Energy band offset extraction - a comparative study

Structural quantum confinement in thin silicon double-gate MOSFETs has been quantified using the temperature dependence of the subthreshold current. The results were compared with the shifts in threshold voltage. Data was obtained from simulations after initial verification with experimental data. This study demonstrates that with the temperature dependence of the subthreshold current, shifts in the valence and conduction band edge can be extracted separately from changes in mobility and density of states, making this method more accurate than the commonly used threshold voltage method

Selective Functionalization with PNA of Silicon Nanowires on Silicon Oxide Substrates

Silicon nanowire chips can function as sensors for cancer DNA detection, whereby selective functionalization of the Si sensing areas over the surrounding silicon oxide would prevent loss of analyte and thus increase the sensitivity. The thermal hydrosilylation of unsaturated carbon-carbon bonds onto H-terminated Si has been studied here to selectively functionalize the Si nanowires with a monolayer of 1,8-nonadiyne. The silicon oxide areas, however, appeared to be functionalized as well. The selectivity toward the Si-H regions was increased by introducing an extra HF treatment after the 1,8-nonadiyne monolayer formation. This step (partly) removed the monolayer from the silicon oxide regions, whereas the Si-C bonds at the Si areas remained intact. The alkyne headgroups of immobilized 1,8-nonadiyne were functionalized with PNA probes by coupling azido-PNA and thiol-PNA by click chemistry and thiol-yne chemistry, respectively. Although both functionalization routes were successful, hybridization could only be detected on the samples with thiol-PNA. No fluorescence was observed when introducing dye-labeled noncomplementary DNA, which indicates specific DNA hybridization. These results open up the possibilities for creating Si nanowire-based DNA sensors with improved selectivity and sensitivity.</p

Extracting energy band offsets on Thin Silicon-On-Insulator MOSFETs

Structural quantum confinement in thin silicon double-gate MOSFETs has been quantified using the temperature dependence of the subthreshold current. The results were compared with the shifts in threshold voltage. Data was obtained from simulations after initial verification with experimental data. This study demonstrates that with the temperature dependence of the subthreshold current, shifts in the valence and conduction band edge can be extracted separately from changes in mobility and density of states, making this method more accurate than the commonly used threshold voltage method

Extracting energy band offsets on long-channel thin silicon-on-insulator MOSFETs

Structural quantum confinement in long-channel thin silicon-on-insulator MOSFETs has been quantified using the temperature dependence of the subthreshold current. The results were compared with the shifts in the threshold voltage. Data were obtained from simulations after initial verification with experimental data. This study demonstrates that, with the temperature dependence of the subthreshold current, shifts in the valence and conduction band edge can be extracted distinctively from changes in mobility and density of states (DOS), making this method more accurate in assessing the impact of structural quantum confinement than the commonly used threshold voltage method. Furthermore, we show that, with additional C–V data, a possible change in mobility and DOS can be disentangled

Selective Functionalization with PNA of Silicon Nanowires on Silicon Oxide Substrates

Silicon nanowire chips can function as sensors for cancer DNA detection, whereby selective functionalization of the Si sensing areas over the surrounding silicon oxide would prevent loss of analyte and thus increase the sensitivity. The thermal hydrosilylation of unsaturated carbon-carbon bonds onto H-terminated Si has been studied here to selectively functionalize the Si nanowires with a monolayer of 1,8-nonadiyne. The silicon oxide areas, however, appeared to be functionalized as well. The selectivity toward the Si-H regions was increased by introducing an extra HF treatment after the 1,8-nonadiyne monolayer formation. This step (partly) removed the monolayer from the silicon oxide regions, whereas the Si-C bonds at the Si areas remained intact. The alkyne headgroups of immobilized 1,8-nonadiyne were functionalized with PNA probes by coupling azido-PNA and thiol-PNA by click chemistry and thiol-yne chemistry, respectively. Although both functionalization routes were successful, hybridization could only be detected on the samples with thiol-PNA. No fluorescence was observed when introducing dye-labeled noncomplementary DNA, which indicates specific DNA hybridization. These results open up the possibilities for creating Si nanowire-based DNA sensors with improved selectivity and sensitivity

Rekeneisen voor het middelbaar beroepsonderwijs

De mbo-sector heeft aangegeven met een eigen rekenaanpak te komen. In dat kader heeft het ministerie van OCW in samenspraak met de MBO Raad en NRTO een Expertgroep Herijking Rekeneisen mbo ingesteld om te komen tot een beschrijving van rekeneisen per mbo-niveau die in vorm en inhoud recht doen aan het eigen karakter van het mbo. In dit rapport treft u de beschrijving van die rekeneisen aa

Rekeneisen voor het middelbaar beroepsonderwijs

De mbo-sector heeft aangegeven met een eigen rekenaanpak te komen. In dat kader heeft het ministerie van OCW in samenspraak met de MBO Raad en NRTO een Expertgroep Herijking Rekeneisen mbo ingesteld om te komen tot een beschrijving van rekeneisen per mbo-niveau die in vorm en inhoud recht doen aan het eigen karakter van het mbo. In dit rapport treft u de beschrijving van die rekeneisen aa

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Background: Endometrial cancer can be molecularly classified into POLEmut, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication. Methods: This combined analysis included diagnostic haematoxylin and eosin-stained slides and molecular and clinicopathological data from 2028 patients with intermediate-to-high-risk endometrial cancer from the PORTEC-1 (n=466), PORTEC-2 (n=375), and PORTEC-3 (n=393) randomised trials and the TransPORTEC pilot study (n=110), the Medisch Spectrum Twente cohort (n=242), a case series of patients with POLEmut endometrial cancer in the Leiden Endometrial Cancer Repository (n=47), and The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma cohort (n=395). PORTEC-3 was held out as an independent test set and a four-fold cross validation was performed. Performance was measured with the macro and class-wise area under the receiver operating characteristic curve (AUROC). Whole-slide images were segmented into tiles of 360 μm resized to 224 × 224 pixels. im4MEC was trained to learn tile-level morphological features with self-supervised learning and to molecularly classify whole-slide images with an attention mechanism. The top 20 tiles with the highest attention scores were reviewed to identify morpho-molecular correlates. Predictions of a nuclear classification deep learning model serve to derive interpretable morphological features. We analysed 5-year recurrence-free survival and explored prognostic refinement by molecular class using the Kaplan-Meier method. Findings: im4MEC attained macro-average AUROCs of 0·874 (95% CI 0·856–0·893) on four-fold cross-validation and 0·876 on the independent test set. The class-wise AUROCs were 0·849 for POLEmut (n=51), 0·844 for MMRd (n=134), 0·883 for NSMP (n=120), and 0·928 for p53abn (n=88). POLEmut and MMRd tiles had a high density of lymphocytes, p53abn tiles had strong nuclear atypia, and the morphology of POLEmut and MMRd endometrial cancer overlapped. im4MEC highlighted a low tumour-to-stroma ratio as a potentially novel characteristic feature of the NSMP class. 5-year recurrence-free survival was significantly different between im4MEC predicted molecular classes in PORTEC-3 (log-rank p<0·0001). The ten patients with aggressive p53abn endometrial cancer that was predicted as MMRd showed inflammatory morphology and appeared to have a better prognosis than patients with correctly predicted p53abn endometrial cancer (p=0·30). The four patients with NSMP endometrial cancer that was predicted as p53abn showed higher nuclear atypia and appeared to have a worse prognosis than patients with correctly predicted NSMP (p=0·13). Patients with MMRd endometrial cancer predicted as POLEmut had an excellent prognosis, as do those with true POLEmut endometrial cancer. Interpretation: We present the first interpretable deep learning model, im4MEC, for haematoxylin and eosin-based prediction of molecular endometrial cancer classification. im4MEC robustly identified morpho-molecular correlates and could enable further prognostic refinement of patients with endometrial cancer. Funding: The Hanarth Foundation, the Promedica Foundation, and the Swiss Federal Institutes of Technology