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Quizzing: un’attività di orientamento per un approccio efficace ai quesiti a risposta multipla
No full textIn questo intervento presentiamo un’attività di orientamento promossa dall’Università Politecnica delle Marche nell’ambito del PNRR e rivolta a studenti di scuola superiore. L’attività, che integra l’uso della tecnologia e le interazioni in presenza, mira ad avere impatto sul livello metacognitivo dell’apprendimento della matematica nella fase di transizione fra scuola secondaria e università
Trade exposure, immigrants, and workplace injuries
No full textWe study the effects of globalization on workplace accidents in the Italian manufacturing sector from 2008 to 2019. We focus on the local intensity of import exposure to China and the share of foreign-born residents. To address potential endogeneity, we instrument import competition using that of other high-income countries and immigration exposure with historical conational settlements. We find that increased import competition worsens workplace safety, especially in provinces with low population density, strong transport networks, limited outside options, and many small firms. The effect is stronger in areas with below-median immigrant shares, suggesting foreign workers may buffer its adverse impact
Functional Proteomics of Quinazolin‐4‐One Derivatives Targeting the Proteasome
No full textQuinazolinones have been recently recognized as valuable scaffolds for developing novel therapeutic opportunities. They indeed exhibit structural versatility and a wide range of biological activities, including antifungal, antitubercular, antihypertensive, anticancer, and antiviral ones. In this work, a focused library of new bioactive 4-(3-H)-quinazolinones has been synthesized, their cytotoxic action against DU-145 prostate cancer cells has been detailed, and compound 4k has been revealed as the most active one. Consequently, its interactome has been characterized by a label-free functional proteomics-based platform coupling drug affinity responsive target stability (DARTS) and targeted limited proteolysis-multiple reaction monitoring-mass spectrometry (t-LiP-MRM-MS). This multifaced strategy has been employed to reveal few subunits of the 26S proteasome machinery as the most reliable compound 4k biological targets. This paved the way for the deepening of the protein-ligand interaction using in vitro and in silico bio-orthogonal techniques. Finally, the analysis of its function in living DU-145 cells prompted compound 4k as a novel quinazolinone-bearing inhibitor of the chymotrypsin-like activity of the proteasomal β-5 subunit, stirring this framework for the development of new anticancer drugs
Valutazione dei cambiamenti dermoscopici dopo trattamento con complessi ibridi cooperativi di acido ialuronico nelle donne affette da lichen sclerosus vulvare: studio osservazionale prospettico
No full textBackground: In the literature, there are only preliminary studies evaluating the clinical effect of hybrid cooperative complexes of Hyaluronic acid (HCC) in patients affected by vulvar lichen sclerosus (VLS), showing an improvement in the quality of life, but none of these considered the dermatoscopic changes that occurred after HCC treatment.
Objective: The primary aim of our study is the improvement of baseline dermatoscopic parameters 6 months after regenerative therapy with HCC. In addition, clinical and ultrasound parameters were also evaluated.
Methods: a prospective observational cohort study was performed, patients with VLS who suspended topical steroid therapy were included. Two HCC intradermal injection of 2 ml of HCC were performed one month apart. Patients were evaluated at baseline (before HCC injections), 1, 3, and 6 months after the last HCC injection and clinical assessment, evaluation in digital dermatoscopy, skin ultrasound and administration of the validated Dermatology Life Quality Index (DLQI) and Female Sexual Functioning Index (FSFI) questionnaires were performed.
Results: A total of 25 patients affected by VLS were included in the study and completed the 6 months of follow-up. Considering the p-for-trend we found a statistically significant reduction at 6 months for sclerosis, leukoderma, hyperkeratosis, itching, burning, pain (p<0.001) and purpuric lesions (p=0.006). As regards quality of life, a significant improvement in DLQI and FSFI was observed. Considering dermatoscopic assessment we found a statistically significant reduction at 6 months for vascularization, whitish plaques without structure, purpuric lesions, comedo-like outlets, horny pearls, scales, ice silvers structures, whitish background (p<0.001), and gray-blue dots (p=0.010). Considering ultrasound assessment, we found a significant improvement in the thickness of hypoechoic band, the epidermal morphology irregularity, and the non-homogeneity of the hypoechoic band.
Conclusion: this prospective observational study demonstrated a significant improvement in dermatoscopic, ultrasound and clinical parameters after HCC injection therapy in patients with VLS
A Novel Image Processing and Machine Learning Approach for Wood Pellet Size Classification Using Shadow Analysis
No full textThe growing importance of wood pellets in renewable energy highlights the need for efficient, scalable methods of quality assessment. Current standards rely on manual caliper measurements, which are time-consuming and poorly capture variability across samples. This study introduces a patent-pending imaging approach that classifies pellet dimensions from shadow features. A prototype system was developed combining controlled lighting, a camera, and computational processing. Shadow characteristics were analyzed statistically and linked to pellet dimensions using machine learning models. Results show that illumination geometry strongly influences classification, with the best performance reaching 71 % accuracy. These findings demonstrate the feasibility of shadow-based imaging as a rapid, non-invasive alternative to manual measurement, with promising applications in pellet production, storage, and combustion systems
Cisplatin as a Xenobiotic Agent: Molecular Mechanisms of Actions and Clinical Applications in Oncology
No full textCisplatin, a platinum-based compound, is a cornerstone of modern chemotherapy and remains widely used against a variety of solid tumors, including testicular, ovarian, lung, bladder, and head and neck cancers. Its anticancer activity is primarily attributed to the formation of DNA crosslinks, which obstruct replication and repair, ultimately leading to apoptosis. However, the clinical value of cisplatin is constrained by two major challenges: its toxic profile and the development of resistance. Cisplatin toxicity arises from its interaction not only with tumor DNA but also with proteins and nucleic acids in healthy tissues, resulting in a range of adverse effects, including, but not limited to, nephrotoxicity, ototoxicity, neurotoxicity, and gastrointestinal injury. In pediatric patients, permanent hearing loss represents a particularly debilitating complication. On the other hand, tumor cells can evade cisplatin cytotoxicity through diverse mechanisms, including reduced intracellular drug accumulation, enhanced DNA repair, detoxification by thiol-containing molecules, and alterations in apoptotic signaling. These resistance pathways severely compromise treatment outcomes and often necessitate alternative or combination strategies. This review examines the chemical structure of cisplatin, the molecular mechanisms of cisplatin cytotoxicity and cisplatin-induced resistance, as well as the main applications in cancer management and the complications associated with its clinical use
Myoelectric control for locomotion. Restoration: from methodological innovation into the clinical application
No full textRipristinare una mobilità naturale per gli individui che utilizzano dispositivi protesici e assistivi per gli arti inferiori rimane una sfida centrale nell'interazione uomo-macchina. Un controllo affidabile richiede la decodifica accurata e in tempo reale dell'intenzione dell'utente a partire da segnali neurofisiologici, eppure i sistemi attuali sono limitati da configurazioni di sensori poco pratiche, spesso incompatibili con gli amputati transfemorali (TF), e da modelli di apprendimento profondo che richiedono dati e risorse computazionali estese. Di conseguenza, il campo di ricerca manca di strategie di sensing minime e prossimali e di algoritmi di decodifica efficienti adatti per un'implementazione embedded e nel mondo reale. Questa tesi affronta queste lacune attraverso due pilastri complementari. Il primo pilastro stabilisce un paradigma di sensing neuromecchanico minimo e prossimale. I Capitoli 2-5 dimostrano che è possibile ottenere una stima accurata della cinematica di caviglia e ginocchio, delle forze di reazione verticale del terreno e delle fasi del passo utilizzando solo pochi sensori posizionati sopra il ginocchio. Questi risultati mostrano che le configurazioni minime e compatibili con amputati TF possono supportare molteplici obiettivi di controllo e spianano la strada verso interfacce protesiche unificate e indossabili per l'arto inferiore. Il secondo pilastro sviluppa algoritmi di decodifica computazionalmente efficienti e consapevoli del contesto per un uso pratico e in tempo reale. I Capitoli 6-8 introducono PHASORS, TAP, DAS, Myoelectric Temporal Patching (MTP) e WaveLSTM, che sono framework di estrazione di caratteristiche spaziali, temporali e spaziotemporali in grado di catturare la dinamica neuromuscolare con alta precisione ma con una complessità di gran lunga inferiore rispetto ai modelli di apprendimento profondo. WaveLSTM offre un'ulteriore alternativa spaziotemporale, mentre la convalida in tempo reale utilizzando un braccialetto Myo a basso costo ne conferma l'effettiva implementabilità. Il Capitolo 9 migliora ulteriormente la sicurezza del controllo incorporando la biomeccanica del passo attraverso un metodo di fusione bayesiana informata dalla fisica, migliorando la stabilità ed eliminando transizioni improvvise e indesiderate. Il Capitolo 10 sintetizza questi contributi, mostrando come il sensing minimo, l'ingegneria delle caratteristiche informata dal contesto e i priori biomeccanici abilitino collettivamente un controllo protesico guidato dall'intenzione, scalabile, a basso consumo e clinicamente valido. Supportato da test clinici su amputati TF e dal rilascio di dataset e strumenti open-access, questo lavoro getta le basi per tecnologie protesiche di prossima generazione che siano indossabili, robuste e pronte per la traduzione nel mondo reale.Restoring natural mobility for individuals using lower-limb assistive and prosthetic devices remains a central challenge in human–machine interaction. Reliable control requires accurate, real-time decoding of user intent from neurophysiological signals, yet current systems are limited by impractical sensor configurations, often incompatible with transfemoral (TF) amputees, and by deep learning models that demand extensive data and computational resources. As a result, the field lacks minimal, proximal sensing strategies and efficient decoding algorithms suitable for embedded, real-world deployment. This thesis addresses these gaps through two complementary pillars. The first pillar establishes a minimal, proximal neuromechanical sensing paradigm. Chapters 2–5 demonstrate that accurate estimation of ankle and knee kinematics, vertical ground reaction forces, and gait phases can be achieved using only a few above-knee sensors. These findings show that minimal and TF-compatible setups can support multiple control objectives and pave the way toward unified, wearable lower-limb prosthetic interfaces. The second pillar develops computationally efficient, context-aware decoding algorithms for practical, real-time use. Chapters 6–8 introduce PHASORS, TAP, DAS, and Myoelectric Temporal Patching (MTP), WaveLSTM, that are spatial, temporal, and spatiotemporal feature extraction frameworks that capture neuromuscular dynamics with high accuracy but far lower complexity than deep learning models. WaveLSTM offers an additional spatiotemporal alternative, while real-time validation using a low-cost Myo armband confirms deployability. Chapter 9 further enhances control safety by incorporating gait biomechanics through a physics-informed Bayesian fusion method, improving stability and eliminating sudden, undesired transitions. Chapter 10 synthesizes these contributions, showing how minimal sensing, context-informed feature engineering, and biomechanical priors collectively enable scalable, low-power, and clinically viable intention-driven prosthetic control. Supported by clinical testing in TF amputees and the release of open-access datasets and tools, this work lays the foundation for next-generation prosthetic technologies that are wearable, robust, and ready for real-world translation
DETECTION OF MICROPLASTICS IN FISH USING COMPUTED TOMOGRAPHY AND DEEP LEARNING
No full textLa contaminazione da microplastiche (MPs) è emersa come una preoccupazione ambientale e sanitaria sempre più diffusa, con MPs rilevate negli ecosistemi marini, nei sistemi di acqua dolce, nei sedimenti, nei suoli e persino in alimenti come frutti di mare, birra, sardine in scatola, miele e acqua potabile. Questi risultati evidenziano l’inevitabilità dell’esposizione umana attraverso ingestione, inalazione e contatto dermico, con stime recenti che suggeriscono un’assunzione annuale di 39.000–52.000 particelle di plastica per persona, probabilmente sottostimata se si considera la deposizione aerea durante i pasti.
Le tecniche analitiche convenzionali per il rilevamento delle MPs in matrici biologiche, come la pirolisi-GC/MS e i metodi spettroscopici (FTIR, Raman), sono ampiamente utilizzate ma presentano limiti critici: richiedono preparazioni distruttive del campione, sono estremamente dispendiose in termini di tempo (spesso 3–4 giorni per campione) e non preservano le informazioni spaziali sulla localizzazione delle MPs nei tessuti. Questi vincoli ostacolano valutazioni tossicologiche complete e aumentano il rischio di contaminazione crociata durante la lavorazione. Di conseguenza, è urgente disporre di metodologie non distruttive, rapide e affidabili, in grado di rilevare MPs in campioni biologici complessi mantenendo l’integrità posizionale.
I recenti progressi nell’imaging e nell’intelligenza artificiale (AI) offrono soluzioni promettenti a queste sfide. La tomografia computerizzata (CT) e la microCT hanno mostrato potenziale per il rilevamento non invasivo delle MPs in sedimenti e suoli; tuttavia, la loro applicazione ai campioni biologici è stata limitata fino a poco tempo fa. La microCT è recentemente emersa come un nuovo metodo per il rilevamento delle MPs: è stata applicata per la prima volta in pesci commerciali e successivamente nello zebrafish, consentendo di ottenere immagini tridimensionali delle MPs senza necessità di preparazioni distruttive. In modo significativo, la microCT ha preservato l’integrità dei tessuti, ridotto il rischio di contaminazione e permesso la mappatura spaziale dei siti di accumulo, fondamentale per correlare la presenza di MPs con potenziali effetti tossicologici.
Sulla base di questi sviluppi, è stata proposta una metodologia automatizzata basata su CT, combinata con deep learning, per il rilevamento delle MPs nei campioni ittici. Questo approccio ha mostrato diversi vantaggi rispetto alle tecniche tradizionali: localizzazione accurata delle MPs, riduzione del rischio di contaminazione, rapidità di elaborazione (poche ore contro diversi giorni) e scalabilità a volumi di campione maggiori (fino a 100 cm3) a un costo relativamente basso (~150 € per campione). Il modello di segmentazione semantica ha ottenuto un rilevamento quasi perfetto delle MPs inoculate, evidenziando il potenziale dell’automazione basata su AI nel superare i limiti dell’analisi spettrale manuale. Allo stesso modo, è stata sottolineata l’importanza della quantificazione dell’incertezza nei flussi di lavoro CT e reti neurali, identificando fonti chiave di errore come la risoluzione dei voxel, il rumore dell’immagine e la variabilità algoritmica, e proponendo strategie di soglia adattiva e calibrazione per mitigare falsi positivi e falsi negativi. Questi risultati mostrano che una gestione robusta dell’incertezza è essenziale per garantire l’affidabilità nel rilevamento delle MPs assistito da AI.
L’integrazione dell’AI con l’imaging CT rappresenta un passo trasformativo nella ricerca sulle MPs. A differenza della spettroscopia FTIR e Raman, che richiede preparazioni estese e risulta sensibile ad additivi o modifiche superficiali, la segmentazione basata su deep learning può sfruttare informazioni multifattoriali (densità, forma, contesto spaziale) per identificare accuratamente le MPs senza processi distruttivi. Questa automazione riduce significativamente la dipendenza dall’operatore e consente analisi ad alto throughput, aprendo la strada a applicazioni di monitoraggio in tempo reale nella sicurezza alimentare e nella valutazione ambientale. Inoltre, la metodologia proposta offre un rapporto costo-beneficio favorevole, combinando acquisizione rapida e segmentazione automatizzata con un fabbisogno minimo di manodopera, affrontando così i principali colli di bottiglia dei metodi convenzionali.
In conclusione, la convergenza tra imaging CT e segmentazione guidata da AI offre una soluzione robusta, non distruttiva ed economicamente vantaggiosa per il rilevamento delle MPs nei campioni biologici. Consentendo analisi rapide, automatizzate e spazialmente risolte, questa metodologia supera i limiti critici dei flussi di lavoro tradizionali e apre nuove prospettive per studi tossicologici, valutazioni del rischio e controllo della qualità alimentare. Sarà essenziale continuare a migliorare i limiti di risoluzione, gestire l’incertezza e ampliare l’applicabilità a matrici diverse per sfruttare appieno il potenziale di questo approccio nella ricerca ambientale e sanitaria. Questa strategia integrata non solo accelera il rilevamento delle MPs, ma fornisce anche una base per future innovazioni volte a mitigare gli impatti ecologici e sanitari dell’inquinamento da plastica.Microplastics (MPs) contamination has emerged as a pervasive environmental and public health concern, with MPs detected in marine ecosystems, freshwater systems, sediments, soils, and even in food items such as seafood, beer, canned sardines, honey, and drinking water. These findings highlight the inevitability of human exposure through ingestion, inhalation, and dermal contact, with recent estimates suggesting an annual intake of 39,000 - 52,000 plastic particles per person, likely underestimated when considering airborne deposition during meals. Conventional analytical techniques for MPs detection in biological matrices, such as pyrolysis-GC/MS and spectroscopic methods (FTIR, Raman), are widely used but suffer from critical limitations: they require destructive sample preparation, are highly time-consuming (often 3 - 4 days per sample), and fail to preserve spatial information regarding MPs localization within tissues. These constraints hinder comprehensive toxicological assessments and increase the risk of cross-contamination during processing. Consequently, there is an urgent need for non-destructive, rapid, and reliable methodologies capable of detecting MPs in complex biological samples while maintaining positional integrity.
Recent advances in imaging and artificial intelligence (AI) offer promising solutions to these challenges. Computed tomography (CT) and microCT have demonstrated potential for non-invasive MPs detection in sediments and soils; however, their application to biological samples has been limited until recently. MicroCT has recently emerged as a novel method for detecting MPs. It was first applied in commercial fish and later in zebrafish, allowing researchers to obtain three-dimensional images of MPs without the need for destructive sample preparation. Importantly, microCT preserved tissue integrity, minimized contamination risk, and allowed for spatial mapping of MPs accumulation sites, critical for correlating MPs presence with potential toxicological effects. Building on these developments, an automated CT-based methodology has been proposed, combined with deep learning for MPs detection in fish samples. This approach demonstrated several advantages over traditional techniques: accurate localization of MPs, reduced contamination risk, rapid processing (a few hours versus several days), and scalability to larger sample volumes (up to 100 cm3) at a relatively low cost (~€150 per sample). The semantic segmentation model achieved near-perfect detection of inoculated MPs, highlighting the potential of AI-driven automation to overcome the limitations of manual spectral analysis. Similarly, the importance of uncertainty quantification in CT and neural network workflows
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has been emphasized, identifying key sources of error, such as voxel resolution, image noise, and algorithmic variability, and proposing adaptive thresholding and calibration strategies to mitigate false positives and false negatives. Their findings underscore that robust uncertainty management is essential for ensuring reliability in AI-assisted MPs detection.
The integration of AI with CT imaging represents a transformative step in MPs research. Unlike FTIR and Raman spectroscopy, which require extensive sample preparation and are sensitive to additives or surface modifications, deep learning-based segmentation can leverage multifactorial information (density, shape, spatial context) to accurately identify MPs without destructive processing. This automation significantly reduces operator dependency and enables high-throughput analysis, paving the way for real-time monitoring applications in food safety and environmental assessment. Furthermore, the proposed methodology offers a favorable cost-benefit ratio, combining rapid acquisition and automated segmentation with minimal labor requirements, thereby addressing critical bottlenecks in conventional workflows.
In conclusion, the convergence of CT imaging and AI-driven segmentation offers a robust, non-destructive, and cost-effective solution for MPs detection in biological samples. By enabling rapid, automated, and spatially resolved analysis, this methodology addresses critical limitations of conventional workflows and opens new avenues for toxicological studies, risk assessment, and food quality control. Continued efforts to refine resolution limits, manage uncertainty, and broaden applicability across diverse matrices will be essential to fully realize its potential in environmental and health research. This integrated approach not only accelerates MPs detection but also provides a foundation for future innovations aimed at mitigating the ecological and health impacts of plastic pollution
Three Essays on Regime-Switching DSGE Models
No full textIn recent years the unstable Macroeconomic and Geopolitical environment has shown the need for different modelling strategies, capable of handling the required flexibility. In this sense the embedding of Regime Switching parameters in standard DSGE models proves to be an attractive avenue for research. The large body of literature produced both for the solution and the estimation of this type of model and the birth of user-friendly instruments to handle them all goes to show the renewed interest in them. This thesis aims to support the transition towards the Regime Switching approach with three main contributions. The first chapter proposes a survey of the main contributions in the applications of Perturbation Methods for solving this type of models. The survey is built around the ordinary methods used for Constant Parameter Models in order to ease the comparison and the adoption for newcomers. The second chapter tries to exploit the foundations built with the first one by employing the RISE Toolbox (Junior Maih 2015) to perform the replication of two existing contributions in Karadi and Nakov 2021 and Foerster 2015. This replication works are done, not only to further develop and strengthen the existing literature of replication exercises, but they also serve as a laboratory to break down the way Markov-Switching models inner working. Moreover, in this chapter we aim at showing the potential of the RISE Toolbox. This software shares many similarities with Dynare, an established benchmark in Macroeconomic modelling, and can be a valid way for many economist to safely approach the RS-DSGE modelling avenue. Finally, in the third chapter we employ the RISE Toolbox and Markov-Switching parameters to change the way occasionally binding constraints are handled in models like the Karadi and Nakov (2021). This contribution aims at showing how the switching parameters can be used, not only, as new way model to model the economy but also as an instrument to re-think already existing models and modelling strategies
Hydrogel Platforms for Water Remediation and Volatile Organic Compound Sensing
No full textQuesta tesi sviluppa piattaforme a base di idrogel per la decontaminazione delle acque reflue e il rilevamento di inquinanti in aria. Nella prima parte è stato sintetizzato un idrogel cationico a base di lignina (LS–pAAm–DAC) e valutato come materiale adsorbente selettivo per farmaci anionici. Il materiale, caratterizzato da struttura porosa, comportamento elastico ed elevato rigonfiamento, rimuove efficacemente diclofenac sodico (DCF-Na) in esperimenti in batch e in colonna a letto impaccato. L’analisi di cinetiche e isoterme conferma un assorbimento controllato dalla diffusione e prestazioni riproducibili in condizioni statiche e dinamiche.
Per combinare adsorbimento e degradazione, il framework metallico-organico MIL-100(Fe) è stato incorporato nell’idrogel ottenendo un composito a doppia funzione per la rimozione del naprossene sodico (NPX-Na). Diffrazione a raggi X e SEM/EDS mostrano che MIL-100(Fe) mantiene la cristallinità ed è distribuito in modo omogeneo nella matrice. Il composito presenta rigonfiamento leggermente ridotto ma maggiore capacità di adsorbimento verso NPX-Na, è rigenerabile per cicli consecutivi di adsorbimento/desorbimento e agisce come catalizzatore foto-Fenton eterogeneo: in presenza di UVA/H2O2 degrada NPX-Na, come confermato dall’identificazione di prodotti di fotodegradazione mediante HPLC/MS.
La seconda parte riguarda il monitoraggio della qualità dell’aria e descrive un sensore fotonico compatto basato su reticoli di diffrazione in trasmissione scritti a laser in un sottile strato idrogel/resina. I reticoli a base di idrogel operano in condizioni ambiente e superano i controlli in sola resina, convertendo variazioni di indice di rifrazione indotte da diversi VOCs in segnali ottici distinti per ampiezza e cinetica. L’integrazione dell’idrogel aumenta la sensibilità del dispositivo, che è a basso consumo, riproducibile e adatto al monitoraggio distribuito di ambienti interni.This thesis develops hydrogel-based platforms for water remediation and gas sensing. First, a cationic lignin-based hydrogel (LS–pAAm–DAC) was synthesized and evaluated as a selective adsorbent for anionic pharmaceuticals. The material exhibits interconnected porosity, elastic behaviour and high swelling, which translate into high diclofenac sodium (DCF-Na) removal in batch and packed-bed column tests. Kinetic and isotherm analyses indicate diffusion-controlled uptake and predominantly monolayer adsorption, confirming reproducible and scalable performance in static and dynamic conditions.
To couple adsorption with degradation, the metal–organic framework MIL-100(Fe) was embedded in the hydrogel to obtain a dual-function composite for naproxen sodium (NPX-Na) removal. XRD and SEM/EDS show that MIL-100(Fe) retains its crystallinity and is uniformly dispersed within the network. The composite displays enhanced NPX-Na adsorption, good regenerability over repeated adsorption/desorption cycles, and acts as a heterogeneous photo-Fenton catalyst. Under UVA/H2O2, it efficiently degrades NPX-Na, as supported by HPLC–MS identification of selected photoproducts.
Finally, the hydrogel was integrated into laser-written transmission diffraction gratings to implement an optical sensor for volatile organic compounds (VOCs). Thin hydrogel/resin gratings operate at ambient conditions and outperform resin-only controls, converting VOC-induced refractive index changes into robust optical signals. Different analytes and doses produce distinct response amplitudes and kinetics, reflecting their physicochemical properties. The proposed platforms demonstrate how hydrogel-based materials can be engineered as versatile tools for contaminant removal and indoor air quality monitoring