Hebbian Research Articles

Input-output specific orchestration of aversive valence in lateral habenula during stress dynamics.

Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb. LHb subregions also separately assign aversive valence via dissociable projections to the downstream targets in the midbrain which provides feedback loops. Despite these strides, the spatiotemporal dynamics of LHb-centric neural circuits remain elusive during the progression of depression-like state under stress. In this review, we attempt to describe a framework in which LHb orchestrates aversive valence via the input-output specific neuronal architecture. Notably, a physiological form of Hebbian plasticity in LHb under multiple stressors has been unveiled to incubate neuronal hyperactivity in an input-specific manner, which causally encodes chronic stress experience and drives depression onset. Collectively, the recent progress and future efforts in elucidating LHb circuits shed light on early interventions and circuit-specific antidepressant therapies.

Theory of Coupled Neuronal-Synaptic Dynamics

In neural circuits, synaptic strengths influence neuronal activity by shaping network dynamics, and neuronal activity influences synaptic strengths through activity-dependent plasticity. Motivated by this fact, we study a recurrent-network model in which neuronal units and synaptic couplings are interacting dynamic variables, with couplings subject to Hebbian modification with decay around quenched random strengths. Rather than assigning a specific role to the plasticity, we use dynamical mean-field theory and other techniques to systematically characterize the neuronal-synaptic dynamics, revealing a rich phase diagram. Adding Hebbian plasticity slows activity in already chaotic networks and can induce chaos in otherwise quiescent networks. Anti-Hebbian plasticity quickens activity and produces an oscillatory component. Analysis of the Jacobian shows that Hebbian and anti-Hebbian plasticity push locally unstable modes toward the real and imaginary axes, respectively, explaining these behaviors. Both random-matrix and Lyapunov analysis show that strong Hebbian plasticity segregates network timescales into two bands, with a slow, synapse-dominated band driving the dynamics, suggesting a flipped view of the network as synapses connected by neurons. For increasing strength, Hebbian plasticity initially raises the complexity of the dynamics, measured by the maximum Lyapunov exponent and attractor dimension, but then decreases these metrics, likely due to the proliferation of stable fixed points. We compute the marginally stable spectra of such fixed points as well as their number, showing exponential growth with network size. Finally, in chaotic states with strong Hebbian plasticity, a stable fixed point of neuronal dynamics is destabilized by synaptic dynamics, allowing any neuronal state to be stored as a stable fixed point by halting the plasticity. This phase of freezable chaos offers a new mechanism for working memory. Published by the American Physical Society 2024

Modeling and contractivity of neural-synaptic networks with Hebbian learning

This paper is concerned with the modeling and analysis of two of the most commonly used recurrent neural network models (i.e., Hopfield neural network and firing-rate neural network) with dynamic recurrent connections undergoing Hebbian learning rules. To capture the synaptic sparsity of neural circuits we propose a low dimensional formulation. We then characterize certain key dynamical properties. First, we give biologically-inspired forward invariance results. Then, we give sufficient conditions for the non-Euclidean contractivity of the models. Our contraction analysis leads to stability and robustness of time-varying trajectories — for networks with both excitatory and inhibitory synapses governed by both Hebbian and anti-Hebbian rules. For each model, we propose a contractivity test based upon biologically meaningful quantities, e.g., neural and synaptic decay rate, maximum in-degree, and the maximum synaptic strength. Then, we show that the models satisfy Dale’s Principle. Finally, we illustrate the effectiveness of our results via a numerical example.

Predictive coding networks for temporal prediction.

One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of predictive coding. Although the original proposals of predictive coding have discussed temporal prediction, later work developing this theory mostly focused on static stimuli, and key questions on neural implementation and computational properties of temporal predictive coding networks remain open. Here, we address these questions and present a formulation of the temporal predictive coding model that can be naturally implemented in recurrent networks, in which activity dynamics rely only on local inputs to the neurons, and learning only utilises local Hebbian plasticity. Additionally, we show that temporal predictive coding networks can approximate the performance of the Kalman filter in predicting behaviour of linear systems, and behave as a variant of a Kalman filter which does not track its own subjective posterior variance. Importantly, temporal predictive coding networks can achieve similar accuracy as the Kalman filter without performing complex mathematical operations, but just employing simple computations that can be implemented by biological networks. Moreover, when trained with natural dynamic inputs, we found that temporal predictive coding can produce Gabor-like, motion-sensitive receptive fields resembling those observed in real neurons in visual areas. In addition, we demonstrate how the model can be effectively generalized to nonlinear systems. Overall, models presented in this paper show how biologically plausible circuits can predict future stimuli and may guide research on understanding specific neural circuits in brain areas involved in temporal prediction.

Motion Learning and Generalization of Musculoskeletal Robot Using Gain Primitives

Organisms have an innate ability to rapidly produce diverse and flexible movement. Biological motor systems are composed of highly redundant muscle actuators and have strongly nonlinear dynamic characteristics. How organisms cope with such complex system is still an open question. Motor primitive theory proposed by neuroscientists has provided a convincing explanation for this extraordinary ability and have supported by several biological evidence. In this paper, based on the gain primitive model of cortical network proposed by neuroscientists, new algorithms for learning and combining gain primitives were designed to control musculoskeletal and robotic system with highly redundant actuators. It can realize dimensionality reduction of controll from the number of actuators to the number of primitives. A parameter adaptation algorithm inspired by monoamines modulation mechanism was applied to improve the learning efficiency of gain primitive. Learned motion experiences and primitives were introduced to effectively generate new movements. Validation experiments were carried out on a musculoskeletal model with 9 muscles and an articulated robot Baxter with redundant actuators. The experimental results demonstrated that the recurrent neural network modulated by gain primitives can generate high-dimensional control signals and realize efficient motion generalization for practical systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper applied a biologically plausible control method with gain primitive to practical systems with highly redundant actuators. To reduce the cumulative error occurring in learning gain primitive for control networks with high dimensional output, a bioinspired Hebbian learning rule was applied to train a recurrent neural network for optimizing the gain primitive. During the training process, systematic entropy was introduced as an index to adaptively adjust learning parameters, so as to reduce the exploration cost and improve the learning stability of the system. To realize easy implementation and generalization, prior motion experiences were introduced into the initialization of the optimization algorithm, and motion generalization was realized by using the particle swarm optimization algorithm. The algorithms designed in this paper are applicable to complex systems with highly redundant actuators such as humanoid robots.

Theta Phase Entrainment of Single-Cell Spiking in Rat Somatosensory Barrel Cortex and Secondary Visual Cortex Is Enhanced during Multisensory Discrimination Behavior.

Phase entrainment of cells by theta oscillations is thought to globally coordinate the activity of cell assemblies across different structures, such as the hippocampus and neocortex. This coordination is likely required for optimal processing of sensory input during recognition and decision-making processes. In quadruple-area ensemble recordings from male rats engaged in a multisensory discrimination task, we investigated phase entrainment of cells by theta oscillations in areas along the corticohippocampal hierarchy: somatosensory barrel cortex (S1BF), secondary visual cortex (V2L), perirhinal cortex (PER), and dorsal hippocampus (dHC). Rats discriminated between two 3D objects presented in tactile-only, visual-only, or both tactile and visual modalities. During task engagement, S1BF, V2L, PER, and dHC LFP signals showed coherent theta-band activity. We found phase entrainment of single-cell spiking activity to locally recorded as well as hippocampal theta activity in S1BF, V2L, PER, and dHC. While phase entrainment of hippocampal spikes to local theta oscillations occurred during sustained epochs of task trials and was nonselective for behavior and modality, somatosensory and visual cortical cells were only phase entrained during stimulus presentation, mainly in their preferred modality (S1BF, tactile; V2L, visual), with subsets of cells selectively phase-entrained during cross-modal stimulus presentation (S1BF: visual; V2L: tactile). This effect could not be explained by modulations of firing rate or theta amplitude. Thus, hippocampal cells are phase entrained during prolonged epochs, while sensory and perirhinal neurons are selectively entrained during sensory stimulus presentation, providing a brief time window for coordination of activity.

Chemical prelithiation of SiOx/Gr anode for improved cycling performance in lithium-ion batteries

Owing to its high specific capacity and excellent cycling performance, SiOx is an appealing alternative to traditional graphite anodes. In order to increase the anode's cycling stability, this work demonstrates the chemical prelithiation of SiOx/Gr anode for real-world applications. The highest ICE obtained is 98.8 % using the chemical prelithiation method for half cell. The prelithiated anode was characterized using various techniques, and the viability of the process was tested using a pouch cell assembly, demonstrating enhanced capacity compared to the pristine pouch cell. This research has implications for portable electronics, electric vehicles, renewable energy storage, and other applications requiring high-capacity and long-lasting batteries.

Making Room for Silicon: Including SiOx in a Graphite-Based Anode Formulation and Harmonization in 1 Ah Cells

Transitioning to more ambitious electrode formulations facilitates developing high-energy density cells, potentially fulfilling the demands of electric car manufacturers. In this context, the partial replacement of the prevailing anode active material in lithium-ion cells, graphite, with silicon-based materials enhances its capacity. Nevertheless, this requires adapting the rest of the components and harmonizing the electrode integration in the cell to enhance the performance of the resulting high-capacity anodes. Herein, starting from a replacement in the standard graphite anode recipe with 22% silicon suboxide at laboratory scale, the weight fraction of the electrochemically inactive materials was optimized to 2% carbon black/1% dispersant/3% binder combination before deriving an advantage from including single-wall carbon nanotubes in the formulation. In the second part, the recipe was upscaled to a semi-industrial electrode coating and cell assembly line. Then, 1 Ah lithium-ion pouch cells were filled and tested with different commercial electrolytes, aiming at studying the dependency of the Si-based electrodes on the additives included in the composition. Among all the electrolytes employed, the EL2 excelled in terms of capacity retention, obtaining a 48% increase in the number of cycles compared to the baseline electrolyte formulation above the threshold capacity retention value (80% state of health).

A spectral approach to Hebbian-like neural networks

We consider the Hopfield neural network as a model of associative memory and we define its neuronal interaction matrix J as a function of a set of K×M binary vectors {ξμ,A}μ=1,...,KA=1,...,M representing a sample of the reality that we want to retrieve. In particular, any item ξμ,A is meant as a corrupted version of an unknown ground pattern ζμ, that is the target of our retrieval process. We consider and compare two definitions for J, referred to as supervised and unsupervised, according to whether the class μ, each example belongs to, is unveiled or not, also, these definitions recover the paradigmatic Hebb's rule under suitable limits. The spectral properties of the resulting matrices are studied and used to inspect the retrieval capabilities of the related models as a function of their control parameters.

Control-Etched Ti3C2Tx MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation.

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.

Exploring the synergistic potential of mixed metal sulfides in hybrid supercapacitors

In energy reservoirs, hybrid supercapacitors (HSCs) have gained incredible recognition owing to their exceptional specific energy (Es), Specific power (Ps), and high cyclic stability. However, electroactive electrode materials with flashing electrochemical features and high storage ability are still required. In this regard, nickel-cobalt binary sulfide composites need further exploration as an electrode material owing to their high electrochemical properties, better conductivity, excellent redox reversibility, and high-rate capability. Here, we account the electrochemical activities and synthesis of composites including (nickel and cobalt sulfides). The composites were analyzed for structural and morphological analysis using XRD and SEM techniques, respectively. Amidst all other compositions, Ni0.75Co0.25S exhibited the optimum electrochemical performance measured in half-cell assembly with specific capacity (Qs) of 1147.6 Cg−1 and 900 Cg−1 at lowest scan rate and current density respectively. This electroactive electrode material together with activated carbon (AC) was further utilized in two cell assembly. This Hybrid device revealed ES of 50.21 Whkg−1 and Ps of 9350 Wkg−1 with capacity retention of 80.30% after consecutive charge discharge. The performance analysis of the actual device was further examined using a simulation approach, focusing on both capacitive and diffusive contributions by comparing the results of both linear and quadratic models. These attractive results revealed by Ni0.75Co0.25S signify it as favorable electrode material for hybrid supercapacitor applications.

Markov Blankets and Mirror Symmetries-Free Energy Minimization and Mesocortical Anatomy.

A theoretical account of development in mesocortical anatomy is derived from the free energy principle, operating in a neural field with both Hebbian and anti-Hebbian neural plasticity. An elementary structural unit is proposed, in which synaptic connections at mesoscale are arranged in paired patterns with mirror symmetry. Exchanges of synaptic flux in each pattern form coupled spatial eigenmodes, and the line of mirror reflection between the paired patterns operates as a Markov blanket, so that prediction errors in exchanges between the pairs are minimized. The theoretical analysis is then compared to the outcomes from a biological model of neocortical development, in which neuron precursors are selected by apoptosis for cell body and synaptic connections maximizing synchrony and also minimizing axonal length. It is shown that this model results in patterns of connection with the anticipated mirror symmetries, at micro-, meso- and inter-arial scales, among lateral connections, and in cortical depth. This explains the spatial organization and functional significance of neuron response preferences, and is compatible with the structural form of both columnar and noncolumnar cortex. Multi-way interactions of mirrored representations can provide a preliminary anatomically realistic model of cortical information processing.

Metal Phthalocyanine Sensitized Photoelectrochemical Cell Assembling with Azide‐Functionalized Reduced Graphene and Quaternary Metal Chalcogenide Composites

AbstractHere, photoelectrodes of photoelectrochemical (PEC) cells consisting of azide functionalized reduced graphene oxide (GO‐N3) and quaternary metal chalcogenide (CdZnNiSSe) composites sensitized with metal phthalocyanines (MPc, M: Co, Zn, Ti) are developed and then tested in hydrogen evolution reaction (HER). CdZnNiSSe/RGO‐N3 composite is deposited on an indium tin oxide (ITO) electrode through a facile electrodeposition technique and ITO/CdZnNiSSe/RGO‐N3 photoelectrode is constructed. Then, MPcs bearing terminal alkyne groups are connected to RGO‐N3 via the copper(I)‐catalyzed azide‐alkyne cycloaddition (click chemistry) technique to produce ITO/CdZnNiSSe/RGO‐N3‐MPc structures. Decoration of ITO/CdZnNiSSe/RGO‐N3 with MPcs is proposed to improve the charge transfer capability of the photoelectrode due to wide light absorption of MPcs from UV toward the IR region of the light spectrum. PECHER responses indicate that among the photoanodes bearing ZnPc, CoPc, and TiOPc, ITO/CdZnNiSSe/RGO‐N3‐ZnPc electrode exhibits the highest PECHER performance owing to the suitability of band structure of ZnPc. Sensitizing ITO/CdZnNiSSe/RGO‐N3 with ZnPc increases the photocurrent density from 5.0 to 7.3 mA cm−2 at 0.8 V vs. RHE and the applied bias photon to current efficiency (ABPE) enhances from 3.18 % to 3.87 %. This study provides a new approach for increasing the performance of photoanodes via sensitization with MPcs in photoelectrochemical hydrogen evolution processes for future applications.

Bioorthogonal In Situ Polymerization of Dendritic Agents for Hijacking Lysosomes and Enhancing Antigen Presentation in Cancer Cells.

A low-generation lysine dendrimer, SPr-G2, responds to intracellular glutathione to initiate bioorthogonal in situ polymerization, resulting in the formation of large assemblies in mouse breast cancer cells. The intracellular large assemblies of SPr-G2 can interact with lysosomes to induce lysosome expansion and enhance lysosomal membrane permeabilization, leading to major histocompatibility complex class I upregulation on tumor cell surfaces and ultimately tumor cell death. Moreover, the use of the SPr-G2 dendrimer to conjugate the chemotherapeutic drug, camptothecin (CPT), can boost the therapeutic potency of CPT. Excellent antitumor effects in vitro and in vivo are obtained from the combinational treatment of the SPr-G2 dendrimer and CPT. This combinational effect also enhances antitumor immunity through promoting activation of cytotoxic T cells in tumor tissues and maturation of dendritic cells. This study can shed new light on the development of peptide dendritic agents for cancer therapy.

What Do Hebbian Learners Learn? Reduction Axioms for Iterated Hebbian Learning

This paper is a contribution to neural network semantics, a foundational framework for neuro-symbolic AI. The key insight of this theory is that logical operators can be mapped to operators on neural network states. In this paper, we do this for a neural network learning operator. We map a dynamic operator [φ] to iterated Hebbian learning, a simple learning policy that updates a neural network by repeatedly applying Hebb's learning rule until the net reaches a fixed-point. Our main result is that we can "translate away" [φ]-formulas via reduction axioms. This means that completeness for the logic of iterated Hebbian learning follows from completeness of the base logic. These reduction axioms also provide (1) a human-interpretable description of iterated Hebbian learning as a kind of plausibility upgrade, and (2) an approach to building neural networks with guarantees on what they can learn.

Sleep-Like Unsupervised Replay Improves Performance When Data Are Limited or Unbalanced (Student Abstract)

The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples. Here, we investigated the role of sleep in improving the performance of ANNs trained with limited data on the MNIST and Fashion MNIST datasets. Sleep was implemented as an unsupervised phase with local Hebbian type learning rules. We found a significant boost in accuracy after the sleep phase for models trained with limited data in the range of 0.5-10% of total MNIST or Fashion MNIST datasets. When more than 10% of the total data was used, sleep alone had a slight negative impact on performance, but this was remedied by fine-tuning on the original data. This study sheds light on a potential synaptic weight dynamics strategy employed by the brain during sleep to enhance memory performance when training data are limited or imbalanced.

Chromosome level genome assembly and transcriptome analysis of E11 cells infected by tilapia lake virus

The E11 cell line, derived from striped snakehead fish (Channa striata), possesses a distinctive feature: it is persistently infected with a C-type retrovirus. Notably, it exhibits high permissiveness to piscine nodavirus and the emerging tilapia lake virus (TiLV). Despite its popularity in TiLV research, the absence of genome assembly for the E11 cell line and Channa striata has constrained research on host-virus interactions. This study aimed to fill this gap by sequencing, assembling, and annotating the E11 cell line genome. Our efforts yielded a 600.5 Mb genome including 24 chromosomes with a BUSCO score of 98.8%. In addition, the complete proviral DNA sequence of snakehead retrovirus (SnRV) was identified in the E11 cell genome. Comparative genomic analysis between the E11 cell line and another snakehead species Channa argus revealed the loss of many immune-related gene families in the E11 cell genome, indicating a compromised immune response. We also conducted transcriptome analysis of mock- and TiLV-infected E11 cells, unveiling new perspectives on virus-virus and host-virus interactions. The TiLV infection suppressed the high expression of SnRV in E11 cells, and activated some other endogenous retroviruses. The protein-coding gene comparison revealed a pronounced up-regulation of genes involved in immune response, alongside a down-regulation of genes associated with specific metabolic processes.In summary, the genome assembly and annotation of the E11 cell line provide valuable resources to understand the SnRV and facilitate further studies on nodavirus and TiLV. The RNA-seq profiles shed light on the cellular mechanisms employed by fish cells in response to viral challenges, potentially guiding the development of therapeutic strategies against TiLV in aquaculture. This study also provides the first insights into the viral transcriptome profiles of endogenous SnRV and evading TiLV, enhancing our understanding of host-virus interactions in fish.

Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices

Abstract This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily.

Coupling of Sharp Wave Events between Zebrafish Hippocampal and Amygdala Homologs.

The mammalian hippocampus exhibits spontaneous sharp wave events (1-30 Hz) with an often-present superimposed fast ripple oscillation (120-220 Hz) to form a sharp wave ripple (SWR) complex. During slow-wave sleep or quiet restfulness, SWRs result from the sequential spiking of hippocampal cell assemblies initially activated during learned or imagined experiences. Additional cortical/subcortical areas exhibit SWR events that are coupled to hippocampal SWRs, and studies in mammals suggest that coupling may be critical for the consolidation and recall of specific memories. In the present study, we have examined juvenile male and female zebrafish and show that SWR events are intrinsically generated and maintained within the telencephalon and that their hippocampal homolog, the anterodorsolateral lobe (ADL), exhibits SW events with ∼9% containing an embedded ripple (SWR). Single-cell calcium imaging coupled to local field potential recordings revealed that ∼10% of active cells in the dorsal telencephalon participate in any given SW event. Furthermore, fluctuations in cholinergic tone modulate SW events consistent with mammalian studies. Moreover, the basolateral amygdala (BLA) homolog exhibits SW events with ∼5% containing an embedded ripple. Computing the SW peak coincidence difference between the ADL and BLA showed bidirectional communication. Simultaneous coupling occurred more frequently within the same hemisphere, and in coupled events across hemispheres, the ADL more commonly preceded BLA. Together, these data suggest conserved mechanisms across species by which SW and SWR events are modulated, and memories may be transferred and consolidated through regional coupling.

Influence of impregnation time on TiO2 films sensitized with N719 and 2-Mercaptobenzothiazole: An electrochemical analysis

Dye sensitized solar cells (DSSC) are low-cost photovoltaic devices with easy production. They operate with a photoanode, a photocathode, and an electrolyte responsible for charge permeation within the cell. Recent research focus in DSSCs has aimed at enhancing their energy conversion properties, such as the insertion of new materials to reduce electronic recombination, utilization of gel electrolytes, dye synthesis, and studying cell assembly parameters. This study examined the effects of various immersion durations (6, 12, and 24 h) on TiO2 semiconductor films immersed in solutions containing charge carriers (N719 and 2-Mercaptobenzothiazole). To characterize the electrochemical properties, analyses such as current density versus potential curves (j-V), Electrochemical Impedance Spectroscopy (EIS), charge extraction, Intensity-Modulated Photovoltage (IMVS), and Photocurrent Spectroscopy (IMPS) were performed. It was found that the cell with 24 h of impregnation, presented a higher current density, consequently increasing efficiency (η = 5.76%). Impedance analyses showed that the 24-h samples facilitated charge transport, reducing cell resistance, consistent with charge extraction measurements. Lastly, IMPS and IMVS tests revealed that 24 h of immersion generated shorter collection times (τc = 0.016 ± 0.01) and longer electronic recombination times (τr = 0.07 ± 0.007), aiding the charge transport process.