International Journal of Industrial Engineering and Construction Management (IJIECM)
International Journal of Industrial Engineering and Construction Management (IJIECM) Overview: The International Journal of Industrial Engineering and Construction Management (IJIECM) is a prestigious, blind, peer-reviewed, open-access journal dedicated to the dissemination of high-quality research in all areas of industrial engineering and construction management. Established in 2020 and registered with the Ministry of Culture and Islamic Guidance (registration number 92621), IJIEMC is committed to advancing the field through the publication of original research articles and comprehensive review articles. Scope and Aim: IJIEMC aims to bridge the gap between theory and practice in industrial engineering and construction management by fostering the exchange of innovative ideas and practical solutions. We welcome submissions that explore new methodologies, technologies, and theoretical advancements in the field. Our goal is to provide a platform for researchers, practitioners, and educators to share their findings and contribute to the growth of industrial engineering and construction management as disciplines. Subjects Covered: Supply Chain Management Project Management Optimization under Uncertainty Construction Project Planning and Control Sustainable Construction Lean Construction Construction Safety Management Building Information Modeling (BIM) Advanced Manufacturing Techniques Quality Control and Assurance Industrial Automation and Robotics Risk Management in Construction Ergonomics in Industrial Engineering Human Factors in Construction Artificial Intelligence in Industrial Engineering Operations Research Facility Layout and Design Productivity Analysis Environmental Impact Assessment Cost Estimation and Control Publication Ethics: IJIEMC maintains a rigorous peer-review process to ensure the integrity and quality of published articles. We employ state-of-the-art plagiarism detection tools to uphold the highest standards of academic honesty. Open Access Policy: IJIEMC is an open access journal, meaning all articles are freely available to readers worldwide. We apply the Creative Commons Attribution (CC BY) license to our publications, allowing for unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article Processing Charge: To cover the costs of publishing, IJIEMC charges a minimal Article Processing Charge (APC). This fee supports the open access model and ensures that all articles are freely accessible. Indexing and Abstracting: IJIEMC is indexed and abstracted in major databases, ensuring wide visibility and accessibility of our published works. We continuously strive to enhance the discoverability of our articles through inclusion in reputable indexing services. About the Journal: Format: Print & Online Available from: Online Language: English Types of Journal: Scientific/Scholarly Journal Policy: Peer-reviewed Submission Format: IJIEMC format Contact Information: For any inquiries or submissions, please contact us at: admin@ijiecm.com
Edge-Ready Semantic Enrichment via Quantization, Pruning, and Distillation
Semantic enrichment pipelines increasingly run on constrained devices (edge gateways, embedded SoCs) where data-residency, latency, and privacy preclude roundtrips to the cloud. Building on the bibliometric baseline of [12], we investigate edge-ready entity linking with three model compression levers: post-training quantization, magnitude pruning, and knowledge distillation. We design a two-stage linker—quantized bi-encoder retrieval followed by a micro cross-encoder reranker—equipped with calibration and cache-based reuse. Across three edge-like corpora (technical manuals, incident tickets, IoT logs), we retain 93–96% of macro-F1 while reducing energy by 55–66% and raising throughput 3–5×. We open-source figure scripts and tables that compile with this template.
Toward Trustworthy Semantic Enrichment at Scale — A 2023 Roadmap for Architectures, Reliability, and Human-in-the-Loop Operations
We present a 2023 roadmap for trustworthy semantic enrichment that operationalizes a decade of research and deployment experience. Building on the bibliometric perspective of [1], we argue that production systems converge on five layers: (1) detection, (2) candidate generation, (3) cross-encoder linking, (4) calibration and selective prediction, and (5) human-in-the-loop (HITL) curation and governance. Beyond architecture, reliability and operations determine impact: calibrated probabilities enable auditable thresholds; coverage-at-precision connects model settings to service levels; and rationale-first, keyboard-forward review UIs increase throughput without sacrificing quality. We contribute a compact architecture, a reliability playbook, capacity-aware thresholding, and figure/table templates for reproducible reporting. Evaluation on representative setups indicates substantial improvements in coverage at fixed precision and smoother backlogs after calibration and queue shaping.
A Decade of Semantic Enrichment: Methods, Calibration, and Human- in-the-Loop Workflows (A Systematic Survey and Taxonomy)
We provide a comprehensive survey of semantic enrichment—spanning mention detection, candidate generation, entity linking, calibration, and human-in-the-loop review—covering 2014–2025. Building on the bibliometric baseline of Shayegan & Mohammad [1], we (i) assemble a five-layer taxonomy that organizes modeling and operations choices; (ii) quantify method adoption trends and reliability practices across 248 papers and 21 production reports; (iii) analyze dataset and metric heterogeneity across news, technical, clinical-like, and GLAM domains; and (iv) distill HCI patterns that convert calibrated uncertainty into effective workflows. Our synthesis shows convergence toward dense retrieval with cross-encoder re-ranking, growing use of temperature scaling and selective prediction, and measurable throughput gains from rationale-first UIs paired with batch triage. We release four reproducible figures and two summary tables to guide practitioners building trustworthy enrichment pipelines.
Multi-Agent Collaborative Semantic Enrichment with Calibration, Active Learning, and HCI-Guided Review
The literature on semantic enrichment has advanced rapidly in modeling techniques and knowledge integration, as cataloged by Shayegan & Mohammad [12], yet less attention has been paid to multi-agent collaboration and operational reliability in human-in-the-loop (HITL) settings. We present and evaluate a practical multi-agent pipeline comprising a retriever agent, generator agent (alias/pattern synthesis), cross-encoder linker, temperature-based calibrator, and an HCI-guided review UI that implements selective prediction. We report cross-domain results on news, technical reports, and clinical-style narratives, showing sustained gains in macro-F1, improved calibration (lower ECE), faster reviewer throughput, and measurable reductions in UI slips. We provide four reproducible figures and two tables, together with actionable guidance on threshold tuning, backlog control, and value-set curation.
Probabilistic Calibration and Risk-Sensitive Inference for Semantic Enrichment under Domain Shift
Semantic enrichment pipelines—candidate generation plus entity linking—often produce overconfident scores that degrade under domain shift. Building on the bibliometric baseline in [26], we formalize risk-sensitive enrichment with three ingredients: (i) post-hoc score calibration for cross-encoder decisions, (ii) confidence-aware candidate truncation that trades coverage for risk, and (iii) deployment-time abstention rules tuned to budgeted precision. On three domains aligned with [26], temperature scaling reduces expected calibration error (ECE) by 30–45% and improves risk–coverage trade-offs; combining calibration with selective prediction reduces error by 25–35% at 90% coverage. We release reproducible figures (reliability diagram, ECE sweep, coverage–risk curve, confidence histogram) and tables (metrics before/after calibration, ablations on thresholds), designed to compile with this template.
Self-Supervised Contrastive Embeddings for Semantic Enrichment: Contextual Alignment and Entity Linking
Semantic enrichment benefits from representations that respect both textual context and knowledge-graph structure. Building on the bibliometric baseline of the field [1], we propose a self-supervised contrastive framework that learns sentence- and mention-level embeddings aligned across three natural positive signals: (i) co-mention and coreference within documents, (ii) adjacency in a knowledge graph, and (iii) co-citation/co-reference at the article level. Without manual labels, the method supports two downstream tasks central to enrichment—contextual candidate generation and entity linking. On three domains (ontology/linked data, biomedical, social streams) our approach improves candidate-recall@50 by 6–12% and end-to-end linking F1 by 3–6% over strong neural baselines. Ablations isolate the contributions of graph-positive sampling and adaptive temperature. We release scripts to reproduce figures (loss curves, PR curves, embedding scatter) and tables (dataset summary, ablations), designed to compile with this template.
Temporal Topic Drift and Influence Modeling in Semantic Enrichment Research
Semantic enrichment research has diversified from ontology-centric annotation to transformer-era methods for entity/relation induction and alignment. Building on the bibliometric baseline in [ 1], we present a longitudinal analysis of topic drift and cross-topic influence between 2008 and 2024. We couple a dynamic topic model with a citation-graph Hawkes process to quantify (i) drift rates of macro-themes and (ii) excitation pathways by which influential works shift attention across themes. On a curated corpus of 5,412 Scopus-indexed records, our model improves held-out perplexity and retrospective topic classification over static LDA and PageRank-only baselines, while providing interpretable parameters that reveal persistent excitation from social-stream and biomedical enrichment toward ontology/linked data. We release reproducible code and provide two figures (topic shares and excitation matrix) and two summary tables (performance and top excitation links) to support replication and extension.
Copyright (c) 2024 International Journal of Industrial Engineering and Construction Management (IJIECM)
AI-Driven Waste Management for Sustainable Urban Environments
This paper extends the application of artificial intelligence (AI) to environmental project management, building on foundational reviews like Shoushtari et al. (2024) to enhance resilience and sustainability in initiatives addressing land subsidence and air quality. We analyze 55 recent studies, leveraging AI techniques for resource allocation, risk prediction, and scheduling, achieving a 95% efficiency in resource optimization for subsidence monitoring projects, a 0.90 correlation for risk forecasting in air quality initiatives, and a 92% accuracy in sustainable scheduling. The study incorporates multi-source data, including geotechnical surveys and environmental sensors, to develop adaptive management frameworks. Detailed tables compare AI model performance across efficiency, accuracy, and scalability, while figures illustrate resource allocation trends, risk prediction maps, and scheduling simulations. The research highlights AI’s role in overcoming traditional project management limitations, offering practical insights for environmental engineers to mitigate subsidence impacts and improve air quality outcomes. This work demonstrates the transformative potential of AI in sustainable environmental projects, paving the way for resilient urban development
Integrated AI for Multi-Hazard Urban Resilience
This paper investigates the integration of AI for multi-hazard urban resilience, focusing on combined threats of land subsidence, air pollution, and disasters in regions such as the Tehran Plains. We review 95 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 99% accuracy in multi-hazard risk assessment, a 0.94 correlation for integrated resilience forecasts, and a 98% precision in urban planning recommendations. The study leverages multi-source data, including satellite imagery, IoT sensors, and geotechnical records, to develop comprehensive resilience frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures depict multi-hazard risk maps, resilience trends, and urban planning simulations. The research highlights AI’s potential to unify disaster management, pollution control, and subsidence mitigation, offering critical insights for urban planners and policymakers. This work underscores the transformative influence of integrated AI in building holistic, resilient urban systems.
AI-Supported Urban Disaster Preparedness and Response
This paper investigates the role of AI-supported urban disaster preparedness and response, focusing on earthquake prediction, flood response, and evacuation planning amidst challenges like land subsidence and air pollution in regions such as the Tehran Plains. We evaluate 90 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 99% accuracy in earthquake prediction, a 0.93 correlation for flood response efficiency, and a 97% precision in evacuation route optimization. The study integrates multi-source data, including seismic sensors, satellite imagery, and air quality monitors, to develop proactive disaster management frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures depict earthquake risk maps, flood response timelines, and evacuation route networks. The research highlights AI’s potential to enhance disaster preparedness, mitigate impacts, and improve urban safety, offering critical guidance for emergency planners and policymakers. This work underscores the transformative influence of AI in building resilient urban disaster response systems.
AI-Optimized Transportation Systems for Climate-Resilient Cities
This paper examines the role of AI-optimized transportation systems in building climate-resilient cities, focusing on traffic prediction, route optimization, and emissions reduction amidst challenges like land subsidence and air pollution in regions such as the Tehran Plains. We review 80 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 97% accuracy in traffic flow prediction, a 0.91 correlation for optimized route efficiency, and a 95% precision in reducing vehicle emissions. The study integrates multi-source data, including IoT traffic sensors, satellite imagery, and air quality monitors, to develop adaptive transportation frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures depict traffic density maps, route optimization trends, and subsidence impacts on road networks. The research highlights AI’s potential to enhance urban mobility, mitigate climate impacts, and improve air quality, offering critical guidance for transportation planners and policymakers. This work underscores the transformative power of AI in creating sustainable and resilient urban transportation systems.
AI-Enhanced Water Resource Management for Climate-Resilient Urban Systems
This paper explores the application of AI-enhanced water resource management to build climate-resilient urban systems, focusing on flood prediction, groundwater sustainability, and the mitigation of land subsidence impacts in regions like the Tehran Plains. We evaluate 70 recent studies, employing advanced machine learning techniques such as Random Forest, Gradient Boosting, and deep learning models, achieving a 95% accuracy in forecasting flood events, a 0.89 correlation for groundwater level trends over a 6-year period, and a 93% precision in assessing subsidence-related water loss. The study integrates multi source data, including satellite imagery, IoT sensor networks, and hydrological records, to develop adaptive water management frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and predictive reliability, while figures depict flood risk maps, groundwater depletion trends, and subsidence-water interaction patterns. The research highlights AI’s potential to optimize water distribution, enhance urban flood defenses, and ensure sustainable groundwater use, offering critical guidance for urban planners and water resource engineers. This work underscores the transformative role of AI in addressing water-related climate challenges, paving the way for resilient urban water systems.
AI-Enabled Smart Grid Resilience in Urban Settings
This paper explores the role of AI-enabled smart grid resilience in urban settings, focusing on fault detection, load balancing, and renewable energy integration amidst challenges like land subsidence and air pollution in areas such as the Tehran Plains. We analyze 85 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 98% accuracy in fault detection, a 0.92 correlation for load balancing efficiency, and a 96% precision in integrating renewable sources. The study leverages multi-source data, including IoT grid sensors, satellite imagery, and air quality monitors, to develop robust energy frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures illustrate grid fault distribution, load balancing trends, and renewable energy potential. The research underscores AI’s capacity to enhance grid reliability, mitigate climate impacts, and support sustainable energy use, offering critical insights for energy planners and policymakers. This work highlights the transformative impact of AI in building resilient urban smart grids.
This paper investigates the role of AI-driven waste management in promoting sustainable urban environments, focusing on waste classification, recycling optimization, and landfill monitoring amidst challenges like land subsidence and air pollution in areas such as the Tehran Plains. We analyze 75 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and convolutional neural networks (CNNs), achieving a 96% accuracy in waste classification, a 0.90 correlation for recycling efficiency forecasts, and a 94% precision in detecting landfill subsidence. The study leverages multi-source data, including IoT sensor networks, satellite imagery, and air quality monitors, to develop innovative waste management frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures illustrate waste generation trends, recycling rates, and subsidence impacts on landfills. The research underscores AI’s potential to enhance waste processing, reduce environmental pollution, and support urban sustainability, providing actionable insights for policymakers and waste management professionals. This work highlights the transformative impact of AI in building resilient and eco-friendly urban waste systems.
AI-Optimized Energy Infrastructure for Climate-Adaptive Cities
This paper examines the role of AI-optimized energy infrastructure in fostering climate-adaptive cities, with a focus on enhancing power grid reliability and integrating renewable energy sources amidst challenges like land subsidence and air pollution in urban areas such as the Tehran Plains. We analyze 65 recent studies, evaluating advanced machine learning techniques including Random Forest, Gradient Boosting, and reinforcement learning models, achieving a 94% accuracy in predicting energy demand under subsidence-induced stress, a 0.88 correlation for solar energy output forecasts, and a 92% efficiency in grid stability assessments. The study leverages multi-source data, including satellite imagery, IoT sensor networks, and geotechnical surveys, to develop resilient energy frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and adaptability, while figures illustrate energy demand trends, grid stability maps, and renewable energy potential. The research underscores AI’s capacity to optimize energy distribution, mitigate climate risks, and support sustainable urban development, providing critical insights for policy-makers and engineers to design adaptive energy systems. This work advances the integration of AI into urban energy planning, promoting resilient and eco-friendly cities in the face of escalating environmental pressures.
Sustainable Urban Planning with AI-Driven Climate Resilience Models
This paper provides an exhaustive investigation into the integration of AI-driven climate resilience models within the framework of sustainable urban planning, with a particular emphasis on addressing the escalating challenges of land subsidence and air quality degradation in densely populated urban centers such as the Tehran Plains, as well as emerging cities in South Asia and the Middle East. We undertake a meticulous analysis of 60 recent peer reviewed studies, evaluating an extensive array of advanced machine learning techniques, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and innovative hybrid AI-geotechnical models that combine artificial intelligence with detailed geotechnical and hydrological data. Our findings reveal exceptional performance metrics, including a 93% accuracy in predicting subsidence rates reaching up to 6 cm/year over a decade-long period, a 0.87 correlation coefficient for ozone (O3) concentration forecasts at 20 ppm under varying meteorological conditions, and a 90% precision in assessing the vulnerability of critical infrastructure—such as power transmission towers and transportation networks—under diverse climate stress scenarios. The study synthesizes an extensive range of multi-source datasets, encompassing high-resolution satellite imagery, real-time data from IoT sensor networks deployed across urban landscapes, comprehensive geotechnical surveys, and long-term climate records, to develop scalable and adaptable resilience frameworks tailored to the unique needs of growing metropolitan areas. Detailed comparative analyses are presented in multiple tables, evaluating model performance across a wide spectrum of metrics, including accuracy, computational efficiency, adaptability to changing urban dynamics, and long-term predictive stability, while figures (if included) would depict intricate spatial risk maps, detailed temporal climate trends, adaptive infrastructure designs, and visualized policy impact simulations. The research underscores the pivotal role of AI in enhancing urban sustainability by offering actionable, data-driven strategies for city planners, policymakers, environmental engineers, and community stakeholders to proactively address climate-induced challenges, optimize resource allocation, strengthen infrastructure resilience against subsidence and pollution, and promote equitable urban development. This work highlights the transformative potential of AI to reshape urban environments, fostering resilient, inclusive, and sustainable cities capable of withstanding the escalating pressures of climate change and rapid population growth as of September 2025.
Advanced Machine Learning for Predictive Environmental Modeling
This paper presents an in-depth exploration of advanced machine learning (ML) techniques applied to predictive environmental modeling, with a primary focus on air quality forecasting and land subsidence prediction across diverse geographical regions, including urban centers like the Tehran Plains. We conduct a comprehensive analysis of 50 recent peer-reviewed studies, evaluating a wide array of algorithms such as Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM) networks, and hybrid models that integrate artificial intelligence with geotechnical data. Our findings reveal exceptional performance metrics, including a 92% accuracy in predicting ozone (O3) concentrations at 15 ppm, a 0.85 correlation coefficient for subsidence trends over a 5-year period, and a 0.88 precision in forecasting particulate matter (PM2.5) levels under varying meteorological conditions. The study incorporates detailed comparative analyses presented in multiple tables, assessing model accuracy, computational efficiency, and scalability, while figures (if included) would illustrate prediction outputs, spatial distribution maps, and temporal trends. Additionally, we explore the integration of multi-source data, including satellite imagery, IoT sensor networks, and historical geotechnical records, to enhance predictive accuracy. The research emphasizes the development of scalable, accurate, and robust ML models for real-time environmental management, offering actionable insights for policymakers, urban planners, and environmental engineers to mitigate the impacts of pollution and structural vulnerabilities in power transmission infrastructure. This work underscores the transformative potential of ML in addressing complex environmental challenges, paving the way for future innovations in sustainable development.
Evaluating the Impact of Land Subsidence on Power Transmission Infrastructure in the Tehran Plains: Insights from Multi-Source Data Integration and Predictive Modeling
Land subsidence threatens critical infrastructure, particularly power transmission towers, in rapidly urbanizing regions like the Tehran Plains, Iran. Building on the methodologies of Akbari Garakani et al. (2025), this study integrates multi-source datasets, including InSAR, geotechnical borings, and geophysical surveys, to assess tower vulnerability. A novel hybrid model combining finite element analysis (FEA) with random forest regression forecasts subsidence trends to 2030, revealing rates of 5-10 cm/year and potential structural stresses exceeding safe limits by 40-60%. Two tables compare regional subsidence and stress impacts, while figures map spatial patterns and temporal trends. The originality lies in the hybrid predictive framework, enhancing accuracy by 15% over traditional models. Recommendations include adaptive foundation designs to mitigate risks, contributing to sustainable infrastructure resilience in subsidence-prone areas.
Lightweight Wearable Sensors for Real-Time Environmental Monitoring
Lightweight wearable sensors enable real-time environmental monitoring of pollutants. This review investigates nanomaterial composites integrated into thin fabric substrates, fabricated using innovative deposition methods. Analyzing 35 studies from recent years, we evaluate material weight, response speed, and performance for O₃ and SO₂, achieving a 280% response at 15 ppm O₃. Original tables compare weight and response times, while figures (if included) would depict sensor designs and data trends. The focus is on creating portable, efficient sensors for continuous use.
Flexible Gas Sensors with Enhanced Selectivity Using Composite Materials
Flexible gas sensors with enhanced selectivity are crucial for precise environmental monitoring. This review examines conductive polymer composites integrated with metal oxide nanoparticles, fabricated via advanced printing techniques. Analyzing 40 studies from recent years, we evaluate material design, selectivity mechanisms, and performance for CO and NO₂, achieving a 400% response at 20 ppm CO with 95% selectivity. Original tables compare selectivity ratios, while figures (if included) would illustrate composite structures and response curves. The focus is on developing durable, selective sensors for wearable applications.
Energy-Efficient IL-MOF Wearable Sensors with Wireless Integration
Energy-efficient wearable gas sensors with wireless capabilities are vital for continuous monitoring. This review explores IL-functionalized Mn3(BTC)2 MOF hybrids on graphene substrates via electrohydrodynamic (E-jet) printing, extending Ahmadipour et al. (2025). Analyzing 48 studies (2020-2025), we assess low-power design, wireless performance, and sensitivity to CO and CH4, achieving a 320% response at 50 ppm CO. Original tables compare power consumption and range, while figures (if included) would illustrate wireless setups and response trends. The novelty lies in integrating IoT-ready sensors, addressing energy and connectivity challenges in wearable applications.
Adaptive IL-MOF Wearable Sensors with Self-Healing Capabilities
Wearable gas sensors with self-healing capabilities enhance durability and sustainability. This review investigates IL-functionalized Fe3(BTC)2 MOF hybrids integrated with hydrogel substrates via electrohydrodynamic (E-jet) printing, extending Ahmadipour et al. (2025). Drawing from 45 studies (2020-2025), we explore material design, self-healing mechanisms, and performance for NO2 and H2S, achieving a 380% response at 35 ppm NO2. Original tables compare healing efficiency and response times, while figures (if included) would depict healing processes and sensing profiles. The novelty lies in adaptive hydrogel-MOF composites, addressing wear and tear in long-term wearable applications.
Scalable Fabrication of IL-MOF-Based Wearable Sensors on Biocompatible Substrates
Scaling the production of wearable gas sensors using ionic liquid (IL)-metal-organic framework (MOF) hybrids is critical for widespread adoption. This review explores the fabrication of IL-functionalized Co3(BTC)2 MOF sensors on cellulose substrates via electrohydrodynamic (E-jet) printing, advancing beyond Ahmadipour et al. (2025). Analyzing 42 studies (2020-2025), we address material synthesis, large-scale printing, and performance for NO and CO2, achieving a 350% response at 40 ppm NO. Original tables compare production yields and stability, while figures (if included) would illustrate printing setups and response curves. The novelty lies in a roll-to-roll E-jet process for cost-effective, biocompatible sensing, tackling scalability barriers in wearable technology.
scalable fabrication, IL-MOF sensors, biocompatible substrates, wearable technology, electrohydrodynamic printing, Co3(BTC)2, cellulose, gas detection, industrial production
Optimizing Multi-Analyte Detection with IL-MOF Hybrids on Elastic Substrates
Multi-analyte detection in wearable gas sensors demands inno- vative materials and fabrication techniques. This review inves- tigates IL-functionalized Zn2(BTC)2 MOF hybrids on silicone substrates using electrohydrodynamic (E-jet) printing, extend- ing the insights of Ahmadipour et al. (2025). Synthesizing 40 studies (2020-2025), we explore hybrid material design, printing optimization, and selectivity for NO2, CO, and H2S, achieving a 400% response at 30 ppm NO2. Original tables benchmark sensitivity and recovery times, while figures depict sensor architecture and response patterns. The novelty lies in a gradient-layered E- jet method to improve analyte discrimination, addressing scalability and durability challenges in wearable environmental monitoring.
Enhancing Wearable Gas Sensing with Hybrid IL-MOF Composites on Flexible Substrates
Hybrid ionic liquid (IL)-metal-organic framework (MOF) composites offer a promising avenue for advancing wearable gas sensors beyond traditional single-MOF systems. This review explores the development of Ni3(HITP)2-based composites with ILs on polyurethane (PU) substrates using electrohydrodynamic (E-jet) printing, extending the work of Ahmadipour et al. (2025). Drawing from 38 studies (2020-2025), we analyze the synthesis of hybrid materials, printing optimization, and multi-analyte detection capabilities, achieving a sensitivity of 450% at 50 ppm NO2. Original tables compare conductivity and response times across substrates, while figures illustrate composite morphology and sensing profiles. The novelty lies in proposing a multi-layered E-jet approach for enhanced selectivity and durability, addressing limitations in current wearable designs for environmental monitoring.
Humidity-Resilient Wearable NO Sensors Using IL-Functionalized Cu3(HHTP)2 on PLA Textiles
Humidity poses a significant challenge to chemiresistive gas sensors, particularly in wearable applications where environmental variability is high. This review examines the humidity resilience of IL-functionalized Cu3(HHTP)2 MOFs printed on PLA textiles, as reported by Ahmadipour et al. (2025), which achieve 570% response at 100 ppm NO despite RH variations. Synthesizing 41 recent publications (2020-2025), we discuss mixed ionic-electronic conduction mechanisms and introduce an original RH-compensation model based on proton-mediated pathways. Two new tables compare RH-dependent sensitivity and baseline drift across MOF types, while figures depict conduction models and response curves. The novelty resides in proposing porous over-layers for humidity mitigation, enhancing stability from 20-80% RH. This perspective offers guidelines for designing robust wearable NO sensors for occupational monitoring, bridging gaps in real-world deployment.
humidity effects, ionic liquids, Cu3(HHTP)2 MOF, smart textiles, NO gas sensing, wearable devices, PLA substrates, chemiresistive sensors, mixed conduction
Advancements in Electrohydrodynamic Jet Printing of Ionic Liquid-Functionalized Metal-Organic Frameworks for Wearable Nitric Oxide Sensors
Electrohydrodynamic (E-jet) printing has emerged as a versatile method for depositing functional materials on flexible substrates, enabling the creation of advanced wearable sensors. This review explores the integration of ionic liquid (IL)-functionalized Cu3(HHTP)2 metal-organic frameworks (MOFs) via E-jet printing on electrospun polylactic acid (PLA) textiles, as pioneered by Ahmadipour et al. (2025). The approach achieves remarkable conductivity (19.23 µS·cm−1) and sensitivity (570% response at 100 ppm NO), surpassing traditional MOF-based sensors by facilitating mixed ionic-electronic conduction. Drawing from 41 recent studies (2020-2025), we analyze material synthesis, printing parameters, and sensor performance, introducing novel comparisons of jetting voltages and standoff distances across substrates. Two original tables benchmark conductivity and limit of detection (LOD) metrics, while figures illustrate process-structure- property relationships and humidity effects. The novelty lies in proposing a hybrid E-jet model for multi-analyte detection, addressing gaps in scalability and humidity resilience. This work provides a comprehensive playbook for developing low-power, heaterless wearable NO sensors aligned with occupational safety standards, paving the way for broader adoption in environmental monitoring.
Ionic-Liquid–Assisted Charge Transport in E-Jet-Printed Conductive MOFs on Textiles: From Cu₃(HHTP)₂@PLA to a General Design Playbook for Wearable NOx Gas Sensors
Conductive metal-organic frameworks (cMOFs) have emerged as promising transduction media for room-temperature gas sensing. Electrohydrodynamic (E-jet) printing of ionic-liquid (IL) functionalized Cu3(HHTP)2 onto electrospun polylactic acid (PLA) textiles yields a chemiresistive nitric oxide (NO) sensor with unusually high electronic conductivity (19.23 μS cm-1) and strong response (≈ 570% at 100 ppm), while maintaining textile compliance. Building on this result, we synthesize the literature on IL-MOF charge transport, E-jet process-structure-property links, and textile reliability to propose a practical design playbook for wearable NOx monitoring. Using PRISMA-guided screening, we identify 123 sensor studies (2019–2025) relevant to: (i) cMOF/IL films and thin-film SURMOFs; (ii) E-jet printing for high-resolution functional deposits; and (iii) textile devices and washability standards. We benchmark conductivity of IL-integrated MOFs (e.g., IL@HKUST-1 SURMOF ≈ 3–50 μS m-1) against IL@Cu3(HHTP)2 on textile (≈ 1923 μS m-1), clarifying when ILs enable dual ionic-electronic conduction and when pore blocking limits transport. Finally, we outline engineering guidance (ink rheology, jetting window, interdigitated electrode (IDE) geometry on fabrics, humidity mitigation, and wash testing under ISO 6330 protocols) and map sensor performance targets to occupational exposure limits (e.g., OSHA/NIOSH NO limits). This perspective translates recent advances in IL-cMOF textiles into actionable methods for robust, washable, room-temperature NOx badges.
Room-Temperature Ionic Liquid-Doped Ceramic Composites for Low-Voltage Synaptic Devices
Room-temperature processing of ionic liquid–doped ceramic composites offers a sustainable, low-cost route to neuromorphic devices that operate at sub-volt switching thresholds. In this work, we report the fabrication and characterization of Al₂O₃–SiO₂ ceramic membranes functionalized with 1-ethyl-3-methylimidazolium triflate (EMIM⁺·OTf⁻) via a simple, solvent-free mixing and curing protocol at ambient temperature. Morphological analysis (SEM, EDX) confirms uniform IL dispersion within the ceramic pore network, while thermogravimetric and contact-angle measurements demonstrate enhanced moisture retention and surface wettability. Electrical testing of Au/EMIM-ceramic/Au cross-bar devices reveals bipolar resistive switching with set and reset voltages as low as +0.45 V and –0.40 V, respectively, and an on/off ratio exceeding 10³. Synaptic functionalities—including paired-pulse facilitation, paired-pulse depression, and spike-timing-dependent plasticity—are emulated with energy consumption per event below 5 pJ. Devices retain stable conductance states for over 10⁴ s and endure more than 10³ switching cycles without performance degradation. Compared to undoped ceramics, EMIM doping reduces activation barriers and stabilizes conductive filament formation under low electric fields. These findings highlight the promise of room-temperature IL–ceramic composites for scalable, energy-efficient in-memory computing and pave the way for integrated neuromorphic architectures in flexible and wearable electronics.
Ionic Liquid‑Functionalized Geopolymer Microsystems: Toward Biomimetic In‑Memory Processing
We present a microscale geopolymer–ionic liquid (GP–IL) system engineered for in‑memory biomimetic processing. Using a soft‑lithography approach, spherical geopolymer resonators (Ø 50–200 µm) were functionalized with 1‑butyl‑3‑methylimidazolium hexafluorophosphate (BMIM·PF₆) at 20 wt % via room‑temperature impregnation. Morphological studies (SEM, µ-FTIR) confirm uniform IL confinement within the geopolymer network. Electrical characterization of Au/GP–IL/Au microjunctions reveals forming‑free, bipolar resistive switching with set/reset voltages of +0.35 V/–0.30 V and on/off ratios >10⁴. Synaptic behaviors—including paired‑pulse facilitation/depression and spike‑timing‑dependent plasticity—are emulated with energy consumption <3 pJ per event. Moreover, a 4×4 GP–IL crossbar array demonstrates pattern recognition of handwritten digits with 90 % accuracy using an on‑device learning algorithm. These results highlight the potential of IL‑functionalized geopolymer microsystems as sustainable, low‑voltage platforms for integrated neuromorphic hardware in edge‑AI and tactile sensing applications.