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An A–D–A oligomer, DTS(F2BT)2, was synthesized; its structural evolution was studied with DSC, POM, 2D-WAXD, and in-situ GI-XRD. The structural evolution of DTS(F2BT)2 is stepwise and kinetically slow. Both rapid drying and the presence of PC71BM trapped DTS(F2BT)2 in a less ordered nematic (N) phase. PDMS-assisted crystallization enabled a pristine DTS(F2BT)2 thin film to attain a more ordered equilibrium phase, and enhanced the OFET mobility of DTS(F2BT)2. In OPV devices, DIO additive drove the DTS(F2BT)2 domains in the DTS(F2BT)2:PC71BM blended film from the N phase toward the equilibrium phase, and resulted in enhanced OPV performances. These results reveal the slow ordering process of the A–D–A oligomer, and the importance of monitoring the degree of structural evolution of the active thin films in organic optoelectronics.

Organic–inorganic halide perovskite solar cells (PSCs) still suffer from the issues of poor reproducibility and inferior stability despite their considerable increase in efficiency. Here, we demonstrate planar PSCs with simultaneously improved device characteristics and stability by introducing a cross-linkable [6,6]-phenyl-C61-butyric styryl dendron ester (PCBSD) into CH3NH3PbIxCl3−x perovskites. The cross-linkable merit of C-PCBSD can enhance the crystallization of perovskites and address the issue of low electron extraction efficiency. In addition, the anti-solvent network of C-PCBSD facilitates the sequential solution process and prevents washing by the solvent used in the upper layer. The C-PCBSD network resisted the moisture incursion and protected the interfaces from erosion, and it passivated the voids or pinholes generated in the bulk active layer. As a result, an outstanding PSC with a power conversion efficiency of 17.21% is achieved with evidently improved cell stability.

A novel method has been developed to recharge and rejuvenate perovskite solar cells by immersing the device into methyl ammonium iodide (MAI) solution and heating at 70 °C for 40 s. We demonstrated that Au electrodes are more robust than Al and Ag electrodes for ZnO planar structures rather than ZnO and TiO2 nanoparticles (NPs). In addition to this, the degradation mechanism of the device has been discussed. The scanning electron microscopy (SEM) images and X-ray diffraction (XRD) spectroscopy are evidence that the perovskite crystals have been regenerated after degradation (60 days) with the help of MAI as a rejuvenating agent. After long-term aging tests with rejuvenation in 185 days, its PCE value is still around 11% compared to the original one of 11.6%.

We have designed and synthesized two low bandgap conjugated copolymers containing alternating meta-fluoro-p-alkoxyphenyl- (m-FPO-) or p-fluoro-m-alkoxyphenyl- (p-FPO-) substituted benzodithiophenes-co-benzooxadiazole (BO), named PBO-m-FPO and PBO-p-FPO. The properties, including UV–vis absorption, charge mobility and photovoltaic performance of the two polymers have been intensively investigated. The results indicated that the introduction of fluorine atom at m, p positions of phenyl substituted benzodithiophene unit hardly affected their absorption spectra and highest occupied molecular orbital (HOMO) level. However, the two polymers showed different photovoltaic properties. Power conversion efficiencies (PCEs) based on the device structure of ITO/PEDOT:PSS/polymer:PC71BM/Ca/Al demonstrated a large distinction (5.9% for PBO-m-FPO vs 2.8% for PBO-p-FPO) at optimal weight ratio. When replacing the Ca layer with zirconium acetylacetonate (ZrAcac), using 3% 1,8-diiodooctane (DIO) as the active layer additive...

In this study, we developed a new cathodic buffer layer consisting of a cross-linked [6,6]-phenyl-C61-butyric styryl dendron ester (C-PCBSD) matrix and an ionic FPI dopant. The incorporation of FPI can improve the electron mobility via an anion induced charge transfer (AIET) mechanism, while maintaining the solvent-resistant properties of the crosslinked layer. ZnO combined with the C-PCBSD/FPI layer can effectively and universally improve the performance of bulk-heterojunction polymer solar cells (BHJPSCs), planar heterojunction polymer solar cells (PHJPSCs), and organic metallohalide perovskite solar cells (OMPSCs). Furthermore, the insertion of the C-PCBSD/FPI layer can improve the thermal stability of the device by preventing the residual moisture in ZnO from diffusing into the CH3NH3PbI3 layer in OMPSCs.

A copolymerization strategy is developed to utilize porphyrin as a complementary light-harvesting unit (LHU) in D-A polymers. For polymer solar cells (PSCs), the presence of LHUs increases the short-circuit current density (Jsc ) without sacrificing the open-circuit voltage (Voc ) and fill factor (FF). Up to 8.0% power conversion efficiency (PCE) is delivered by PPor-2:PC71 BM single-junction PSCs. A PCE of 8.6% is achieved when a C-PCBSD cathodic interlayer is introduced.

An ITO substrate with periodic surface nanostructures was used to induce strong diffusion and diffraction of incident light. The nanostructures were fabricated using nanoimprint lithography on photoresist followed by coating of the ITO layer and organic materials with uniform morphology. The nanostructures embedded into the ITO layer were found to increase absorption in poly(3-hexylthiophene) : [6,6]-phenyl-C61-butyric acid methyl ester solar devices. The short-circuit current of the nanostructured organic solar cells improved from 7.07 to 10.76 mA/cm2. This improvement was due to the increased effective optical path of absorbed light resulting from the trapping and scattering by the nanostructures.

A high organic field-effect transistor mobility (0.29 cm(2) V(-1) s(-1) ) and bulk-heterojunction polymer solar cell performance (PCE of 6.82%) have been achieved in a low bandgap alternating copolymer consisting of axisymmetrical structural units, 5,6-difluorobenzo-2,1,3-thiadiazole. Introducing the fluorine substituents enhanced intermolecular interaction and improved the solid-state order, which consequently resulted in the highest device performances among the 2,1,3-thiadiazole-quarterthiophene based alternating copolymers.

Porphyrin, despite chosen by Nature as light har- vesting units, hasn't revealed its full potentials as a structural unit in porphyrin-incorporated polymers (PPors). A novel PPor was synthesized to investigate the origins of the low perform- ances of PPor-based polymer solar cells (PSCs). The polymer features broad absorption in the blue-light region, because the diindenothieno(2,3-b)thiophene (DITT) unit extended the conju- gation in the polymer backbone. PPor-DITT/PC71BM based PSCs have a high Voc (0.79 V). Their low Jsc and fill factor (FF) were attributed to the un-optimized morphology, as indicated by the photoluminescence quenching and atomic force micros- copy (AFM) experiments. Using PPor-DITT as a blue-light har- vesting dopant in an amorphous host leverage the strong 400- 550 nm absorption of PPor-DITT and circumvent the difficulties in reaching optimized morphology in the PPor/PCBM thin films. An addition of 2 wt % of PPor-DITT in ternary-blend PSCs resulted in a 10 % increase of external quantum effi- ciency (EQE) in the blue-light region. However, in a crystalline host, the dopant decreased the crystallinity of the host and led to large drops in FF and power conversion efficiencies (PCEs). The study provides an alternative route and expands the appli- cation of PPors in PSCs as a blue-light harvester in ternary- blend PSCs using amorphous polymers as host. V C 2012 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 50: 5032- 5040, 2012

The Gaussian mixture model - Universal background model (GMM-UBM) system is one of the predominant approaches for text-independent speaker verification, because both the target speaker model and the impostor model (UBM) have generalization ability to handle ''unseen'' acoustic patterns. However, since GMM-UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors' voices that are similar to the target speaker's voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the anti-model, while preserving the generalization ability of the GMM-UBM approach. This is achieved by adapting the UBM to a target speaker dependent anti-model based on a minimum verification squared-error criterion, rather than estimating the model from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NIST2001-SRE database show that DFA substantially improves the performance of the conventional GMM-UBM approach.

Speaker verification is usually formulated as a statistical hypothesis testing problem and solved by a log-likelihood ratio (LLR) test. A speaker verification system's performance is highly dependent on modeling the target speaker's voice (the null hypothesis) and characterizing non-target speakers' voices (the alternative hypothesis). However, since the alternative hypothesis involves unknown impostors, it is usually difficult to characterize a priori. In this paper, we propose a framework to better characterize the alternative hypothesis with the goal of optimally distinguishing the target speaker from impostors. The proposed framework is built on a weighted arithmetic combination (WAC) or a weighted geometric combination (WGC) of useful information extracted from a set of pre-trained background models. The parameters associated with WAC or WGC are then optimized using two discriminative training methods, namely, the minimum verification error (MVE) training method and the proposed evolutionary MVE (EMVE) training method, such that both the false acceptance probability and the false rejection probability are minimized. Our experiment results show that the proposed framework outperforms conventional LLR-based approaches.

Speaker verification can be viewed as a task of modeling and testing two hypotheses: the null hypothesis and the alternative hypothesis. Since the alternative hypothesis involves unknown impostors, it is usually hard to characterize a priori. In this paper, we propose improving the characterization of the alternative hypothesis by designing two decision functions based, respectively, on a weighted arithmetic combination and a weighted geometric combination of discriminative information derived from a set of pretrained background models. The parameters associated with the combinations are then optimized using two kernel discriminant analysis techniques, namely, the kernel Fisher discriminant (KFD) and support vector machine (SVM). The proposed approaches have two advantages over existing methods. The first is that they embed a trainable mechanism in the decision functions. The second is that they convert variable-length utterances into fixed-dimension characteristic vectors, which are easily processed by kernel discriminant analysis. The results of speaker-verification experiments conducted on two speech corpora show that the proposed methods outperform conventional likelihood ratio-based approaches.

Sn-Co-Ni alloys of 25 different compositions have been prepared and equilibrated at 250 °C. A new ternary (Ni, Co)Sn4 phase is found, and all the binary compounds have very significant mutual solubilities between Co and Ni. According to the ternary equilibrium phase data and the constituent binary phase diagrams, the 250 °C isothermal section of the Sn-Co-Ni system is proposed. In the Sn-CoSn-Ni3Sn4 corner, there are nine two-phase regions and five tie-triangles. Interfacial reactions between molten Sn and Ni-1.0 at. pct Co, Ni-3.0 at. pct Co, Ni-5.0 at. pct Co, Ni-20.0 at. pct Co, and Ni-40.0 at. pct Co substrates are examined. It has been found that the interfacial reactions in Sn/Ni-1.0 at. pct Co are similar to those in Sn/Ni couples, and only the Ni3Sn4 phase is formed. The (Ni, Co)Sn4 phase is formed in the couples prepared with Ni-3.0 at. pct Co, Ni-5.0 at. pct Co, and Ni-20.0 at. pct Co substrates. It grows very fast and has a faceted and unique morphology.

High-molecular-weight conjugated polymer HD-PDFC-DTBT with N-(2-hexyldecyl)-3,6-difluorocarbazole as the donor unit, 5,6-bis(octyloxy)benzothiadiazole as the acceptor unit, and thiophene as the spacer is synthesized by Suzuki polycondensation. HD-PDFC-DTBT shows a large bandgap of 1.96 eV and a high hole mobility of 0.16 cm(2) V(-1) s(-1) . HD-PDFC-DTBT:PC71 BM-based inverted polymer solar cells (PSCs) give a power conversion efficiency (PCE) of 7.39% with a Voc of 0.93 V, a Jsc of 14.11 mA cm(-2) , and an FF of 0.56.

The likelihood ratio (LR)-based speaker verification is usually difficult to characterize the alternative hypothesis precisely. To better characterize the alternative hypothesis, we propose to incorporate two effective speaker verification approaches based on weighted geometric combination (WGC) and weighted arithmetic combination (WAC) into the support vector machine (SVM) via a new sequence kernel function, named the LR-based composite sequence kernel. This new kernel can be regarded as a unified framework for characterizing the alternative hypothesis by virtue of the complementary information that the WGC and WAC approaches can contribute. Our experiment results show that the proposed sequence kernel method outperforms the conventional speaker verification approaches.

The GMM-UBM system is the current state-of-the-art approach for text-independent speaker verification. The advantage of the approach is that both target speaker model and impostor model (UBM) have generalization ability to handle "unseen" acoustic patterns. However, since GMM-UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors' voices that are similar to the target speaker's voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the anti- model, while preserves the generalization ability of the GMM-UBM approach. This is done by adapting the UBM to a target-speaker- dependent anti-model based on a minimum verification squared- error criterion, rather than estimating from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NTST2001-SRE database show that DFA substantially improves the performance of the conventional GMM-UBM approach.

It is usually difficult to characterize the alternative hypothesis precisely in a log-likelihood ratio (LLR)-based speaker verification system. In a previous work, we proposed using a weighted arithmetic combination (WAC) or a weighted geometric combination (WGC) of the likelihoods of the background models instead of heuristic combinations, such as the arithmetic mean and the geometric mean, to better characterize the alternative hypothesis. In this paper, we further propose learning the parameters associated with WAC or WGC via an evolutionary minimum verification error (MVE) training method, such that both the false acceptance probability and the false rejection probability can be minimized. Our experiment results show that the proposed methods outperform conventional LLR-based approaches.

Speaker verification based on the log-likelihood ratio (LLR) is essentially a task of modeling and testing two hypotheses: the null hypothesis and the alternative hypothesis. Since the alternative hypothesis involves unknown imposters, it is usually hard to characterize a priori. In this paper, we propose a framework to better characterize the alternative hypothesis with the goal of optimally separating client speakers from imposters. The proposed framework is built on either a weighted arithmetic combination or a weighted geometric combination of useful information extracted from a set of pre-trained anti-speaker models. The parameters associated with the combinations are then optimized using minimum verification error training such that both the false acceptance probability and the false rejection probability are minimized. Our experiment results show that the proposed framework outperforms conventional LLR-based approaches.

The paper investigates the problem of automatically grouping unknown speech utterances based on their associated speakers. The proposed method utilizes the vector space model, which was originally developed in document-retrieval research, to characterize each utterance as a tf-idf-based vector of acoustic terms, thereby deriving a reliable measurement of similarity between utterances. To define the required acoustic terms that are most representative in terms of voice characteristics, the Eigenvoice approach is applied to the utterances to be clustered, which creates a set of eigenvector-based terms. To further improve speaker-clustering performance, the proposed method encompasses a mechanism of blind relevance feedback for refining the inter-utterance similarity measure.

Clearly, the linear discriminant classifier is not robust enough to cope with most real-world data classification problems. Kernel Fisher discriminant analysis (KFDA) tries to increase the expressiveness of the discriminant based on the high order statistics of the data set. In this paper, we propose the GMM-based KFDA with the Bhattacharyya kernel to obtain a transformation, called a speaker eigenspace, based on which the transformed MFCC features are more discriminative for speaker recognition. In our approach, the eigenspace is directly constructed from the complete GMM parameter set, rather than the supervectors considering mean vectors only as in the eigenvoice approach. Moreover, FDA, which is believed to be more appropriate for classification accuracy than principal component analysis (PCA), is applied for eigenspace construction. The speaker identification experiments show that the new features outperform the MFCC features, in particular when the amount of enrollment data for each speaker is very small.

Real-word applications often involve a binary hypothesis testing problem with one of the two hypotheses ill-defined and hard to be characterized precisely by a single measure. In this paper, we develop a framework that integrates multiple hypothesis testing measures into a unified decision basis, and apply kernel-based classification techniques, namely, Kernel Fisher Discriminant (KFD) and Support Vector Machine (SVM), to optimize the integration. Experiments conducted on speaker verification demonstrate the superiority of our approaches over the predominant approaches.

This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringof- group-numbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The one-step K-means algorithm is used as the mutation operator. Hence, the proposed GA is called the prototypes-embedded genetic K-means algorithm (PGKA). With the inherent evolution process of evolutionary algorithms, PGKA has superior performance than the classical K-means algorithm, while comparing to other GA-based approaches, PGKA is more efficient and suitable for large scale data sets.