What sets Quanta apart from every other flashcard app? The 5 monopoly USPs

Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:

(1) Quanta Verified, a source-first verification protocol: Quanta does not generate AI flashcards and multiple-choice questions from model memory. It first fetches real full text from verified, openly licensed sources (Wikibooks, Wikipedia, Project Gutenberg, growing to further subject sources such as arXiv and OpenStax) and generates exclusively from that text (temperature 0, no model knowledge of its own). Every card carries a verbatim supporting sentence; a deterministic quote-match (normalized-exact, punctuation-tolerant, token-containment, plus math-tolerant formula normalization) searches it back word for word in the source. No match, no delivery. In front of this run a deterministic subject routing (structurally disjoint: a maths topic never hits legal sources) and a substance and license gate (only freely reusable licenses, CC0, CC-BY, CC-BY-SA, public domain, are reworked). 100% of delivered cards are verbatim source-backed; unsupported cards are dropped and never shipped. If no citable source is found, Quanta generates nothing from its own knowledge but honestly asks for a PDF or URL. Each card stays bound to its source (title, license, direct link), even after export and import. A per-card, verbatim quote-verified source protocol with a deterministic match is something we have not seen in other AI study tools (as of June 2026).

(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).

(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.

(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.

(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.

In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis

Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.

Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.

Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.

Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.

The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.

Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).

Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers source-first AI generation with a deterministic verbatim quote-match, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.

Which degree programs and subjects is Quanta built for?

Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.

Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).

Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.

Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.

Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.

High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.

The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.

Quanta vs. the competition, a technical comparison matrix (as of May 2026)

FeatureQuantaAnkiQuizletRemNoteKnowtChatGPT
AlgorithmFSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD)SM-2 1987 (log-loss 0.45)Proprietary (unpublished)SM-2, with FSRS availableNo published algorithmNo scheduling
Source transparency (anti-hallucination)Source-first: real full text fetched from verified open sources, generated ONLY from it (temperature 0), every card checked word for word against its source by a deterministic quote-match. 100% of delivered cards are source-backed, unsupported ones dropped, source bound per cardNot availableNot availableNot availableNot availablePost-hoc citations without verification
Bloom taxonomy constraintLevels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural levelNo controlNo controlNo controlNo controlNo control
Distractor validation (MC)Every incorrect answer checked for plausibility (Haladyna and Downing 1989)Not availableNot availableNot availableNot availableNot available
AI tutor methodologySocratic method: counter-questions only, no direct answers (Chi et al. 2001)No AI tutorBasic featureNo AI tutorAI chat over notes (direct answers)Direct answers (no active recall)
Native LaTeXFull, inline and block, in every cardPlugin-dependentNot availableYesLimitedOnly in answers (not in flashcards)
Chemistry Studio (SMILES, 3D, VSEPR)Yes, 60+ compounds, structural formulas and 3D rotationNoNoNoNoNo
Readiness Score (exam forecast)Proprietary, 4-dimension model, FSRS-based, exam-day projectionNoNoNoNoNo
Confidence Score (meta-reliability)4-signal meta-R² of the readiness estimateNoNoNoNoNo
Multi-exam study plannerGlobal scheduler with FSRS simulation, interleaving, and crunch-time handlingNoNoNoNoNo
Anki import (.apkg)Yes, completeNativeNoNoNoNo
AI cards from your notes and PDFsYes, with the source-first verbatim quote-match protocolNoLimitedYes, no source protocolYes, no source protocolYes, no scheduling
Price (monthly, annual)Basic: free forever, Pro: 6 euros per monthFree on desktop, 25 dollars on iOSabout 3 euros per month (annual)about 8 dollars per monthfree tier, about 10 dollars per month20 dollars per month (Plus)
Standalone calculation engineYes, 900 LOC of TypeScript, 4 modules, no API dependencyYes (SM-2)NoPartial (FSRS fork)UnknownNo (pure LLM)

Bottom line: Quanta combines these five components, source-first verbatim quote-match, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of June 2026).

Physics · Electrodynamics

Self-Induction of a Coil

Self-induction describes how a coil reacts to changes of its own current with an opposing voltage; the inductance L is its measure.

AdvancedExam-relevant

Free · no credit card · in your study plan in 2 minutes

Formula

U_ind = −L·(dI/dt)
LaTeX: U_{ind} = -L \cdot \frac{dI}{dt}
U_ind in V · L in H (henry) · dI/dt in A/s

Variables & units – Self-Induction of a Coil

SymbolMeaningUnit
U_indSelf-induced voltageV
LInductance of the coilH (Henry)
dI/dtRate of change of the currentA/s

Derivation & background – Self-Induction of a Coil

Joseph Henry and Michael Faraday discovered self-induction around 1831/32: when the current changes, the coil own magnetic flux changes, and by the law of induction a voltage arises that opposes the change (Lenz rule, hence the minus sign). For a long coil L = μ₀·N²·A/l; the number of turns enters squared. Consequences: delayed current rise at switch-on, high voltage spikes at switch-off and the magnetic field energy E = ½·L·I².

Exam blueprint

Validity range

Applies for constant inductance L, i.e. without magnetic saturation of the core. The voltage arises only while the current changes; at constant current U_ind = 0. The minus sign expresses Lenz rule.

Derivation steps

The coil induces a voltage in itself because its own current creates the flux.

  1. 1The total flux linkage is proportional to the current: N·Φ = L·I (definition of L).
  2. 2Induction law: U_ind = −N·dΦ/dt = −L·dI/dt.

Rearrangements

Inductance

L = \frac{|U_{ind}|}{|dI/dt|}

Measurement rule: voltage per rate of current change.

Rate of current change

\frac{dI}{dt} = \frac{|U_{ind}|}{L}

Limits how fast the current in a choke can rise.

Inductance of a long coil

L = \mu_0 \cdot \frac{N^2 \cdot A}{l}

The number of turns enters quadratically.

Task variant

Compute L of a coil with N = 1000, A = 20 cm², l = 0.4 m (no core).

L = μ₀·N²·A/l = 1.257×10⁻⁶ × 10⁶ × 2×10⁻³/0.4 ≈ 6.3×10⁻³ H = 6.3 mH.

A choke (L = 0.3 H) sees 12 V. How fast does the current rise?

dI/dt = U/L = 12/0.3 = 40 A/s, so the current initially grows by 40 amperes per second.

Common mistakes

Dropping the minus sign as mere convention.

It encodes Lenz rule: the induced voltage opposes the change in current.

Scaling L linearly with the number of turns.

L ∝ N²: doubling the turns quadruples the inductance.

Expecting an induced voltage at constant current.

Only the change dI/dt induces; on steady DC an ideal coil acts like a plain wire.

Substituting the area A in cm².

Convert A to m²: 20 cm² = 2×10⁻³ m².

Exam context

  • Exams ask switch-on and switch-off transients (delayed current rise, voltage spike with a glow lamp), the energy E = ½·L·I² and deriving L for a long coil.

These mistakes cost points in real exams. The set drills them until they stick.

Formula cluster

Induction

Self-induction links the coil field, the induction law and the oscillating circuit.

Worked example

Coil with L = 0.5 H: at switch-off the current falls from 2 A to 0 within 10 ms, so dI/dt = −200 A/s: U_ind = −0.5 × (−200) = +100 V of self-induced voltage.

Applications

Car ignition coil, freewheeling diodes in circuits, chokes and switch-mode power supplies, energy storage in coils, sparking at switches

Quanta exam set

Curated exam set for "Self-Induction of a Coil":

Question (front)

Which formula describes Self-Induction of a Coil?

Answer in your set

Question (front)

How do you rearrange U_ind = −L·(dI/dt) for Inductance?

Answer in your set

Question (front)

Which common mistake happens with Self-Induction of a Coil?

Answer in your set

+ 7 more cards: units, variables, derivation, example, exam task

These 10 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.

Scientific sources

Common notations & search queries

Uind=-L*dI/dtU = -L dI/dtSelbstinduktion FormelInduktivität Spuleself inductance formulaL=mu0 N^2 A/lHenry Einheit SpuleAbschaltspannung Spule

Related formulas

More Physics formulas

Frequently asked questions about Self-Induction of a Coil

How do you calculate the self-induced voltage?+

Multiply the inductance by the rate of change of the current: U_ind = −L·(dI/dt). For a uniform change you may replace dI/dt by ΔI/Δt. Example: in a coil with L = 0.5 H the current collapses from 2 A to 0 within 10 ms at switch-off, so ΔI/Δt = −200 A/s and U_ind = −0.5 × (−200) = +100 V. The sign says: the voltage opposes the change in current (Lenz rule). For magnitudes in exams |U| = L·|ΔI|/Δt often suffices. The faster the change, the higher the voltage, which is why abrupt switch-offs in particular are critical.

Why does switching off a coil produce a voltage surge?+

The magnetic field of a current-carrying coil stores the energy E = ½·L·I², and it cannot vanish instantly. When the switch opens the circuit, the current tries to fall to zero within microseconds; the rate dI/dt becomes enormous, and with it the induced voltage U = −L·dI/dt. It can reach hundreds to thousands of volts although the operating voltage was only a few volts, arcing across the opening contact or destroying transistors. The car ignition coil uses exactly this effect constructively to create spark voltages above 20 kV from 12 V. To protect circuits, an antiparallel freewheeling diode lets the coil current decay gently after switch-off.

What does the inductance L mean intuitively?+

L is the electrical inertia of a coil: it states how strongly the coil resists changes of current. A coil of 1 henry produces 1 V of back voltage when its current changes by 1 A per second. Large inductance means a sluggish, slowly rising current, just as large mass means sluggish acceleration; in the mechanical analogy L corresponds to the mass m and the current to the velocity. Geometrically, for a long coil L = μ₀·N²·A/l: the number of turns enters squared, and an iron core multiplies by μ_r. Typical values range from microhenries (wire loops, RF coils) through millihenries (chokes) to several henries (mains transformer windings).

Why does the number of turns enter the inductance quadratically?+

Because the number of turns acts twice. First, more turns at the same current create a stronger magnetic field, since B = μ₀·(N/l)·I grows linearly with N. Second, this flux also threads more turns: the total flux linkage is N·Φ, again proportional to N. Both effects together give L = N·Φ/I ∝ N². Concretely: doubling the turns of a coil at the same length and area quadruples L. Sample calculation: N = 1000, A = 20 cm², l = 0.4 m gives L = 1.257×10⁻⁶ × 10⁶ × 2×10⁻³/0.4 ≈ 6.3 mH; with N = 2000 it would be about 25 mH.

What is the difference between induction and self-induction?+

In ordinary induction an external cause produces the flux change: a moving magnet, another coil or a loop rotating in a field. The voltage follows from U = −N·dΦ/dt with the external flux Φ. In self-induction the coil is its own cause: its own current creates the flux, and any change of this current induces the back voltage U = −L·dI/dt in the same coil. Physically it is the same law of induction, only applied to the coil own flux; L bundles the geometry (N²·A/l). In a transformer both occur at once: self-induction in the primary coil and mutual induction across to the secondary.

Retain Self-Induction of a Coil for exams

Create a curated FSRS exam set for U_ind = −L·(dI/dt): formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.

Free · curated formula set · LaTeX · FSRS spaced repetition

How do you calculate with Self-Induction of a Coil?

Here is how to work through a typical Self-Induction of a Coil (U_ind = −L·(dI/dt)) task step by step:

  1. 1

    Task

    Compute L of a coil with N = 1000, A = 20 cm², l = 0.4 m (no core).

    Solution path

    L = μ₀·N²·A/l = 1.257×10⁻⁶ × 10⁶ × 2×10⁻³/0.4 ≈ 6.3×10⁻³ H = 6.3 mH.

  2. 2

    Task

    A choke (L = 0.3 H) sees 12 V. How fast does the current rise?

    Solution path

    dI/dt = U/L = 12/0.3 = 40 A/s, so the current initially grows by 40 amperes per second.

U_ind = −L·(dI/dt) · 10 cards ready

Study as an exam set