Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study.

BACKGROUND: The existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients. METHODS: The data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram. RESULTS: In total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values. CONCLUSIONS: In this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.

BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks. METHODS: To comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights. RESULTS: The experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization. CONCLUSION: With the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.

Single-junction organic solar cells with over 19% efficiency enabled by a refined double-fibril network morphology.

In organic photovoltaics, morphological control of donor and acceptor domains on the nanoscale is the key for enabling efficient exciton diffusion and dissociation, carrier transport and suppression of recombination losses. To realize this, here, we demonstrated a double-fibril network based on a ternary donor-acceptor morphology with multi-length scales constructed by combining ancillary conjugated polymer crystallizers and a non-fullerene acceptor filament assembly. Using this approach, we achieved an average power conversion efficiency of 19.3% (certified 19.2%). The success lies in the good match between the photoelectric parameters and the morphological characteristic lengths, which utilizes the excitons and free charges efficiently. This strategy leads to an enhanced exciton diffusion length and a reduced recombination rate, hence minimizing photon-to-electron losses in the ternary devices as compared to their binary counterparts. The double-fibril network morphology strategy minimizes losses and maximizes the power output, offering the possibility of 20% power conversion efficiencies in single-junction organic photovoltaics.

Exploration of Damage Identification Method for a Large-Span Timber Lattice Shell Structure in Taiyuan Botanical Garden based on Structural Health Monitoring.

Large-span spatial lattice structures generally have characteristics such as incomplete modal information, high modal density, and high degrees of freedom. To address the problem of misjudgment in the damage detection of large-span spatial structures caused by these characteristics, this paper proposed a damage identification method based on time series models. Firstly, the order of the autoregressive moving average (ARMA) model was selected based on the Akaike information criterion (AIC). Then, the long autoregressive method was used to estimate the parameters of the ARMA model and extract the residual sequence of the autocorrelation part of the model. Furthermore, principal component analysis (PCA) was introduced to reduce the dimensionality of the model while retaining the characteristic values. Finally, the Mahalanobis distance (MD) was used to construct the damage sensitive feature (DSF). The dome of Taiyuan Botanical Garden in China is one of the largest non-triangular timber lattice shells worldwide. Relying on the structural health monitoring (SHM) project of this structure, this paper verified the effectiveness of the damage identification model through numerical simulation and determined the damage degree of the dome structure through SHM measurement data. The results demonstrated that the proposed damage identification method can effectively identify the damage of large-span timber lattice structures, locate the damage position, and estimate the degree of damage. The constructed DSF had relatively strong robustness to small damage and environmental noise and has practical application value for SHM in engineering.

Sleep profile and the risk of cardiovascular events in patients undergoing percutaneous coronary intervention.

A significant proportion of patients with coronary artery disease (CAD) who undergo percutaneous coronary intervention (PCI) suffer from physical and mental disorders which lead to the decline of sleep profile. Sleep disorders are highly prevalent in these patients. But the effect of sleep on the outcomes of post-PCI patients remains unclear. We aim to examine the individual and joint effects of sleep quality and sleep duration on the risk of adverse cardiovascular events in post-PCI patients. We included 314 participants who were diagnosed with a first CAD and underwent PCI with drug-eluting stents and followed up for a mean duration of 341 days to assess major adverse cardiovascular events (MACEs). Sleep quality, based on the Pittsburgh Sleep Quality Index, was categorized as good (a score of ≤7) or poor (>7). Sleep duration was categorized into three classes: ≤ 5, 6-8 (reference group) and ≥ 9 hours per day. The log-rank test and the Cox regression model were used for data analysis. MACEs occurred in 26 (8.3%) patients. Subjects whose sleep duration was ≤ 5 hours per day had a shorter time to MACEs than those whose sleep duration was 6-8 hours (p = 0.036). A significantly increased risk for MACEs was observed for participants with a ≤ 5 hours sleep duration (HR = 2.18, 95% CI = 1.02-4.64) after adjustment for demographic and clinical confounders. Associations between long sleep duration (≥ 9 hours), sleep quality, or their joint effect and MACEs were not found. This suggests the importance of considering sleep loss when developing strategies to improve health outcomes of PCI patients. And further researches are needed to examine the effects of different aspects of sleep quality on the prognosis of PCI patients and explore the reasons that lead to the decline of sleep profile.

A Thieno[3,4-b]thiophene-Based Non-fullerene Electron Acceptor for High-Performance Bulk-Heterojunction Organic Solar Cells.

A thieno[3,4-b]thiophene-based electron acceptor, ATT-1, is designed and synthesized. ATT-1 exhibits a planar conjugated framework, broad absorption with a large absorption coefficient, and a slightly high LUMO energy level. Bulk-heterojunction (BHJ) solar cells based on PTB7-Th electron donor and ATT-1 electron acceptor delivered power conversion efficiencies of up to 10.07%, which is among the best performances reported for non-fullerene BHJ solar cells using PTB7-Th as the electron donor.

[Study on the Photoluminescence Properties of Tb Doped Si Nanowires].

Silicon nanowires with high density, uniform distribution and large area were produced directly from Si(100) based on solid-liquid-solid mechanism under the growth temperature of 1 100 ℃ with 30 min. The flow of N2 is 1 500 sccm and Au-Al films is used as metallic catalyst. The diameters of Si nanowires is 50~120 nm and the lengths of the formed Si nanowires is hundreds of nanometers. After that Tb-doped Si nanowires were reserched. We experimentally investigated the influences of the different process parameters on the luminescence of Tb-doped Si nanowires.The main process parameters includ doping temperature(1 000~1 200 ℃), doping time (30~90 min) and gas flow rate of N2 (0~1 000 sccm). Finally, Tb-doped bulk silicon substrates have been studied experimentally. We characterized and analyzed the photoluminescence properties of Tb-doped Si nanowires with the Hitachi F-4600 fluorescence spectrophotometer. The corresponding relation of energy level structure and transition properties of Tb ion with the experimental spectrum is analyzed in detail. The experiment result indicates that the Tb-doped Si nanowires have a stronly green luminescencent. The emission peak position of the largest intensity at 554 nm (5D4→7F5) with the doping temperature 1 100 ℃, the flow of N2 1 000 sccm and the excitation wavelength 243 nm. At the same time,three emission bands of 494 nm (5D4→7F6), 593 nm (5D4→7F4) and 628 nm (5D4→7F3) were observed under room temperature. The Tb-doped Si nanowires appeared strong green light emission compared with the bulk silicon substrate. Its application has a certain reference value for studying the characteristics of luminescence of rare earth element doped Si based material. Meanwhile,the photoluminescence properties of Tb-doped Si nanowires affected by the diameter, length, distribution and density of Si nanowires. That is necessary forour further research.

Nanostructures and multi-scale aggregation of high ion exchange capacity short-side-chain perfluorosulfonic acid dispersion.

Short-side-chain perfluorosulfonic acid (SSC-PFSA) ionomers with high ion-exchange-capacity are promising candidates for high-temperature proton exchange membranes (PEMs) and catalyst layer (CL) binders. The solution-casting method determines the importance of SSC-PFSA dispersion characteristics in shaping the morphology of PEMs and CLs. Therefore, a thorough understanding of the chain behavior of SSC-PFSA in dispersions is essential for fabricating high-quality PEMs and CLs. In this study, we have employed multiple characterization techniques, including dynamic light scatting (DLS), small-angle X-ray scattering (SAXS), and cryo-transmission electron microscope (Cryo-TEM), to fully study the chain aggregation behaviors of SSC-PFSA in water-ethanol solvents and elucidate the concentration-dependent self-assembly process. In dilute dispersions (2 mg/mL), SSC-PFSA assembles into mono-disperse rod-like aggregates, featuring a twisted fluorocarbon backbone that forms a hydrophobic stem, and the sulfonic acid side chains extending outward to suit the hydrophilic environment. As the concentration increases, the radius of rod particles increases from 1.47 to 1.81 nm, and the mono-disperse rod particles first form a "end-to-end" configuration that doubles length (10 mg/mL), and then transform into a swollen network structure in semi-dilute dispersion (20 mg/mL). This work provides a well-established structure model for SSC-PFSA dispersions, which is the key nanostructure to be inherited by PEMs.

Jamming Giant Molecules at Interface in Organic Photovoltaics to Improve Performance and Stability.

A novel approach for depositing the giant molecule acceptor (GMA) at the donor-acceptor interface to enhance the efficiency and stability of organic photovoltaic (OPV) devices through a designed interface-enhanced layer-by-layer device fabrication protocol is proposed. The giant molecule acceptor DQx-Ph is mixed with the polymer donor in the bottom layer to form a polymer donor fibril phase and a mixed phase, followed by subsequent deposition of the main acceptor L8-BO. The L8-BO solution swells the bottom layer and alters the localized morphology of the mixing phase, introducing L8-BO fibrillar crystallization and pushing DQx-Ph giant molecules outwards to the fibril interfaces. Through this approach, the localized morphology and optoelectronic property of the bulk heterojunction are optimized. This configuration maintains the superior transport properties of L8-BO while integrating the high open-circuit voltage characteristics of DQx-Ph. Additionally, exciton dissociation and charge generation are simultaneously enhanced, with suppressed energy losses. A power conversion efficiency of 19.9% with improved operational stability is achieved, underscoring the importance of GMA interface jamming in advancing OPV technology. This study provides new insights into the development of ancillary OPV materials to overcome the critical limitations in OPV, revealing innovative approaches for photovoltaic technologies.

GPT-Assisted Learning of Structure-Property Relationships by Graph Neural Networks: Application to Rare-Earth-Doped Phosphors.

Two challenges facing machine learning tasks in materials science are data set construction and descriptor design. Graph neural networks circumvent the need for empirical descriptors by encoding geometric information in graphs. Large language models have shown promise for database construction via text extraction. Here, we apply OpenAI's Generative Pre-trained Transformer 4 (GPT-4) and the Crystal Graph Convolutional Neural Network (CGCNN) to the problem of discovering rare-earth-doped phosphors for solid-state lighting. We used GPT-4 to datamine the chemical formulas and emission wavelengths of 264 Eu2+-doped phosphors from 274 articles. A CGCNN model was trained on the acquired data set, achieving a test R2 of 0.77. Using this model, we predicted the emission wavelengths of over 40 000 inorganic materials. We also used transfer learning to fine-tune a bandgap-predicting CGCNN model for emission wavelength prediction. The workflow requires minimal human supervision and is generalizable to other fields.

Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model.

As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients' survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.

All-polymer organic solar cells with nano-to-micron hierarchical morphology and large light receiving angle.

Distributed photovoltaics in living environment harvest the sunlight in different incident angles throughout the day. The development of planer solar cells with large light-receiving angle can reduce the requirements in installation form factor and is therefore urgently required. Here, thin film organic photovoltaics with nano-sized phase separation integrated in micro-sized surface topology is demonstrated as an ideal solution to proposed applications. All-polymer solar cells, by means of a newly developed sequential processing, show large magnitude hierarchical morphology with facilitated exciton-to-carrier conversion. The nano fibrilar donor-acceptor network and micron-scale optical field trapping structure in combination contributes to an efficiency of 19.06% (certified 18.59%), which is the highest value to date for all-polymer solar cells. Furthermore, the micron-sized surface topology also contributes to a large light-receiving angle. A 30% improvement of power gain is achieved for the hierarchical morphology comparing to the flat-morphology devices. These inspiring results show that all-polymer solar cell with hierarchical features are particularly suitable for the commercial applications of distributed photovoltaics due to its low installation requirement.

Experimental Study on Variable-Amplitude Fatigue Performance of M60 High-Strength Bolts of Grid Structures with Bolted Spherical Joints.

The high-strength bolts of grid structures with bolted spherical joints under the action of suspension cranes are at risk of severe fatigue failure. Thus, this paper studies the variable-amplitude fatigue performance of M60 high-strength bolts. The test results for eight specimens in four loading modes are obtained using an Amsler fatigue testing machine. The fatigue life is also estimated based on Miner and Corten-Dolan's theories, and the applicability of Corten-Dolan's theory is verified. The fracture induced by the variable-amplitude fatigue is microscopically analyzed using scanning electron microscopy (SEM), revealing the mechanism of the variable-amplitude fatigue failure. Our findings provide valuable experimental data supporting the fatigue life estimation of grid structures with bolted spherical joints in service.

Correlating Electronic Structure and Device Physics with Mixing Region Morphology in High-Efficiency Organic Solar Cells.

The donor/acceptor interaction in non-fullerene organic photovoltaics leads to the mixing domain that dictates the morphology and electronic structure of the blended thin film. Initiative effort is paid to understand how these domain properties affect the device performances on high-efficiency PM6:Y6 blends. Different fullerenes acceptors are used to manipulate the feature of mixing domain. It is seen that a tight packing in the mixing region is critical, which could effectively enhance the hole transfer and lead to the enlarged and narrow electron density of state (DOS). As a result, short-circuit current (JSC ) and fill factor (FF) are improved. The distribution of DOS and energy levels strongly influences open-circuit voltage (VOC ). The raised filling state of electron Fermi level is seen to be key in determining device VOC . Energy disorder is found to be a key factor to energy loss, which is highly correlated with the intermolecular distance in the mixing region. A 17.53% efficiency is obtained for optimized ternary devices, which is the highest value for similar systems. The current results indicate that a delicate optimization of the mixing domain property is an effective route to improve the VOC , JSC , and FF simultaneously, which provides new guidelines for morphology control toward high-performance organic solar cells.

Design Rules of the Mixing Phase and Impacts on Device Performance in High-Efficiency Organic Photovoltaics.

In nonfullerene acceptor- (NFA-) based solar cells, the exciton splitting takes place at both domain interface and donor/acceptor mixture, which brings in the state of mixing phase into focus. The energetics and morphology are key parameters dictating the charge generation, diffusion, and recombination. It is revealed that tailoringthe electronic properties of the mixing region by doping with larger-bandgap components could reduce the density of state but elevate the filling state level, leading to improved open-circuit voltage (V OC) and reduced recombination. The monomolecular and bimolecular recombinations are shown to be intercorrelated, which show a Gaussian-like relationship with V OC and linear relationship with short-circuit current density (J SC) and fill factor (FF). The kinetics of hole transfer and exciton diffusion scale with J SC similarly, indicating the carrier generation in mixing region and crystalline domain are equally important. From the morphology perspective, the crystalline order could contribute to V OC improvement, and the fibrillar structure strongly affects the FF. These observations highlight the importance of the mixing region and its connection with crystalline domains and point out the design rules to optimize the mixing phase structure, which is an effective approach to further improve device performance.

Organic Solar Cells with 18% Efficiency Enabled by an Alloy Acceptor: A Two-in-One Strategy.

The trade-off between the open-circuit voltage (Voc ) and short-circuit current density (Jsc ) has become the core of current organic photovoltaic research, and realizing the minimum energy offsets that can guarantee effective charge generation is strongly desired for high-performance systems. Herein, a high-performance ternary solar cell with a power conversion efficiency of over 18% using a large-bandgap polymer donor, PM6, and a small-bandgap alloy acceptor containing two structurally similar nonfullerene acceptors (Y6 and AQx-3) is reported. This system can take full advantage of solar irradiation and forms a favorable morphology. By varying the ratio of the two acceptors, delicate regulation of the energy levels of the alloy acceptor is achieved, thereby affecting the charge dynamics in the devices. The optimal ternary device exhibits more efficient hole transfer and exciton separation than the PM6:AQx-3-based system and reduced energy loss compared with the PM6:Y6-based system, contributing to better performance. Such a "two-in-one" alloy strategy, which synergizes two highly compatible acceptors, provides a promising path for boosting the photovoltaic performance of devices.

Subtle Molecular Tailoring Induces Significant Morphology Optimization Enabling over 16% Efficiency Organic Solar Cells with Efficient Charge Generation.

Manipulating charge generation in a broad spectral region has proved to be crucial for nonfullerene-electron-acceptor-based organic solar cells (OSCs). 16.64% high efficiency binary OSCs are achieved through the use of a novel electron acceptor AQx-2 with quinoxaline-containing fused core and PBDB-TF as donor. The significant increase in photovoltaic performance of AQx-2 based devices is obtained merely by a subtle tailoring in molecular structure of its analogue AQx-1. Combining the detailed morphology and transient absorption spectroscopy analyses, a good structure-morphology-property relationship is established. The stronger π-π interaction results in efficient electron hopping and balanced electron and hole mobilities attributed to good charge transport. Moreover, the reduced phase separation morphology of AQx-2-based bulk heterojunction blend boosts hole transfer and suppresses geminate recombination. Such success in molecule design and precise morphology optimization may lead to next-generation high-performance OSCs.

13.7% Efficiency Small-Molecule Solar Cells Enabled by a Combination of Material and Morphology Optimization.

Compared with the quick development of polymer solar cells, achieving high-efficiency small-molecule solar cells (SMSCs) remains highly challenging, as they are limited by the lack of matched materials and morphology control to a great extent. Herein, two small molecules, BSFTR and Y6, which possess broad as well as matched absorption and energy levels, are applied in SMSCs. Morphology optimization with sequential solvent vapor and thermal annealing makes their blend films show proper crystallinity, balanced and high mobilities, and favorable phase separation, which is conducive for exciton dissociation, charge transport, and extraction. These contribute to a remarkable power conversion efficiency up to 13.69% with an open-circuit voltage of 0.85 V, a high short-circuit current of 23.16 mA cm-2 and a fill factor of 69.66%, which is the highest value among binary SMSCs ever reported. This result indicates that a combination of materials with matched photoelectric properties and subtle morphology control is the inevitable route to high-performance SMSCs.

Modulating Structure Ordering via Side-Chain Engineering of Thieno[3,4-b]thiophene-Based Electron Acceptors for Efficient Organic Solar Cells with Reduced Energy Losses.

Nonfullerene-based organic solar cells (OSCs) have made a huge breakthrough in the recent years. Introducing a proper side chain on the π-conjugated backbone plays a vital role for further improving the power conversion efficiency (PCE) of OSCs due to easy tuning of the physical properties of the molecule such as absorption, energetic level, solid-state stacking, and charge transportation. More importantly, the side chain significantly affected the blend film's morphology and thus determined the PCEs of the devices. In this work, two low-band-gap nonfullerene acceptors, ATT-4 and ATT-5, with an alkyl or branched alkyl substitute on indacenodithiophene (IDT) and thieno[3,4-b]thiophene (TbT) backbone were synthesized for investigating the effect of the substituent on the performance of the nonfullerene acceptors (NFAs). In comparison to ATT-1 with p-hexylphenyl-substituted IDT and n-octyl-substituted TbT moieties, ATT-4 and ATT-5 exhibit better crystallinity with shorter interchain distance and ordered molecular structure in neat and the corresponding blend films. The tailored ATT-5 exhibits a high PCE of 12.36% with a Voc of 0.93 V, Jsc of 18.86 mA cm-2, and fill factor (FF) of 0.71, blending with a wide-band-gap polymer donor PBDB-T. Remarkably, although ATT-4 and ATT-5 exhibit broader light absorption, the devices obtained higher Voc than that of ATT-1 mainly due to the reduced nonradiative recombination in the blend films. These results implied that side-chain engineering is an efficient approach to regulate the electronic structure and molecular packing of NFAs, which can well match with polymer donor, and obtain high PCEs of the OSCs with improved Voc, Jsc, and FF, simultaneously.

Aggregation-Induced Multilength Scaled Morphology Enabling 11.76% Efficiency in All-Polymer Solar Cells Using Printing Fabrication.

All-polymer solar cells (all-PSCs) exhibit excellent stability and readily tunable ink viscosity, and are therefore especially suitable for printing preparation of large-scale devices. At present, the efficiency of state-of-the-art all-PSCs fabricated by the spin-coating method has exceeded 11%, laying the foundation for the preparation and practical utilization of printed devices. A high power conversion efficiency (PCE) of 11.76% is achieved based on PTzBI-Si:N2200 all-PSCs processing with 2-methyltetrahydrofuran (MTHF, an environmentally friendly solvent) and preparation of active layers by slot die printing, which is the top efficient for all-PSCs. Conversely, the PCE of devices processed by high-boiling point chlorobenzene is less than 2%. Through the study of film formation kinetics, volatile solvents can freeze the morphology in a short time, and a more rigid conformation with strong intermolecular interaction combined with the solubility limit of PTzBI-Si and N2200 in MTHF results in the formation of a fibril network in the bulk heterojunction. The multilength scaled morphology ensures fast transfer of carriers and facilitates exciton separation, which boosts carrier mobility and current density, thus improving the device performance. These results are of great significance for large-scale printing fabrication of high-efficiency all-PSCs in the future.