproducts, including sugarcane molasses, glucose‐fructose syrup, and whey permeate, were compared with glucose as a reference substrate. Lacticaseibacillus casei reached the highest l ‐LA concentration in whey permeate (86 g L −1 in 24 h), with a yield of 1.07 g g −1 and maximum productivity of 4.70 g L −1 h −1 , values higher than previously reported for lactose‐based media. Lacticaseibacillus coryniformis ATCC 25600 achieved 70 g L −1 of d ‐LA from glucose‐fructose syrup (95 g L −1 initial sugars) in 72 h, with complete sugar depletion, yield of 0.75 g g −1 , and productivity of 1.95 g L −1 h −1 . For dl ‐LA, L. plantarum produced 70 g L −1 in whey permeate within 48 h (87% sugar removal, yield 0.76 g g −1 , productivity 1.44 g L ‐1 h −1 ). Across all strains, whey permeate and syrup enabled the most efficient fermentations, whereas molasses showed potential but was limited by nitrogen content. These results highlight the feasibility of valorizing low‐cost byproducts for optically pure LA production and provide kinetic and stoichiometric benchmarks to guide industrial process design.

This paper analyzes the impact of COVID-19 on the supply chain and production, investigating countermeasures for industrial recovery. In particular, the study examines how COVID-19 has affected the raw material supply chain, production, and outages on a real case study, that is, Turkey’s Glass-Reinforced Plastic (GRP) pipe industry. Using two- and three-way analysis of variance (ANOVA), significant negative impacts on the raw material supply chain are identified with 95% confidence. To enhance decision-making, the fuzzy q-rung orthopair set (FQROPS) and entropy-based multi-criteria decision-making (MCDM) methods are integrated in the baseline method. Specifically, ANOVA-identified factors, such as cost, supply continuity, production capacity, and risk level, are used as criteria in the MCDM analysis. Entropy determined criteria weights and FQROPS evaluate alternatives based on their proximity to the ideal solution. Findings show that significant disruptions occurred due to the pandemic. In addition, the MCDM analysis reveals that pre-pandemic conditions for key materials, such as fiberglass and resin, were significantly more favorable in terms of cost, supply continuity, production capacity, and risk levels. This integrated approach provides strategic insights for managing supply chains and production in the GRP pipe industry during and after pandemic events.

Revealing optimal end-of-life options for biodegradable plastic bags: A cradle-to-grave life cycle assessment.

Reactive blending of a vitrimer phase within a thermoplastic matrix offers promising perspectives for the high-throughput synthesis and (re)processing of vitrimer materials using conventional equipment of the plastic industry. Herein, we report on blends between a poly(vinylidene fluoride) (PVDF) matrix with commercially available epoxy/acid vitrimer precursors at fractions ranging from 24 to 75 vol %. The formulation required to obtain a satisfactory dispersion is rationalized with a particular emphasis on the synthesis of a tailor-made poly(methyl methacrylate-co-glycidyl methacrylate) copolymer acting as a compatibilizer at the PVDF/vitrimer interphase. While formulations incorporating intermediate amounts of vitrimer (50–60 vol %) display remarkable toughness well beyond those of the pure components, higher vitrimer fractions (>75 vol %) are required to form a bicontinuous morphology where both PVDF and vitrimer form percolating networks, thus improving the solvent resistance and the high temperature dimensional stability, respectively.

Microplastics (MPs) pollution has emerged as a critical environmental concern due to its ecological impacts and health hazards. Previous studies have confirmed the presence of MPs in various environmental media, including the atmosphere. However, research on airborne MPs contamination in occupational places, particularly in plastic manufacturing industry, remains limited. The objective of our research was to investigate and analyze the exposure characteristics of airborne MPs in the plastic manufacturing industry through pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) and hyperspectral imaging (HSI) analysis. The analytical results revealed that the types of raw materials used in factory production were identified as the main components of airborne MPs, which predominantly existed as particulate matter, characterized by small sizes (<10μm). In terms of concentration, the airborne MPs in the crushing workshop exhibited the highest (43.57±39.85μg/m3), followed by the injection molding workshop (19.37±7.38 μg/m3), workshop office (9.96±3.69μg/m3), and outdoor residential area (8.00±0.64μg/m3). Crushing operators were identified as the high-exposure group in the traditional plastic processing industry. Their MPs 8-hour time-weighted average concentration (CTWA) was 61.16μg/m3. It is estimated that male workers aged 18-44 in this crushing position could inhale approximately 117.03mg/a MPs through occupational exposure. Taken together, occupational exposure is a significant source of MPs inhalation in humans, which is closely associated with production processes and raw materials. Our results provide valuable data for establishing occupational health standards, formulating preventive and control strategies and further studies on occupational health risks assessment of MPs.

pH-switchable vortex-assisted liquid-liquid microextraction using hydrophobic eutectic solvents for the extraction of phthalate esters from water, fruit juice, and milk samples.

Few-shot anomaly detection (FSAD) has emerged as a critical paradigm for identifying irregularities using scarce normal references. While recent methods have integrated textual semantics to complement visual data, they predominantly rely on features pretrained on natural scenes, thereby neglecting the granular, domain-specific semantics essential for industrial inspection. Furthermore, prevalent fusion strategies often resort to superficial concatenation, failing to address the inherent semantic misalignment between visual and textual modalities, which compromises robustness against cross-modal interference. To bridge these gaps, this study proposes VTFusion, a vision-text multimodal fusion framework tailored for FSAD. The framework rests on two core designs. First, adaptive feature extractors for both image and text modalities are introduced to learn task-specific representations, bridging the domain gap between pretrained models and industrial data; this is further augmented by generating diverse synthetic anomalies to enhance feature discriminability. Second, a dedicated multimodal prediction fusion module is developed, comprising a fusion block that facilitates rich cross-modal information exchange and a segmentation network that generates refined pixel-level anomaly maps under multimodal guidance. VTFusion significantly advances FSAD performance, achieving image-level area under the receiver operating characteristics (AUROCs) of 96.8% and 86.2% in the 2-shot scenario on the MVTec AD and VisA datasets, respectively. Furthermore, VTFusion achieves an AUPRO of 93.5% on a real-world dataset of industrial automotive plastic parts introduced in this article, further demonstrating its practical applicability in demanding industrial scenarios.

Metabolic engineering of Escherichia coli for the high-level production of putrescine.

Priority control strategies derived from updated measurement of VOCs source profiles for plastic product industry.