Department of Physics, Mathematics and Informatics

The rapid development of the Internet of Things (IoT) and smart industrial systems has created an urgent demand for high-performance gas sensors capable of room-temperature operation with low power consumption. To date, while field-effect transistor (FET) architectures have demonstrated remarkable capabilities in addressing key challenges related to miniaturization, power consumption, and sensitivity, the conventional designs' critical dependence on channel materials for selectivity and stability imposes stringent selection criteria, which significantly limits their application scope. Here, we report an atomically thin all-2D gate-sensitive field-effect transistor (GS-FET) based on an h-BN/MoS2 heterostructure. This design decouples the functions of chemical recognition and charge transport, which not only ensures the stability of the channel material but also allows for flexible tuning of selectivity. Moreover, the atomically thin architecture enables ultrasensitive detection capabilities. Devices with two distinct floating-gate materials (1 nm-thick Ni5Pd95 alloy and Pt) were fabricated, and combined DFT calculations and experimental measurements demonstrate that Ni5Pd95-based and Pt-based devices are optimized for high sensitivity (0.056%/ppm) and fast response/recovery kinetics, respectively. Systematic studies reveal that the thickness-dependent capacitive coupling of the h-BN dielectric provides an additional degree of freedom for sensitivity tuning, while its atomically flat interface minimizes charge scattering and enhances carrier mobility. Furthermore, the h-BN-encapsulated device exhibits superior humidity resistance and long-term stability. This work establishes a universal materials platform for gas sensing that combines the exceptional properties of 2D materials with unique device architecture, opening new possibilities for high-performance environmental monitors, industrial safety systems, and wearable health diagnostics.

Conventional type 1 dendritic cells (cDC1s) are specialized for cross-presenting tumor antigens and determining the efficacy of immunotherapies, including immune checkpoint blockade and adoptive cell therapy. However, their rarity and tumor-induced dysfunction severely limit CD8 T cell priming and represent a central bottleneck to therapeutic efficacy. While strategies such as anti-DEC-205-mediated antigen delivery and Flt3L-driven DC expansion can enhance host DC function, their reliance on functional cDC1s remains a significant constraint. We developed Tim-3-targeted vaccines by conjugating tumor antigens or neoantigens to anti-Tim-3 antibodies. These vaccines delivered antigens to both cDC1s and cDC2s, and elicited robust, durable CD8 T cell responses. Remarkably, Tim-3-targeted vaccines endowed cDC2s with efficient cross-presentation capacity that matched that of cDC1s. In tumor-bearing mice or in CD11c-β-cateninactive mice, which model β-catenin-driven DC dysfunction, Tim-3-targeted vaccination restored cross-priming and counteracted tumor- and DC-mediated immunosuppression. In Batf3-/- mice lacking cDC1s, anti-Tim-3-based vaccines still elicited significant CD8 T cell cross-priming and tumor control-albeit both were reduced compared to wild-type mice- demonstrating that cDC1s contribute to but are not essential for Tim-3-targeted vaccine-induced CD8 T cell priming and anti-tumor efficacy. Strikingly, a single dose of anti-Tim-3-neoantigen vaccination eradicated large established MC38 tumors in a CD8 T cell-dependent manner. Together, these data identify Tim-3-targeted vaccines as a next-generation cancer vaccine platform that broadens DC engagement, reduces reliance on cDC1s, and overcomes tumor- and DC-mediated immunosuppression, addressing key limitations of current DC-based cancer vaccines.

The climate action plan has enhanced the demand for critical minerals and metals, such as those found in ore deposits associated with ultramafic-mafic magmas (e.g. platinum group elements, Ni, Cu, Co, V and Ti). A growing demand for these metals is spurring many countries to explore new geological areas, as well as to more fully investigate existing deposits. This special volume contains 11 chapters covering a wide range of topics, organized thematically, which provide new information concerning the genetic links between mineralization and magmatism that will improve our scientific understanding of orthomagmatic ore formation processes.

Ensuring the functional correctness of a digital system is achievable through formal verification. Despite the increased complexity of modern systems, formal verification still needs to be done in a reasonable time. Hence, Polynomial Formal Verification (PFV) techniques are being explored, as they provide guaranteed polynomial upper bounds on the time and space required for the verification process. Recently, it was shown that circuits characterized by a constant cutwidth can be verified in linear time using Answer Set Programming (ASP). However, these results are limited to combinational circuits, whereas most designs used in digital systems are sequential. In this paper, we introduce Linear Formal Verification (LFV) as a subclass of PFV for sequential circuits with constant cutwidth, which are verifiable in linear time and space using ASP. We achieve this by proposing a new data structure called Weighted And-Inverter Graph (W-AIG). Unlike existing formal verification methods, we prove that our approach can verify any sequential circuit with a constant cutwidth in linear time and space. Finally, we implement our approach and experimentally show that a variety of sequential circuits, such as pipelined adders, serial adders, and shift registers, can be verified in linear time and space, while ring counters can be verified in polynomial time and space, confirming our theoretical findings.

The Metaproteomics Initiative was officially launched in 2021 to strengthen collaboration, promote knowledge exchange, and support and lead standardization efforts within the growing metaproteomics community. Over the past five years, the Initiative has developed into a structured, global network of researchers. It has launched community-driven benchmark studies, helped shape emerging metadata and reporting standards, developed practical guidance and training materials, organized international symposia, and fostered connections across the microbiome research landscape (https://metaproteomics.org/). We outline the Initiative's organization, activities, achievements, and ongoing efforts, and reflect on how sustained, community-led coordination has shaped the development of metaproteomics as a field. We further position the Grand Metaproteome Challenges as a next step toward coordinated, community-scale biological research, aimed at advancing functional microbiome studies across clinical, industrial, and environmental application domains, and invite engagement from the wider microbiome and omics communities.

Understanding the interaction between polymers and proteins is of interest for researchers in medicine, biology, food science, and water treatment, among other fields. The goal may be to create strong interactions with enzymes to improve their catalytic stability, while in nanomedicine and biomedical engineering, the focus is often on reducing protein adsorption on polymer surfaces. Researchers have developed libraries of polymers with various monomer combinations and tested their binding to different proteins to better understand these interactions. In this work, we aimed to identify the polymer with the highest or lowest binding affinity to all proteins, respectively, using Gaussian Process Regression (GPR). However, incorporating categorical features such as the type of monomer has not been widely applied in GPR. Here we compare a range of process models, which were coined Multiplicative kernel, Additive kernel, Easy to interpret Gaussian Process model (EzGP), Latent Variable Gaussian Processes (LVGP), and the Latent Map Gaussian Processes (LMGP) by their developers. The LVGP model was found to perform best on the polymer-protein data set, where the output for binding strength was given by Förster resonance energy transfer (FRET), which can be used to help generate large data sets for machine learning (ML). The polymer that had the highest affinity to glucose oxidase (GOx), uricase (Uri), casein (Cas), trypsin (Trp), carbonic anhydrase (CAn) and bovine serum albumin (BSA) carried positive charges as well as hydrophobic benzyl groups. Negatively charged monomers dominated the polymer that rejected the most proteins intermixed with some cationic units, reminiscent of zwitterionic polymers.

Sensor technology progressions have paved the way for the rapid expansion of the Internet of Things (IoT) applications to construct behavioral and physiological monitoring systems, like an IoT-based student healthcare monitoring system. The status of student health observation is necessary because the number of students who survive loneliness is increasing in large geographical areas. This research article presents an approach named optimized attention enhanced temporal graph convolutional network-based cloud resource allocation supported Internet of Things for students' health monitoring system (HMS-AETGCN-NGOA-IoT). The proposed HMS-AETGCN-NGOA-IoT is implemented using MATLAB. To detect students' health status, performance metrics like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, and ROC are considered. The HMS-AETGCN-NGOA-IoT approach achieves 19.11%, 24.12%, and 28.13% higher specificity; 24.93%, 23.04%, and 9.51% lower computation time; 15.2%, 25.45%, and 13.91% higher ROC values; and 8.45%, 20.98%, and 27.55% higher accuracy compared with the existing Health Monitoring System based on Message Passing Neural Network for Internet of Things(HMS-MPNN-IoT), Health Monitoring System based on Support Vector Machine for Internet of Things(HMS-SVM-IoT) and Health Monitoring System based on Deep Neural Network for Internet of Things(HMS-DNN-IoT) methods, respectively.

This study proposes a pretraining enhanced multicomponent directed message passing neural network (PEMC D-MPNN) for predicting the solubility of H2S in ionic liquids (ILs). Traditional feature engineering methods often treat ILs as single entities, overlooking the different structural roles of the cations and anions. To address this, we introduce a multicomponent framework that separately encodes cation and anion structures using a D-MPNN, explicitly modeling their interactions. Given the limited experimental H2S solubility data, a pretraining strategy is employed utilizing the CheMeleon foundation model trained on one million molecules from PubChem to learn universal molecular representations, which are then fine-tuned for H2S solubility prediction. The proposed model integrates operational conditions (i.e., temperature and pressure) and leverages interpretability tools, such as SHapley Additive exPlanations (SHAP) and principal component analysis (PCA), to validate feature importance. The evaluation results demonstrate that the proposed PEMC D-MPNN model outperforms existing models (i.e., GPR, RF, XGBoost, SVM, DBN, RNN, DJINN, GP, GMDH), with an R2 of 0.9922, MAE of 0.0080, and RMSE of 0.0136 on 722 data points and an R2 of 0.9964, AAPRE of 5.0506%, and RMSE of 0.0099 on 1516 data points. External validation on unseen ILs, i.e.,1-ethyl-3-methylimidazolium trifluoromethanesulfonate, 1-ethyl-3-methylimidazolium tris(pentafluoroethyl)trifluorophosphate, and 1-butylpyridinium tetrafluoroborate (with 139 data points) confirms strong generalization ability, highlighting the robustness of the proposed model and practical utility for IL screening and design.

Lateral flow immunoassay (LFIA) is a vital point-of-care testing (POCT) technique that is widely used for on-site detection and in vitro diagnosis. Many diseases, particularly cardiovascular diseases (CVDs), require the quantitative detection of multiple biomarkers across a broad dynamic range. Recurrent viral infections like COVID-19 elevate CVDs risk, necessitating early warning and prognostic monitoring, especially based on POCT of multiple blood biomarkers of D-dimer, NT-proBNP, and cTnI across a broad range from pg/mL to μg/mL. Traditional LFIAs lack ultrasensitivity and broad-range quantification. We design the surface chemistry-mediated template-free assembly of metal-aggregation-induced emission luminogens (AIEgen) framework@gold nanoparticles (MAF@AuNPs) for developing ultrasensitive and dynamic dual-quantitative LFIA (ddLFIA). The template-free self-assembled MAF@AuNPs have strong fluorescent and plasmonic properties, avoiding complicated synthesis, chemical modifications, and covalent conjugations, thus leading to highly convenient and versatile visual POCT. MAF@AuNPs ddLFIA achieves a normal detection at ng/mL using chromogenic AuNPs and ultrasensitive detection at pg/mL using fluorogenic MAFs, for which the fluorescence signal is 102-103 times more sensitive than the color signal. MAFs and AuNPs are functionally complementary to form a dynamic, broad-range quantification system covering over 5 orders of magnitude, at the same time having a naked eye sensitivity near 1 pg/mL. The visual POCT using MAF@AuNPs ddLFIA aligns well with clinical chemiluminescent assays. MAF@AuNPs ddLFIA perfectly matches ultrasensitive and broad-range multiple biomarker detections for clinical CVDs diagnosis.

CdTe colloidal quantum wells (CQWs) exhibit very low quantum yields (QYs) (<1%) and suffer significant stability challenges unlike their CdSe counterparts, which feature near unity QY and high stability under ambient conditions. Here, we synthesized highly stable CdSeTe CQWs with photoluminescence quantum efficiency (PLQY) reaching 25%, which far exceeds the previously best-reported PLQYs from CdTe CQWs while preserving the PL spectral characteristics (PL peak position and full-width-at-half-maximum (fwhm) of PL emission) similar to those of CdTe CQWs. In these as-synthesized CdSeTe nanoplatelets, a progressive increase in the Se/Te ratio leads to a notable enhancement in their quantum yield, accompanied by extended photoluminescence lifetimes, strongly indicating that higher Se incorporation effectively mitigates nonradiative recombination channels. In addition, we demonstrate that these CdSeTe CQWs are stable under ambient conditions unlike pristine CdTe CQWs through systematic studies with X-ray photoelectron spectroscopy (XPS). Using density functional theory (DFT) calculations we demonstrate that, under oxygen-rich conditions, O atoms weaken Cd–Te bonds and destabilize CdTe nanoplatelets, whereas in Se-alloyed CdSeTe CQWs, oxygen atoms preferentially adsorb onto the surface without penetrating the structure, effectively suppressing bond destabilization, which is consistent with our XPS observations. The enhanced optical properties, stability, and robustness of these CdSeTe-alloyed nanoplatelets offer a practical solution to overcoming the inherent limitations of CdTe CQWs and position them as highly attractive materials for practical use in advanced optoelectronic applications.