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Alkali treatments are crucial for low bandgap (Ag,Cu)InSe2 (ACIS) and Cu(In,Ga)Se2‐based solar cell performance. Traditionally, Ag‐alloying of CIS (ACIS) is grown on soda‐lime glass (SLG) at temperatures exceeding 500 °C, resulting in uncontrolled alkali diffusion from the substrate and variable photovoltaic properties. A substrate‐independent low‐bandgap ACIS growth process is introduced and the impact of controlled supplies of NaF and RbF alkali fluorides before and after absorber growth through precursor layers and post‐deposition treatments (PDT) are investigated. NaF and RbF precursor layers enhance carrier lifetimes and doping density, outperforming the previous SLG‐dependent strategy. Even small quantities of RbF significantly enhance device performance, while specific NaF amount during deposition are necessary to limit grain growth and achieve high doping densities and lifetimes. A certain density of grain boundaries appears crucial for high doping levels. Although subsequent NaF post‐deposition treatment (PDT) does not provide additional benefits with sufficient Na during growth, RbF‐PDT remains crucial. The best performance is achieved with a combination of NaF and RbF precursor layers along with RbF‐PDT, resulting in over 19% efficiency, 605 mV open‐circuit voltage (VOC), 73% fill factor (FF), a carrier density of 3 × 1016 cm−3, and a 700 ns lifetime. This approach supports high‐efficiency ACIS solar cell advancement, particularly for thin‐film tandem photovoltaic devices. [ABSTRACT FROM AUTHOR]

Objective: Our objective was to evaluate the relationship between scalp‐EEG and stereoelectroencephalography (SEEG) seizure‐onset patterns (SOP) in patients with MRI‐negative drug‐resistant focal epilepsy. Methods: We analyzed retrospectively 41 patients without visible lesion on brain MRI who underwent video‐EEG followed by SEEG. We defined five types of SOPs on scalp‐EEG and eight types on SEEG. We examined how various clinical variables affected scalp‐EEG SOPs. Results: The most prevalent scalp SOPs were rhythmic sinusoidal activity (56.8%), repetitive epileptiform discharges (22.7%), and paroxysmal fast activity (15.9%). The presence of paroxysmal fast activity on scalp‐EEG was always seen without delay from clinical onset and correlated with the presence of low‐voltage fast activity in SEEG (sensitivity = 22.6%, specificity = 100%). The main factor explaining the discrepancy between the scalp and SEEG SOPs was the delay between clinical and scalp‐EEG onset. There was a correlation between the scalp and SEEG SOPs when the scalp onset was simultaneous with the clinical onset (p = 0.026). A significant delay between clinical and scalp discharge onset was observed in 25% of patients and featured always with a rhythmic sinusoidal activity on scalp, corresponding to similar morphology of the discharge on SEEG. The presence of repetitive epileptiform discharges on scalp was associated with an underlying focal cortical dysplasia (sensitivity = 30%, specificity = 90%). There was no significant association between the scalp SOP and the epileptogenic zone location (deep or superficial), or surgical outcome. Significance: In patients with MRI‐negative focal epilepsy, scalp SOP could suggest the SEEG SOP and some etiology (focal cortical dysplasia) but has no correlation with surgical prognosis. Scalp SOP correlates with the SEEG SOP in cases of simultaneous EEG and clinical onset; otherwise, scalp SOP reflects the propagation of the SEEG discharge. Plain Language Summary: We looked at the correspondence between the electrical activity recorded during the start of focal seizure using scalp and intracerebral electrodes in patients with no visible lesion on MRI. If there is a fast activity on scalp, it reflects similar activity inside the brain. We found a good correspondence between scalp and intracerebral electrical activity for cases without significant delay between clinical and scalp electrical onset (seen in 75% of the cases we studied). Visualizing repetitive epileptic activity on scalp could suggest a particular cause of the epilepsy: a subtype of brain malformation called focal cortical dysplasia. [ABSTRACT FROM AUTHOR]

Nodular heterotopia (NH)–related drug‐resistant epilepsy is challenging due to the deep location of the NH and the complexity of the underlying epileptogenic network. Using ictal stereo‐electroencephalography (SEEG) and functional connectivity (FC) analyses in 14 patients with NH‐related drug‐resistant epilepsy, we aimed to determine the leading structure during seizures. For this purpose, we compared node IN and OUT strength between bipolar channels inside the heterotopia and inside gray matter, at the group level and at the individual level. At seizure onset, the channels within NH belonging to the epileptogenic and/or propagation network showed higher node OUT‐strength than the channels within the gray matter (p =.03), with higher node OUT‐strength than node IN‐strength (p =.03). These results are in favor of a "leading" role of NH during seizure onset when involved in the epileptogenic‐ or propagation‐zone network (50% of patients). However, when looking at the individual level, no significant difference between NH and gray matter was found, except for one patient (in two of three seizures). This result confirms the heterogeneity and the complexity of the epileptogenic network organization in NH and the need for SEEG exploration to characterize more precisely patient‐specific epileptogenic network organization. [ABSTRACT FROM AUTHOR]

Objective: Quantification of the epileptogenic zone network (EZN) most frequently implies analysis of seizure onset. However, important information can also be obtained from the postictal period, characterized by prominent changes in the EZN. We used permutation entropy (PE), a measure of signal complexity, to analyze the peri‐ictal stereoelectroencephalography (SEEG) signal changes with emphasis on the postictal state. We sought to determine the best PE‐derived parameter (PEDP) for identifying the EZN. Methods: Several PEDPs were computed retrospectively on SEEG‐recorded seizures of 86 patients operated on for drug‐resistant epilepsy: mean baseline preictal entropy, minimum ictal entropy, maximum postictal entropy, the ratio between the maximum postictal and the minimum ictal entropy, and the ratio between the maximum postictal and the baseline preictal entropy. The performance of each biomarker was assessed by comparing the identified epileptogenic contacts or brain regions against the EZN defined by clinical analysis incorporating the Epileptogenicity Index and the connectivity epileptogenicity index methods (EZNc), using the receiver‐operating characteristic and precision‐recall. Results: The ratio between the maximum postictal and the minimum ictal entropy (defined as the Permutation Entropy Index [PEI]) proved to be the best‐performing PEDP to identify the EZNC. It demonstrated the highest area under the curve (AUC) and F1 score at the contact level (AUC 0.72; F1 0.39) and at the region level (AUC 0.78; F1 0.47). PEI values gradually decreased between the EZN, the propagation network, and the non‐involved regions. PEI showed higher performance in patients with slow seizure‐onset patterns than in those with fast seizure‐onset patterns. The percentage of resected epileptogenic regions defined by PEI was significantly correlated with surgical outcome. Significance: PEI is a promising tool to improve the delineation of the EZN. PEI combines ease and robustness in a routine clinical setting with high sensitivity for seizures without fast activity at seizure onset. [ABSTRACT FROM AUTHOR]