Machine-Learning-Driven Discovery of Mn⁴⁺-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors

Machine-Learning-Driven Discovery of Mn⁴⁺-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors

Machine-Learning-Driven Discovery of Mn⁴⁺-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors

Machine-Learning-Driven Discovery of Mn⁴⁺-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors

Machine-Learning-Driven Discovery of Mn⁴⁺-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors

Machine-Learning-Driven Discovery of Mn⁴⁺-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors

Machine-Learning-Driven Discovery of Mn⁴⁺-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors

Highly Efficient and Stable Narrow-Band Red Phosphor Cs₂SiF₆:Mn⁴⁺ for High-Power Warm White LED Applications

Due to the unique narrow-band red emission and broadband blue light excitation, as well as milder synthesis conditions, Mn⁴⁺ ion activated fluoride red phosphors show great promise for white light emitting diode (W-LED) applications. However, as the Mn⁴⁺ emission belongs to a spin-forbidden transition (²E_g → ⁴A₂), it is a fundamental challenge to synthesize these phosphors with a high external quantum efficiency (EQE) above 60%. Herein, a highly efficient and thermally stable red fluoride phosphor, Cs₂SiF₆:Mn⁴⁺, with a high internal quantum efficiency (IQE) of 89% and ultrahigh EQE of 71% is demonstrated. Furthermore, nearly 95% of the room-temperature IQE and EQE are maintained at 150 °C. The static and dynamic spectral measurements, as well as density functional theory (DFT) calculations, show that the excellent performance of Cs₂SiF₆:Mn⁴⁺ is due to the Mn⁴⁺ ions being evenly distributed in the host lattice Cs₂SiF₆. By employing Cs₂SiF₆:Mn⁴⁺ as a red light component, stable 10 W high-power warm W-LEDs with a luminous efficiency of ∼110 lm/W could be obtained. These findings indicate that red phosphor Cs₂SiF₆:Mn⁴⁺ may be a highly suitable candidate for fabricating high-performance high-power warm white LEDs

Table_2_Crosstalk of necroptosis and pyroptosis defines tumor microenvironment characterization and predicts prognosis in clear cell renal carcinoma.docx

Pyroptosis and necroptosis are two recently identified forms of immunogenic cell death in the tumor microenvironment (TME), indicating a crucial involvement in tumor metastasis. However, the characteristics of necroptosis and pyroptosis that define tumor microenvironment and prognosis in ccRCC patients remain unknown. We systematically investigated the transcriptional variation and expression patterns of Necroptosis and Pyroptosis related genes (NPRGs). After screening the necroptosis-pyroptosis clusters, the potential functional annotation for clusters was explored by GSVA enrichment analysis. The Necroptosis-Pyroptosis Genes (NPG) scores were used for the prognosis model construction and validation. Then, the correlations of NPG score with clinical features, cancer stem cell (CSC) index, tumor mutation burden (TMB), TME, and Immune Checkpoint Genes (ICGs) were also individually explored to evaluate the prognosis predictive values in ccRCC. Microarray screenings identified 27 upregulated and 1 downregulated NPRGs. Ten overall survival associated NPRGs were filtered to construct the NPG prognostic model indicating a better prognostic signature for ccRCC patients with lower NPG scores (P< 0.001), which was verified using the external cohort. Univariate and multivariate analyses along with Kaplan-Meier survival analysis demonstrated that NPG score prognostic model could be applied as an independent prognostic factor, and AUC values of nomogram from 1- to 5- year overall survival with good agreement in calibration plots suggested that the proposed prognostic signature possessed good predictive capabilities in ccRCC. A high-/sNPG score is proven to be connected with tumor growth and immune-related biological processes, according to enriched GO, KEGG, and GSEA analyses. Comparing patients with a high-NPG score to those with a low-NPG score revealed significant differences in clinical characteristics, growth and recurrence of malignancies (CSC index), TME cell infiltration, and immunotherapeutic response (P< 0.005), potentially making the NPG score multifunctional in the clinical therapeutic setting. Furthermore, AIM2, CASP4, GSDMB, NOD2, and RBCK1 were also found to be highly expressed in ccRCC cell lines and tumor tissues, and GASP4 and GSDMB promote ccRCC cells’ proliferation, migration, and invasion. This study firstly suggests that targeting the NPG score feature for TME characterization may lend novel insights into its clinical applications in the prognostic prediction of ccRCC.</p

Image_1_Crosstalk of necroptosis and pyroptosis defines tumor microenvironment characterization and predicts prognosis in clear cell renal carcinoma.tiff

Pyroptosis and necroptosis are two recently identified forms of immunogenic cell death in the tumor microenvironment (TME), indicating a crucial involvement in tumor metastasis. However, the characteristics of necroptosis and pyroptosis that define tumor microenvironment and prognosis in ccRCC patients remain unknown. We systematically investigated the transcriptional variation and expression patterns of Necroptosis and Pyroptosis related genes (NPRGs). After screening the necroptosis-pyroptosis clusters, the potential functional annotation for clusters was explored by GSVA enrichment analysis. The Necroptosis-Pyroptosis Genes (NPG) scores were used for the prognosis model construction and validation. Then, the correlations of NPG score with clinical features, cancer stem cell (CSC) index, tumor mutation burden (TMB), TME, and Immune Checkpoint Genes (ICGs) were also individually explored to evaluate the prognosis predictive values in ccRCC. Microarray screenings identified 27 upregulated and 1 downregulated NPRGs. Ten overall survival associated NPRGs were filtered to construct the NPG prognostic model indicating a better prognostic signature for ccRCC patients with lower NPG scores (P< 0.001), which was verified using the external cohort. Univariate and multivariate analyses along with Kaplan-Meier survival analysis demonstrated that NPG score prognostic model could be applied as an independent prognostic factor, and AUC values of nomogram from 1- to 5- year overall survival with good agreement in calibration plots suggested that the proposed prognostic signature possessed good predictive capabilities in ccRCC. A high-/sNPG score is proven to be connected with tumor growth and immune-related biological processes, according to enriched GO, KEGG, and GSEA analyses. Comparing patients with a high-NPG score to those with a low-NPG score revealed significant differences in clinical characteristics, growth and recurrence of malignancies (CSC index), TME cell infiltration, and immunotherapeutic response (P< 0.005), potentially making the NPG score multifunctional in the clinical therapeutic setting. Furthermore, AIM2, CASP4, GSDMB, NOD2, and RBCK1 were also found to be highly expressed in ccRCC cell lines and tumor tissues, and GASP4 and GSDMB promote ccRCC cells’ proliferation, migration, and invasion. This study firstly suggests that targeting the NPG score feature for TME characterization may lend novel insights into its clinical applications in the prognostic prediction of ccRCC.</p