Molecular p-doping allows increasing the conductivity of organic semiconductors, which is regularly exploited in thermoelectric devices. Upon doping, integer and fractional charge transfer have been identified as the two competing mechanisms to occur, where the former is desired to promote the generation of mobile holes in the semiconductor host. In general, high dopant electron affinity is expected to promote integer charge transfer, while strong coupling between the frontier molecular orbitals of dopant and host promotes fractional charge transfer instead. Here, we investigate the role the width of the density of states (DOS) plays in the doping process by doping the conjugated polymer poly(3-hexylthiophene) (P3HT) with tetracyanoquinodimethane (TCNQ) derivatives of different electron affinities at 2% dopant ratio. Cyclic voltammetry confirms that only the electron affinity of F4TCNQ exceeds the ionization energy of P3HT, while TCNQ and FTCNQ turn out to have significantly lower but essentially identical electron affinities.From infrared spectroscopy we learn, however, that ca. 88% of FTCNQ is ionized while all of TCNQ is not. This translates into P3HT conductivities that are increased for F4TCNQ and FTCNQ doping, but surprisingly even reduced for TCNQ doping. To understand the remarkable discrepancy between TCNQ and FTCNQ we calculated the percentage of ionized dopants and the hole densities in the P3HT matrix resulting from varied widths of the P3HT HOMO-DOS via a semi-classical computational approach. We find that broadening of the DOS can yield the expected ionization percentages only if the dopants have significantly different tendencies to cause energetic disorder in the host matrix. In particular, while for TCNQ the doping behavior is well reproduced if the recently reported width of the P3HT HOMO-DOS is used, it must be broadened by almost one order of magnitude to comply with the ionization ratio determined for FTCNQ. Possible reasons for this discrepancy lie in the presence of a permanent dipole in FTCNQ, which highlights that electron affinities alone are not sufficient to define the strength of molecular dopants and their capability to perform integer charge transfer with organic semiconductors.

Here, we study the temperature-dependent transport properties of OFETs with the prototypical OSC 6,13-bis(triisopropylsilylethynyl)pentacene (TIPS-pentacene) co-processed with polystyrene (PS) as the active layer. The active layer is deposited directly on SiO2 using the bar-assisted meniscus shearing (BAMS) method. The co-processing with PS favors a consequential decrease in interfacial trap densities as previously reported. Furthermore, we demonstrate how this processing method leads to devices exhibiting activation energies well below the current state of the art for TIPS-pentacene on SiO2 or other dielectrics. Altogether, our study reports on TIPS-pentacene thin films exhibiting an activation energy (Ea) as low as 15 meV when the active material is blended with PS and processed via BAMS. Such an unprecedentedly low value originates not only from a decrease in the interfacial trap densities but also from trapping energies much shallower than previously reported elsewhere for the same material. This allows us to clarify the previously reported notion that significant passivation of interfacial traps occurs following the separation of PS from TIPS-pentacene into an individual layer at the interface with the insulator and to confirm that the enhanced transport originates from a synergistic effect wherein both trapping density and depth are reduced.

Background: Patient-derived extracellular vesicles (EVs) that contains a complex biological cargo is a valuable source of liquid biopsy diagnostics to aid in early detection, cancer screening, and precision nanotherapeutics. In this study, we predicted that coupling cancer patient blood-derived EVs to timeresolved spectroscopy and artificial intelligence (AI) could provide a robust cancer screening and followup tools. Methods:In our pilot study, fluorescence correlation spectroscopy (FCS) measurements were performed on 24 blood samples-derived EVs. Blood samples were obtained from 15 cancer patients (presenting 5 different types of cancers), and 9 healthy controls (including patients with benign lesions). EVs samples were labeled with PKH67 dye. The obtained FCS autocorrelation spectra were processed into power spectra using the Fast-Fourier Transform algorithm. The processed power spectra were subjected to various machine learning algorithms to distinguish cancer spectra from healthy control spectra. Results and Applications:The performance of AdaBoost Random Forest (RF) classifier, support vector machine, and multilayer perceptron, were tested on selected frequencies in the N=118 power spectra. The RF classifier exhibited a 90% classification accuracy and high sensitivity and specificity in distinguishing the FCS power spectra of cancer patients from those of healthy controls. Further, an image convolutional neural network (CNN), ResNet network, and a quantum CNN were assessed on the power spectral images as additional validation tools. All image-based CNNs exhibited a nearly equal classification performance with an accuracy of roughly 82% and reasonably high sensitivity and specificity scores. Conclusion:Our pilot study demonstrates that AI-algorithms coupled to time-resolved FCS power spectra can accurately and differentially classify the complex patient-derived EVs from different cancer samples of distinct tissue subtypes. As such, our findings hold promise in the diagnostic and prognostic screening in clinical medicine. INTRODUCTION.Cancers globally remain amidst the leading-cause of disease-related mortality. Conventional therapies may be successful for certain subtypes of the disease, while others are complex adaptive systems progressing to clinically aggressive stages causing a paramount disease burden. Further, the long-term health complications and side-effects, successfully treated patients must live with, must be emphasized. Within this pilot study, in efforts to advance precision oncology and patient-centered clinical medicine, we explored the application of artificial intelligence (AI) in tackling one of the greatest challenges in preventive and diagnostic medicine: early cancer detection and prognostic screening. Cancer biomarker discovery was pioneered by Gold and Freedman (1965) with their recognition of the first tumor marker, Carcinoembryonic Antigen (CEA), which remains to date the most used clinically-relevant, blood-based cancer screening and diagnostic in patient-care. Their co-discovery o...

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One key challenge in the field of organic semiconductors (OSCs) and their application in organic electronics is controlling the electronic properties of the OSC by p-/n-doping in a predictable manner. It has been established that upon dopant admixture either integer or fractional charge transfer can occur, where the parameters deciding one over the other are still under debate. In numerous studies integer charge transfer was found for conjugated polymers, while small molecular OSCs show a tendency towards fractional charge transfer forming ground state charge transfer complexes (1). The typical approach to assure integer charge transfer is using molecular dopants with a high electron affinity, exceeding the ionization energy of the OSC (for p-doping), as the dopant then exhibits an empty orbital lying lower in energy than the occupied frontier molecular orbital of the semiconductor. Recently, however, a transition between the two scenarios has been reported for polythiophene and the strong molecular p-dopant F4TCNQ, where aging of the samples appeared to promote the formation of charge-transfer complexes showing fractional charge transfer (2). Another study highlighted the key role of the microstructure for the doping process, where different polymorphs show either fractional or integer charge transfer (3). This lack of predictability of traditional doping employing strong donors/acceptors recently led to intense research efforts to find alternative approaches. There, using Lewis acids (LAs) as molecular p-dopants for OSCs has been found to be particularly promising, as these compounds react selectively with Lewis bases to form robust adducts. This makes LAs, in principle, chemically selective dopants that attack OSCs only at the sites where electron lone pairs are located. Interestingly, despite an electron affinity far below the ionization energy of polythiophene, it has been found that the LA BCF can readily ionize this material, which is incompatible with the conventional picture of integer charge transfer as detailed above (4). Very recently, it has been suggested that LA doping does not follow from the formation of an adduct, but instead, that the LA simply act as a protonating agent after forming a complex with water and, hence, becoming a Br∅nsted acid (5). In this presentation, I will briefly review these ideas and discuss how the concepts of electron affinity, ionization energy, integer, and partial charge transfer as used in the field relate to the doping using LAs. By correlating experimental data from optical and infrared spectroscopy with DFT calculations, I will provide alternative descriptors that allow to clarify the discussion and, more importantly, that have predictive power towards the use of Lewis acids as dopants for OSCs. Finally, I will show that the use of these descriptors, in the case of polythiophene doped with BCF discussed above (4), lift the apparent theoretical inconsistency, and as such, will be of high value for a comprehensive larger-scale exploration of OSCs doping using LAs. 1. Salzmann et al., Acc. Chem. Res. 2016, 49, 370 2. Watts et al., Chem. Mater. 2019, 31, 6986−6994 3. Jacobs et al., Materials Horizons. 2018, 5, 655 4. P. Pingel et al., Adv. Electron. Mater. 2016, 2:1600204. 5. B. Yurash et al., Nature Materials. 2019, 18, 1327–1334