Leveraging Unidentified Metabolic Features for Key Pathway Discovery: Chemical Classification-driven Network Analysis in Untargeted Metabolomics.

Untargeted metabolomics using liquid chromatography-electrospray ionization-high-resolution tandem mass spectrometry (UPLC-ESI-MS/MS) provides comprehensive insights into the dynamic changes of metabolites in biological systems. However, numerous unidentified metabolic features limit its utilization. In this study, a novel approach, the Chemical Classification-driven Molecular Network (CCMN), was proposed to unveil key metabolic pathways by leveraging hidden information within unidentified metabolic features. The method was demonstrated by using the herbivore-induced metabolic response in corn silk as a case study. Untargeted metabolomics analysis using UPLC-MS/MS was performed on wild corn silk and two genetically modified lines (pre- and postinsect treatment). Global annotation initially identified 256 (ESI-) and 327 (ESI+) metabolites. MS/MS-based classifications predicted 1939 (ESI-) and 1985 (ESI+) metabolic features into the chemical classes. CCMNs were then constructed using metabolic features shared classes, which facilitated the structure- or class annotation for completely unknown metabolic features. Next, 844/713 significantly decreased and 1593/1378 increased metabolites in ESI-/ESI+ modes were defined in response to insect herbivory, respectively. Method validation on a spiked maize sample demonstrated an overall class prediction accuracy rate of 95.7%. Potential key pathways were prescreened by a hypergeometric test using both structure- and class-annotated differential metabolites. Subsequently, CCMN was used to deeply amend and uncover the pathway metabolites deeply. Finally, 8 key pathways were defined, including phenylpropanoid (C6-C3), flavonoid, octadecanoid, diterpenoid, lignan, steroid, amino acid/small peptide, and monoterpenoid. This study highlights the effectiveness of leveraging unidentified metabolic features. CCMN-based key pathway analysis reduced the bias in conventional pathway enrichment analysis. It provides valuable insights into complex biological processes.

Modulation of Buried Interface by 1-(3-aminopropyl)-Imidazole for Efficient Inverted Formamidinium-Cesium Perovskite Solar Cells.

Considering the direct influence of substrate surface nature on perovskite (PVK) film growth, buried interfacial engineering is crucial to obtain ideal perovskite solar cells (PSCs). Herein, 1-(3-aminopropyl)-imidazole (API) is introduced at polytriarylamine (PTAA)/PVK interface to modulate the bottom property of PVK. First, the introduction of API improves the growth of PVK grains and reduces the Pb2+ defects and residual PbI2 present at the bottom of the film, contributing to the acquisition of high-quality PVK film. Besides, the presence of API can optimize the energy structure between PVK and PTAA, which facilitates the interfacial charge transfer. Density functional theory (DFT) reveals that the electron donor unit (R-C ═ N) of the API prefers to bind with Pb2+ traps at the PVK interface, while the formation of hydrogen bonds between the R-NH2 of API and I- strengthens the above binding ability. Consequently, the optimum API-treated inverted formamidinium-cesium (FA/Cs) PSCs yields a champion power conversion efficiency (PCE) of 22.02% and exhibited favorable stability.

Doping of ZnO Electron Transport Layer with Organic Dye Molecules to Enhance Efficiency and Photo-Stability of the Non-Fullerene Organic Solar Cells.

The solution-processed zinc oxide (ZnO) electron transport layer (ETL) always exhibits ubiquitous defects, and its photocatalytic activity is detrimental for the organic solar cell (OSC) to achieve high efficiency and stability. Herein, an organic dye molecule, PDINN-S is introduced, to dope ZnO, constructing a hybrid ZnO:PDINN-S ETL. This hybrid ETL exhibits improved electron mobility and conductivity, particularly post-light exposure. The catalytic activity of ZnO is also effectively suppressed.Consequently, the efficiency and photo-stability of inverted non-fullerene OSCs are synergistically enhanced. The devices based on PM6:Y6/PM6:BTP-eC9 active layer with ZnO:PDINN-S as ETL give impressive power conversion efficiencies (PCEs) of 16.78%/17.59%, significantly higher than those with pure ZnO as ETL (PCEs = 15.31%/16.04%). Moreover, ZnO:PDINN-S-based device shows exceptional long-term stability under continuous AM 1.5G illumination (T80 = 1130 h) , overwhelming the reference device (T80 = 455 h). In addition, Incorporating PDINN-S into ZnO alleviate mechanical stress within the inorganic lattice, making ZnO:PDINN-S ETL more suitable for the fabrication of flexible devices. Overall, doping ZnO with organic dye molecules offers an innovative strategy for developing multifunctional and efficient hybrid ETL of the non-fullerene OSCs with excellent efficiency and photo-stability.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach.

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

High Conductivity, Semiconducting, and Metallic PEDOT:PSS Electrode for All-Plastic Solar Cells.

Plastic electrodes are desirable for the rapid development of flexible organic electronics. In this article, a plastic electrode has been prepared by employing traditional conducting polymer poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and plastic substrate polyethersulfone (PES). The completed electrode (Denote as HC-PEDOT:PSS) treated by 80% concentrated sulfuric acid (H2SO4) possesses a high electrical conductivity of over 2673 S/cm and a high transmittance of over 90% at 550 nm. The high conductivity is attributed to the regular arrangement of PEDOT molecules, which has been proved by the X-ray diffraction characterization. Temperature-dependent conductivity measurement reveals that the HC-PEDOT:PSS possesses both semiconducting and metallic properties. The binding force and effects between the PEDOT and PEI are investigated in detail. All plastic solar cells with a classical device structure of PES/HC-PEDOT:PSS/PEI/P3HT:ICBA/EG-PEDOT:PSS show a PCE of 4.05%. The ITO-free device with a structure of Glass/HC-PEDOT:PSS/Al4083/PM6:Y6/PDINO/Ag delivers an open-circuit voltage (VOC) of 0.81 V, short-circuit current (JSC ) of 23.5 mA/cm2, fill factor (FF) of 0.67 and a moderate power conversion efficiency (PCE) of 12.8%. The above results demonstrate the HC-PEDOT:PSS electrode is a promising candidate for all-plastic solar cells and ITO-free organic solar cells.

Novel Method for Comprehensive Annotation of Plant Glycosides Based on Untargeted LC-HRMS/MS Metabolomics.

Glycosides are a large family of secondary metabolites in plants, which play a critical role in plant growth and development. Due to the complexity and diversity in structures and the limited availability of authentic standards, comprehensive annotation of the glycosides remains a great challenge. In this study, using maize as an example, a deep annotation method of glycosides was proposed based on untargeted liquid chromatography-high-resolution tandem mass spectrometry metabolomics analysis. First, knowledge-based in silico aglycone and glycosyl/acyl-glycosyl libraries were built. A total of 1240 known and potential aglycones from databases and literature were recorded. Next, the MS parameters beneficial to aglycone ion-rich MS/MS were explored using 1782 high-resolution MS/MS spectra of glycosides from the MassBank of North America (MoNA) and confirmed by 52 authentic glycoside standards. Then, screening rules for aglycon ions in MS/MS were recommended. Glycoside candidates were further filtered by MS/MS-based chemical classification and MS/MS similarity of aglycon-glycoside pairs. Finally, the glycosylation sites of flavonoid mono-O-glycosides were recommended by characteristic fragmentation patterns. The developed method was validated using glycosides and nonglycosides from the MoNA library. The annotation accuracy rates were 96.8, 94.9, and 98.0% in negative ion mode (ESI-), positive ion mode (ESI+), and the combined ESI- & ESI+, respectively. The annotation specificity was 99.6% (ESI-), 99.6% (ESI+), and 99.2% (ESI- & ESI+). A total of 274 glycosides (including 34 acyl-glycosides) were tentatively annotated in maize by the developed method. The method enables effective and reliable annotation for plant glycosides.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies.

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer.

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography-mass spectrometry-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as an area under the receiver-operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (range 80.0%-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver-operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. (Hepatology 2018;67:662-675).

A novel analysis method for biomarker identification based on horizontal relationship: identifying potential biomarkers from large-scale hepatocellular carcinoma metabolomics data.

Omics techniques develop quickly and have made a great contribution to disease study. Omics data are usually complex. How to analyze the data and mine important information has been a key part in omics research. To study the nature of disease mechanisms systematically, we propose a new data analysis method to define the network biomarkers based on horizontal comparison (DNB-HC). DNB-HC performs molecule horizontal relationships to characterize the physiological status and differential network analysis to screen the biomarkers. We applied DNB-HC to analyze a large-scale metabolomics data, which contained 550 samples from a nested case-control hepatocellular carcinoma (HCC) study. A network biomarker was defined, and its areas under curves (AUC) in the receiver-operating characteristic (ROC) analysis for HCC discrimination were larger than those defined by six efficient feature selection methods in most cases. The effectiveness was further corroborated by another nested HCC dataset. Besides, the performance of the defined biomarkers was better than that of α-fetoprotein (AFP), a commonly used clinical biomarker for distinguishing HCC from high-risk population of liver cirrhosis in other two independent metabolomics validation sets. All and 90.3% of the AFP false-negative patients with HCC were correctly diagnosed in these two sets, respectively. The experimental results illustrate that DNB-HC can mine more important information reflecting the nature of the research problems by studying the feature horizontal relationship systematically and identifying effective disease biomarkers in clinical practice. Graphical abstract.

Deep Annotation of Hydroxycinnamic Acid Amides in Plants Based on Ultra-High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry and Its In Silico Database.

Hydroxycinnamic acid amides (HCAAs), diversely distributed secondary metabolites in plants, play essential roles in plant growth and developmental processes. Most current approaches can be used to analyze a few known HCAAs in a given plant. A novel method for comprehensive detection of plant HCAAs is urgently needed. In this study, a deep annotation method of HCAAs was proposed on the basis of ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) and its in silico database of HCAAs. To construct an in silico UHPLC-HRMS HCAAs database, a total of 846 HCAAs were generated from the most common phenolic acid and polyamine/aromatic monoamine substrates according to possible biosynthesis reactions, which represent the structures of plant-specialized HCAAs. The characteristic MS/MS fragmentation patterns of HCAAs were extracted from reference mixtures. Four quantitative structure-retention relationship (QSRR) models were developed to predict retention times of mono-trans-HCAAs (aromatic amines conjugates), mono-trans-HCAAs (aliphatic amines conjugates), bis-HCAAs, and tris-HCAAs. The developed method was applied for identifying HCAAs in seeds (maize, wheat, and rice), roots (rice), and leaves (rice and tobacco). A total of 79 HCAAs were detected: 42 of them were identified in these plants for the first time, and 20 of them have never been reported to exist in plants. The results showed that the developed method can be used to identify HCAAs in a plant without prior knowledge of HCAA distributions. To the best of our knowledge, it is the first UHPLC-HRMS database developed for effective deep annotation of HCAAs from nontargeted UHPLC-HRMS data. It is useful for the identification of novel HCAAs in plants.

Efficient Colorful Perovskite Solar Cells Using a Top Polymer Electrode Simultaneously as Spectrally Selective Antireflection Coating.

Organometal halide perovskites have shown excellent optoelectronic properties and have been used to demonstrate a variety of semiconductor devices. Colorful solar cells are desirable for photovoltaic integration in buildings and other aesthetically appealing applications. However, the realization of colorful perovskite solar cells is challenging because of their broad and large absorption coefficient that commonly leads to cells with dark-brown colors. Herein, for the first time, we report a simple and efficient strategy to achieve colorful perovskite solar cells by using the transparent conducting polymer (poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate), PEDOT:PSS) as a top electrode and simultaneously as an spectrally selective antireflection coating. Vivid colors across the visible spectrum are attained by engineering optical interference effects among the transparent PEDOT:PSS polymer electrode, the hole-transporting layer and the perovskite layer. The colored perovskite solar cells display power conversion efficiency values from 12.8 to 15.1% (from red to blue) when illuminated from the FTO glass side and from 11.6 to 13.8% (from red to blue) when illuminated from the PEDOT:PSS side. The new approach provides an advanced solution for fabricating colorful perovskite solar cells with easy processing and high efficiency.

Free-Standing Conducting Polymer Films for High-Performance Energy Devices.

Thick, uniform, easily processed, highly conductive polymer films are desirable as electrodes for solar cells as well as polymer capacitors. Here, a novel scalable strategy is developed to prepare highly conductive thick poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (HCT-PEDOT:PSS) films with layered structure that display a conductivity of 1400 S cm(-1) and a low sheet resistance of 0.59 ohm sq(-1). Organic solar cells with laminated HCT-PEDOT:PSS exhibit a performance comparable to the reference devices with vacuum-deposited Ag top electrodes. More importantly, the HCT-PEDOT:PSS film delivers a specific capacitance of 120 F g(-1) at a current density of 0.4 A g(-1). All-solid-state flexible symmetric supercapacitors with the HCT-PEDOT:PSS films display a high volumetric energy density of 6.80 mWh cm(-3) at a power density of 100 mW cm(-3) and 3.15 mWh cm(-3) at a very high power density of 16160 mW cm(-3) that outperforms previous reported solid-state supercapacitors based on PEDOT materials.

Metal electrode-free perovskite solar cells with transfer-laminated conducting polymer electrode.

We report perovskite solar cells with a new device structure that employ highly conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) ( PEDOT: PSS) as the top electrode replacing commonly used metal electrodes. The PEDOT: PSS top electrode is prepared from its aqueous solution through a transfer-lamination technique rather than direct spin-coating, which converts the CH(3)NH(3)PbI(3) into PbI(2). Perovskite solar cells with the structure of glass/FTO/c-TiO(2)/m-TiO(2)/CH(3)NH(3)PbI(3)/spiro-OMeTAD/PEDOT:PSS yield a maximum open-circuit voltage (V(OC)) of 1.02 V, and a maximum power conversion efficiency (PCE) of 11.29% under AM1.5 100 mW/cm(2) illumination. The whole device was fabricated in air without high-vacuum deposition which simplifies the processing and lowers the threshold of both scientific research and industrial production of perovskite solar cells.

Advancing Integration of Direct C-H Arylation-Derived Star-Shaped Oligomers as Second Acceptors for Ternary Organic Solar Cells.

Organic solar cells (OSCs) could benefit from the ternary bulk heterojunction (BHJ), a method that allows for fine-tuning of light capture, cascade energy levels, and film shape, in order to increase their power conversion efficiency (PCE). In this work, the third components of PM6:Y6 and PM6:BTP-eC9 BHJs are a set of four star-shaped unfused ring electron acceptors (SSUFREAs), i.e., BD-IC, BFD-IC, BD-2FIC, and BFD-2FIC, that are facilely synthesized by direct C-H arylation. The four SSUFREAs all show complete complementary absorption with PM6, Y6, and BTP-eC9, which facilitates light harvesting and exciton collection. When BFD-2FIC is added as a third component, the PCEs of PM6:Y6 and PM6:BTP-eC9 binary BHJs are able to be improved from 15.31% to 16.85%, and from 16.23% to 17.23%, respectively, showing that BFD-2FIC is useful for most effective ternary OSCs in general, and increasing short circuit current (JSC) and better film morphology are two additional benefits. The ternary PM6:Y6:BFD-2FIC exhibits a 9.7% percentage of increase in PCE compared to the PM6:Y6 binary BHJ, which is one of the highest percentage increases among the reported ternary BHJs, showing the huge potential of BFD-2FIC for ternary BHJ OSCs.

Fine-Scale Characterization of Plant Diterpene Glycosides Using Energy-Resolved Untargeted LC-MS/MS Metabolomics Analysis.

Plant diterpene glycosides are essential for diverse physiological processes. Comprehensive structural characterization proved to be a challenge due to variations in glycosylation patterns, diverse aglycone structures, and the absence of comprehensive reference databases. In this study, a method for fine-scale characterization was proposed based on energy-resolved (ER) untargeted LC-MS/MS metabolomics analysis using steviol glycosides as a demonstration. Energy-dependent fragmentation patterns were unveiled by a series of model compounds. Distinct glycosylation sites were discerned by leveraging varying fragmentation energies for the precursor ions. The sugar moiety linkage at C19OOH (R1) exhibited facile and intact cleavage at low collision energies, while the sugar moiety at C13-OH (R2) demonstrated consecutive cleavage with increasing energy. Aglycone ions exhibited a higher relative intensity at NCE 50, with relative intensities ranging from 95% to 100%. Subsequently, aglycone candidates, R1 sugar composition, and R2 sugar sequence were deduced through ER-MS/MS analysis. The developed method was applied to Stevia rebaudiana leaves. A total of 91 diterpene glycosides were unambiguously identified, including 16 steviol glycosides with novel acetylglycosylation patterns. This method offers a rapid alternative for glycan analysis and the structural differentiation of isomers. The developed method enhances the understanding of diterpene glycosides in plants, providing a reliable tool for the in-depth characterization of complex metabolite profiles.

Bay-Functionalized Perylene Diimide Derivative Cathode Interfacial Layer for High-Performance Organic Solar Cells.

The field of organic solar cells (OSCs) has acquired rapid progress with the development of nonfullerene acceptors. Interfacial engineering is also significant for the enhancement of the power conversion efficiency (PCE) in OSCs. Among the cathode interfacial materials (CIMs), perylene diimide (PDI) small molecules are promising owing to the excellent electron affinity and electron mobility. Although the well-known PDINN molecule has excellent properties, it has a high planarity formed by an extensive rigid π-conjugated backbone. Because the PDI molecular backbone has a strong tendency to aggregate, it causes the problem of excessive molecular aggregation and stacking, which directly leads to excessive crystallinity. Proper accumulation is beneficial for charge transport, but oversized crystals formed by overaggregation will hinder charge transport, ultimately affecting the film morphology and charge transport efficiency. Modifying the bay position of PDINN is an effective strategy to reduce the planarity, modulate the molecular aggregation, optimize the morphology, and enhance the charge-collecting efficiency. Therefore, PDINN-S was synthesized from PDINN by substituting the hydrogen with thiophene. The optimal PCE in the PM6:Y6 active layer was 16.18% and remained at 80% of the initial value after 720 h in a glovebox. This provides some guidance for exploring CIMs and preparing large-scale OSCs in the future.

Cation and anion optimization of ammonium halide for interfacial passivation of inverted perovskite solar cells.

Perovskite solar cell (PSC) commercialization faces intrinsic stability and efficiency challenges. N1-phenylethane-1,2-diamine hydrohalides (PNEAX) based on a new design strategy featuring cation and anion optimization have been developed for efficient interfacial passivation. Among them, PNEACl-treated devices achieved a champion efficiency of 21.01% with good stability.

Defect passivation via a multifunctional organic additive toward efficient and stable inverted perovskite solar cells.

A multifunctional group molecule, namely MATC, was first introduced into a Cs/FA-based perovskite used as an additive. An impressive PCE of 21.51% was achieved for the inverted PSCs with reduced defect states and improved perovskite film quality. Moreover, MATC passivation considerably enhanced the stability of the PSC devices.