Solvent engineering for scalable fabrication of perovskite/silicon tandem solar cells in air.

Perovskite/silicon tandem solar cells hold great promise for realizing high power conversion efficiency at low cost. However, achieving scalable fabrication of wide-bandgap perovskite (~1.68 eV) in air, without the protective environment of an inert atmosphere, remains challenging due to moisture-induced degradation of perovskite films. Herein, this study reveals that the extent of moisture interference is significantly influenced by the properties of solvent. We further demonstrate that n-Butanol (nBA), with its low polarity and moderate volatilization rate, not only mitigates the detrimental effects of moisture in air during scalable fabrication but also enhances the uniformity of perovskite films. This approach enables us to achieve an impressive efficiency of 29.4% (certified 28.7%) for double-sided textured perovskite/silicon tandem cells featuring large-size pyramids (2-3 µm) and 26.3% over an aperture area of 16 cm2. This advance provides a route for large-scale production of perovskite/silicon tandem solar cells, marking a significant stride toward their commercial viability.

Ultrasensitive and multiplexed tracking of single cells using whole-body PET/CT.

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems; however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upward of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 becquerel per cell and a streamlined workflow to reliably label single cells with over 50 becquerel per cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of more than 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

Increased [18F]FDG uptake of radiation-induced giant cells: a single-cell study in lung cancer models.

Positron emission tomography (PET), a cornerstone in cancer diagnosis and treatment monitoring, relies on the enhanced uptake of fluorodeoxyglucose ([18F]FDG) by cancer cells to highlight tumors and other malignancies. While instrumental in the clinical setting, the accuracy of [18F]FDG-PET is susceptible to metabolic changes introduced by radiation therapy. Specifically, radiation induces the formation of giant cells, whose metabolic characteristics and [18F]FDG uptake patterns are not fully understood. Through a novel single-cell gamma counting methodology, we characterized the [18F]FDG uptake of giant A549 and H1299 lung cancer cells that were induced by radiation, and found it to be considerably higher than that of their non-giant counterparts. This observation was further validated in tumor-bearing mice, which similarly demonstrated increased [18F]FDG uptake in radiation-induced giant cells. These findings underscore the metabolic implications of radiation-induced giant cells, as their enhanced [18F]FDG uptake could potentially obfuscate the interpretation of [18F]FDG-PET scans in patients who have recently undergone radiation therapy.

Phosphorylation-Driven Epichaperome Assembly: A Critical Regulator of Cellular Adaptability and Proliferation.

The intricate protein-chaperone network is vital for cellular function. Recent discoveries have unveiled the existence of specialized chaperone complexes called epichaperomes, protein assemblies orchestrating the reconfiguration of protein-protein interaction networks, enhancing cellular adaptability and proliferation. This study delves into the structural and regulatory aspects of epichaperomes, with a particular emphasis on the significance of post-translational modifications in shaping their formation and function. A central finding of this investigation is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 situated within an intrinsically disordered region, as critical determinants in epichaperome assembly. Our data demonstrate that the phosphorylation of these serine residues enhances HSP90's interaction with other chaperones and co-chaperones, creating a microenvironment conducive to epichaperome formation. Furthermore, this study establishes a direct link between epichaperome function and cellular physiology, especially in contexts where robust proliferation and adaptive behavior are essential, such as cancer and stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone complexes in diseases characterized by epichaperome dysregulation, bridging the gap between fundamental research and precision medicine.

Phosphatase specificity principles uncovered by MRBLE:Dephos and global substrate identification.

Phosphoprotein phosphatases (PPPs) regulate major signaling pathways, but the determinants of phosphatase specificity are poorly understood. This is because methods to investigate this at scale are lacking. Here, we develop a novel in vitro assay, MRBLE:Dephos, that allows multiplexing of dephosphorylation reactions to determine phosphatase preferences. Using MRBLE:Dephos, we establish amino acid preferences of the residues surrounding the dephosphorylation site for PP1 and PP2A-B55, which reveals common and unique preferences. To compare the MRBLE:Dephos results to cellular substrates, we focused on mitotic exit that requires extensive dephosphorylation by PP1 and PP2A-B55. We use specific inhibition of PP1 and PP2A-B55 in mitotic exit lysates coupled with phosphoproteomics to identify more than 2,000 regulated sites. Importantly, the sites dephosphorylated during mitotic exit reveal key signatures that are consistent with MRBLE:Dephos. Furthermore, integration of our phosphoproteomic data with mitotic interactomes of PP1 and PP2A-B55 provides insight into how binding of phosphatases to substrates shapes dephosphorylation. Collectively, we develop novel approaches to investigate protein phosphatases that provide insight into mitotic exit regulation.

Defect engineering enhances plasmonic-hot electrons exploitation for CO2 reduction over polymeric catalysts.

Defect sites present on the surface of catalysts serve a crucial role in different catalytic processes. Herein, we have investigated defect engineering within a hybrid system composed of "soft" polymer catalysts and "hard" metal nanoparticles, employing the disparity in their thermal expansions. Electron paramagnetic resonance, X-ray photoelectron spectroscopy, and mechanistic studies together reveal the formation of new abundant defects and their synergistic integrability with plasmonic enhancement within the hybrid catalyst. These active defects, co-localized with plasmonic Ag nanoparticles, promote the utilization efficiency of hot electrons generated by local plasmons, thereby enhancing the CO2 photoreduction activity while maintaining the high catalytic selectivity.

Efficient and multiplexed tracking of single cells using whole-body PET/CT.

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems, however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upwards of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a new tracking algorithm (PEPT-EM) to push the cellular detection threshold to below 4 Bq/cell, and a streamlined workflow to reliably label single cells with over 50 Bq/cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of method, we tracked the fate of over 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

Synthesis of Black Phosphorene Quantum Dots from Red Phosphorus.

Black phosphorene quantum dots (BPQDs) are most commonly derived from high-cost black phosphorus, while previous syntheses from the low-cost red phosphorus (Pred ) allotrope are highly oxidised. Herein, we present an intrinsically scalable method to produce high quality BPQDs, by first ball-milling Pred to create nanocrystalline Pblack and subsequent reductive etching using lithium electride solvated in liquid ammonia. The resultant ~25 nm BPQDs are crystalline with low oxygen content, and spontaneously soluble as individualized monolayers in tertiary amide solvents, as directly imaged by liquid-phase transmission electron microscopy. This new method presents a scalable route to producing quantities of high quality BPQDs for academic and industrial applications.

VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography.

Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation.

Ultrabroadband plasmon driving selective photoreforming of methanol under ambient conditions.

Liquid methanol has the potential to be the hydrogen energy carrier and storage medium for the future green economy. However, there are still many challenges before zero-emission, affordable molecular H2 can be extracted from methanol with high performance. Here, we present noble-metal-free Cu-WC/W plasmonic nanohybrids which exhibit unsurpassed solar H2 extraction efficiency from pure methanol of 2,176.7 µmol g-1 h-1 at room temperature and normal pressure. Macro-to-micro experiments and simulations unveil that local reaction microenvironments are generated by the coperturbation of WC/W's lattice strain and infrared-plasmonic electric field. It enables spontaneous but selective zero-emission reaction pathways. Such microenvironments are found to be highly cooperative with solar-broadband-plasmon-excited charge carriers flowing from Cu to WC surfaces for efficient stable CH3OH plasmonic reforming with C3-dominated liquid products and 100% selective gaseous H2. Such high efficiency, without any COx emission, can be sustained for over a thousand-hour operation without obvious degradation.

Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study.

BACKGROUND: Multivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability. METHODS: We investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models. RESULTS: In a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86-0.87 at 5 years, 0.79-0.81 at 10 years) than using baseline or last observed CS data (0.80-0.86 at 5 years, 0.73-0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering. CONCLUSION: Our analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000.

Polyisoprenylated benzophenones and xanthones from the pericarp of Garcinia planchonii Pierre.

Two new polyisoprenylated benzophenones, planchoniones A (1) and B (2), together with two known benzophenones (3, 4) and six known xanthones (5-10), were isolated from an ethyl acetate extract of the pericarp of Garcinia planchonii Pierre. Their structures were established using spectroscopic methods, mainly 1D and 2D NMR. The four benzophenones were evaluated for their cytotoxicity against MCF-7 human breast cancer cells, and showed almost no activity. Meanwhile, compounds 5-10 were investigated for their inhibitory effects towards α-glucosidase, and γ-mangostin (5) exhibited the most remarkable effect with IC50 value of 15.3 ± 0.9 µM (compared with acarbose, IC50 = 224.9 ± 3.6 µM).

Two new antidiabetic xanthones from the twigs of Garcinia oblongifolia.

Two new xanthones, oblongixanthones I (1) and J (2), and seven known compounds (3-9), were isolated from an EtOAc extract of the twigs of Garcinia oblongifolia. Their structures were elucidated using spectroscopic methods, mainly 1 D and 2 D NMR. The antidiabetic effects of the two new compounds were evaluated using α-glucosidase and PTP1B inhibition assays. Both compounds displayed strong inhibition towards α-glucosidase with IC50 values of 258.7 ± 49.3 and 187.1 ± 27.5 µM, respectively (compared with acarbose, IC50 = 900.0 ± 3.0 µM) and moderate effects against PTP1B with IC50 values of 93.9 ± 12.3 and 64.1 ± 5.8 µM, respectively (compared with RK682, IC50 = 4.4 ± 0.3 l µM).

Triterpenoids from the bark of Entandrophragma angolense.

Two new triterpenoids, entanolide (1) and methyl 3,4-secotirucalla-23-oxo-4(28),7,24-trien-21-al-3-oate (2), together with nine known compounds (3-11), were isolated from the bark of Entandrophragma angolense. Their structures were elucidated based on spectroscopic analyses, mainly 1 D and 2 D NMR spectral data. Compounds 1-6 and 8 were evaluated for their cytotoxicity against HepG2 cells, and compounds 2-5 exhibited weak activities.

Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation.

BACKGROUND: Delirium poses significant risks to patients, but countermeasures can be taken to mitigate negative outcomes. Accurately forecasting delirium in intensive care unit (ICU) patients could guide proactive intervention. Our primary objective was to predict ICU delirium by applying machine learning to clinical and physiologic data routinely collected in electronic health records. METHODS: Two prediction models were trained and tested using a multicenter database (years of data collection 2014 to 2015), and externally validated on two single-center databases (2001 to 2012 and 2008 to 2019). The primary outcome variable was delirium defined as a positive Confusion Assessment Method for the ICU screen, or an Intensive Care Delirium Screening Checklist of 4 or greater. The first model, named "24-hour model," used data from the 24 h after ICU admission to predict delirium any time afterward. The second model designated "dynamic model," predicted the onset of delirium up to 12 h in advance. Model performance was compared with a widely cited reference model. RESULTS: For the 24-h model, delirium was identified in 2,536 of 18,305 (13.9%), 768 of 5,299 (14.5%), and 5,955 of 36,194 (11.9%) of patient stays, respectively, in the development sample and two validation samples. For the 12-h lead time dynamic model, delirium was identified in 3,791 of 22,234 (17.0%), 994 of 6,166 (16.1%), and 5,955 of 28,440 (20.9%) patient stays, respectively. Mean area under the receiver operating characteristics curve (AUC) (95% CI) for the first 24-h model was 0.785 (0.769 to 0.801), significantly higher than the modified reference model with AUC of 0.730 (0.704 to 0.757). The dynamic model had a mean AUC of 0.845 (0.831 to 0.859) when predicting delirium 12 h in advance. Calibration was similar in both models (mean Brier Score [95% CI] 0.102 [0.097 to 0.108] and 0.111 [0.106 to 0.116]). Model discrimination and calibration were maintained when tested on the validation datasets. CONCLUSIONS: Machine learning models trained with routinely collected electronic health record data accurately predict ICU delirium, supporting dynamic time-sensitive forecasting.

Safety and immunogenicity of Nanocovax, a SARS-CoV-2 recombinant spike protein vaccine: Interim results of a double-blind, randomised controlled phase 1 and 2 trial.

Background: Nanocovax is a recombinant severe acute respiratory syndrome coronavirus 2 subunit vaccine composed of full-length prefusion stabilized recombinant SARS-CoV-2 spike glycoproteins (S-2P) and aluminium hydroxide adjuvant. Methods: We conducted a dose-escalation, open label trial (phase 1) and a randomized, double-blind, placebo-controlled trial (phase 2) to evaluate the safety and immunogenicity of the Nanocovax vaccine (in 25 mcg, 50 mcg, and 75 mcg doses, aluminium hydroxide adjuvanted (0·5 mg/dose) in 2-dose regime, 28 days apart (ClinicalTrials.gov number, NCT04683484). In phase 1, 60 participants received two intramuscular injection of the vaccine following dose-escalation procedure. The primary outcomes were reactogenicity and laboratory tests to evaluate the vaccine safety. In phase 2, 560 healthy adults received either vaccine doses similar in phase 1 (25 or 50 or 75 mcg S antigen in 0·5 mg aluminium per dose) or adjuvant (0·5 mg aluminium) in a ratio of 2:2:2:1. One primary outcome was the vaccine safety, including solicited adverse events for 7 day and unsolicited adverse events for 28 days after each injection as well as serious adverse event or adverse events of special interest throughout the study period. Another primary outcome was anti-S IgG antibody response (Index unit/ml). Secondary outcomes were surrogate virus neutralisation (inhibition percentage), wild-type SARS-CoV-2 neutralisation (dilution fold), and T-cell responses by intracellular staining for interferon gamma (IFNg). Anti-S IgG and neutralising antibody levels were compared with convalescent serum samples from symptomatic Covid-19 patients. Findings: For phase 1 study, no serious adverse events were observed for all 60 participants. Most adverse events were grade 1 and disappeared shortly after injection. For phase 2 study, after randomisation, 480 participants were assigned to receive the vaccine with adjuvant, and 80 participants were assigned to receive the placebo (adjuvant only). Reactogenicity was absent or mild in the majority of participants and of short duration (mean ≤3 days). Unsolicited adverse events were mild in most participants. There were no serious adverse events related to Nanocovax. Regarding the immunogenicity, Nanocovax induced robust anti-S antibody responses. In general, there humoral responses were similar among vaccine groups which reached their peaks at day 42 and declined afterward. At day 42, IgG levels of vaccine groups were 60·48 [CI95%: 51·12-71·55], 49·11 [41·26-58·46], 57·18 [48·4-67·5] compared to 7·10 [6·32-13·92] of convalescent samples. IgG levels reported here can be converted to WHO international standard binding antibody unit (BAU/ml) by multiplying them to a conversion factor of 21·8. Neutralising antibody titre of vaccine groups at day 42 were 89·2 [52·2-152·3], 80·0 [50·8-125.9] and 95·1 [63·1-143·6], compared to 55·1 [33·4-91·0] of the convalescent group. Interpretation: Up to day 90, Nanocovax was found to be safe, well tolerated, and induced robust immune responses. Funding: This work was funded by the Coalition for Epidemic Preparedness Innovations (CEPI), the Ministry of Science and Technology of Vietnam, and Nanogen Pharmaceutical Biotechnology JSC.

Reaction kinetics of 1,4-cyclohexadienes with OH radicals: an experimental and theoretical study.

This work presents the OH-initiated oxidation kinetics of 1,4-cyclochexadiene (1,4-CHD). The temperature dependence of the reaction was investigated by utilizing a laser flash photolysis flow reactor and laser-induced fluorescence (LPFR/LIF) technique over the temperature range of 295-438 K and a pressure of ∼50 torr. The kinetics of the reaction was followed by measuring the LIF signal of OH radicals near 308 nm. The reaction of OH radicals with 1,4-CHD exhibited a clear negative temperature dependence. To discern the role of various channels, ab initio and RRKM-based ME calculations (RRKM-ME) were performed over temperatures of 200-2000 K and pressures of 0.76-7600 torr. The computed energy profile revealed that the reaction proceeds via the formation of a pre-reaction van der Waals complex at the entrance channel. The complex was found to be more stable than that usually seen in other alkenes + OH reactions. Both the addition channel and the abstraction reaction of allylic hydrogen were found to have negative energy barriers. Interestingly, the abstraction reaction exhibited a negative temperature dependence at low temperatures and contributed significantly (∼37%) to the total rate coefficients even under atmospheric conditions. At T ≥ 900 K, the reaction was found to proceed exclusively (>95%) via the abstraction channel. Due to the competing channels, the reaction of OH radicals with 1,4-CHD displays complicated kinetic behaviours, reflecting the salient features of the energy profile. The role of competing channels was fully characterized by our kinetic model. The calculated rate coefficients showed excellent agreement with the available experimental data.

Aortic valve reconstruction surgery using autologous pericardium: The experience in Vietnam.

OBJECTIVE: The study aimed to evaluate the indications and describe the aortic valve reconstruction techniques by Ozaki's procedure in Vietnam and report mid-term outcomes of this technique in Vietnam. METHODS: Between June 2017 and December 2019, 72 patients diagnosed with isolated aortic valve disease, with a mean age of 52.9 (19-79 years old), and a male:female ratio of 3:1 underwent aortic valve reconstruction surgery by Ozaki's technique at Cardiovascular Center, E Hospital, Vietnam. RESULTS: The aortic valve diseases consisted of aortic stenosis (42%), aortic regurgitation (28%), and a combination of both (30%). In addition, the proportion of aortic valves with bicuspid morphology and small annulus (≤21 mm) was 28% and 38.9%, respectively. The mean aortic cross-clamp time was 106 ± 13.8 min, mean cardiopulmonary bypass time was 136.7 ± 18.5 min, and 2.8% of all patients required conversion to prosthetic valve replacement surgery. The mean follow-up time was 26.4 months (12-42 months), the survival rate was 95.8%, the reoperation rate was 2.8%, and rate of postoperative moderate or higher aortic valve regurgitation was 4.2%. Postoperative valvular hemodynamics was favorable, with a peak pressure gradient of 16.1 mmHg and an effective orifice area index of 2.3 cm2 . CONCLUSIONS: This procedure was safe and effective, with favorable valvular hemodynamics and a low rate of valvular degeneration. However, more long-term follow-up data are needed.

Computational signatures for post-cardiac arrest trajectory prediction: Importance of early physiological time series.

BACKGROUND: There is an unmet need for timely and reliable prediction of post-cardiac arrest (CA) clinical trajectories. We hypothesized that physiological time series (PTS) data recorded on the first day of intensive care would contribute significantly to discrimination of outcomes at discharge. PATIENTS AND METHODS: Adult patients in the multicenter eICU database who were mechanically ventilated after resuscitation from out-of-hospital CA were included. Outcomes of interest were survival, neurological status based on Glasgow motor subscore (mGCS) and surrogate functional status based on discharge location (DL), at hospital discharge. Three machine learning predictive models were trained, one with features from the electronic health records (EHR), the second using features derived from PTS collected in the first 24 h after ICU admission (PTS24), and the third combining PTS24 and EHR. Model performances were compared, and the best performing model was externally validated in the MIMIC-III dataset. RESULTS: Data from 2216 admissions were included in the analysis. Discrimination of prediction models combining EHR and PTS24 features was higher than models using either EHR or PTS24 for prediction of survival (AUROC 0.83, 0.82 and 0.79 respectively), neurological outcome (0.87, 0.86 and 0.79 respectively), and DL (0.80, 0.78 and 0.76 respectively). External validation in MIMIC-III (n = 86) produced similar model performance. Feature analysis suggested prognostic significance of previously unknown EHR and PTS24 variables. CONCLUSION: These results indicate that physiological data recorded in the early phase after CA resuscitation contain signatures that are linked to post-CA outcome. Additionally, they attest to the effectiveness of ML for post-CA predictive modeling.

Interfacing transition metal dichalcogenides with chromium germanium telluride quantum dots for controllable light-matter interactions.

In this work, we unravel a facile solution-based method to prepare chromium germanium telluride, Cr2Ge2Te6 (CGT) quantum dots (QDs), which present strong light-matter interactions with monolayer transition metal dichalcogenides (TMDs) in their CGT/TMD vertical heterostructures. The heterostructures' optoelectronic properties were controlled by simply varying the QDs thickness. We observed contrasting emissions from monolayer TMDs in the various CGT QDs-TMDs (of WS2, WSe2 and MoS2) heterostructures depending on the density of QDs in the heterostructures. Low-density CGT QDs-based heterostructures demonstrated a reduced light emission intensity compared to the isolated monolayers, but with an increased trion ratio due to the electron doping effect of CGT QDs. In contrast, high-density CGT QDs-based heterostructures showed an increased light emission intensity and a broadened, red-shifted emission peak in comparison to the bare TMDs, attributed to the enhanced optical absorption in the heterostructures arising from the assembled CGT QDs. Finally, proof-of-concept field-effect transistor (FET) and photodetector devices based on the created CGT QDs-WS2 heterostructures were designed, which showed an enhanced optoelectronic performance.