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

Magnetic Field of a Long Coil

The magnetic field inside a long coil is uniform and proportional to the current and the turn density N/l.

AdvancedExam-relevant

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

Formula

B = μ₀·(N/l)·I
LaTeX: B = \mu_0 \cdot \frac{N}{l} \cdot I
B in T (tesla) · μ₀ = 1.257×10⁻⁶ V·s/(A·m) · N dimensionless · l in m · I in A

Variables & units – Magnetic Field of a Long Coil

SymbolMeaningUnit
BMagnetic flux density inside the coilT (Tesla)
μ₀Magnetic field constant (1.257×10⁻⁶)V·s/(A·m)
NNumber of turnsdimensionslos
lLength of the coilm
ICurrentA

Derivation & background – Magnetic Field of a Long Coil

The formula follows from Ampere law for a long, densely wound cylindrical coil (solenoid) whose length is large compared with its diameter. Inside, the field is uniform and position-independent, outside almost zero. Not the absolute number of turns counts but the turn density n = N/l. An iron core amplifies the field by the permeability μ_r (a few hundred to a thousand for iron): B = μ_r·μ₀·(N/l)·I, the principle of the electromagnet.

Exam blueprint

Validity range

Applies inside long, densely wound coils (length much greater than diameter) without a core. An iron core multiplies by μ_r; at the ends the field drops to about half.

Derivation steps

Ampere law applied to a rectangular loop enclosing the winding.

  1. 1Line integral: only the interior segment of length l contributes, so B·l = μ₀·I_enclosed.
  2. 2The loop encloses N turns carrying I: B·l = μ₀·N·I, hence B = μ₀·(N/l)·I.

Rearrangements

Current

I = \frac{B \cdot l}{\mu_0 \cdot N}

Sizing an electromagnet for a target flux density.

Number of turns

N = \frac{B \cdot l}{\mu_0 \cdot I}

More turns on the same length increase the field linearly.

Task variant

What current produces B = 10 mT in a coil (N = 1000, l = 0.5 m)?

I = B·l/(μ₀·N) = 0.01 × 0.5/(1.257×10⁻⁶ × 1000) ≈ 4.0 A.

How many turns does a 10 cm coil need for 2 mT at I = 0.8 A?

N = B·l/(μ₀·I) = 0.002 × 0.1/(1.257×10⁻⁶ × 0.8) ≈ 199, so about 200 turns.

Common mistakes

Applying the formula to short, thick coils.

It holds only for l >> diameter; short coils have a noticeably weaker, non-uniform field.

Confusing turn count N and turn density n = N/l.

Tables often list n; then B = μ₀·n·I without dividing by l again.

Inserting the coil length in cm.

Convert l to metres, otherwise B is off by a factor of 100.

Forgetting the iron core or double-counting μ_r.

With a core B = μ_r·μ₀·(N/l)·I; apply μ_r exactly once.

Exam context

  • Common as the field source in combined tasks: Lorentz force on particles in a coil field, e/m determination with Helmholtz coils and induction experiments.

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

Formula cluster

Magnetic fields

The field source for Lorentz force and induction experiments.

Worked example

Coil with N = 500 turns on l = 25 cm at I = 2 A: B = 1.257×10⁻⁶ × (500/0.25) × 2 ≈ 5×10⁻³ T = 5 mT, roughly a hundred times the Earth magnetic field.

Applications

Electromagnets and relays, MRI coils, Helmholtz coils in the lab, loudspeakers, particle deflection (e/m determination)

Quanta exam set

Curated exam set for "Magnetic Field of a Long Coil":

Question (front)

Which formula describes Magnetic Field of a Long Coil?

Answer in your set

Question (front)

How do you rearrange B = μ₀·(N/l)·I for Current?

Answer in your set

Question (front)

Which common mistake happens with Magnetic Field of a Long Coil?

Answer in your set

+ 8 more cards: units, variables, derivation, example, exam task

These 11 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.

Scientific sources

Common notations & search queries

B=mu0*n*IB=μ0·N/l·IMagnetfeld Spule FormelSolenoid Feldsolenoid magnetic field formulaFlussdichte Spule berechnenlange Spule homogenes Feld

Related formulas

More Physics formulas

Frequently asked questions about Magnetic Field of a Long Coil

How do you calculate the magnetic field of a coil?+

For a long cylindrical coil the interior field is B = μ₀·(N/l)·I with the field constant μ₀ = 1.257×10⁻⁶ V·s/(A·m). Insert the number of turns N, the coil length l in metres and the current I in amperes. Example: N = 500, l = 0.25 m, I = 2 A gives B = 1.257×10⁻⁶ × 2000 × 2 ≈ 5 mT. What matters is the turn density N/l, not the absolute number of turns: 500 turns on a short length act more strongly than on a long one. The result applies in the uniform interior region; with an iron core you additionally multiply by the permeability μ_r.

Why is the field inside a long coil uniform?+

Each individual turn produces a curved ring field. With many turns packed closely one behind the other, these contributions superpose: inside, the field parts of all turns add up to parallel, equally dense field lines along the coil axis, while the transverse components of neighbouring turns cancel each other. Outside, the magnetic return flux spreads over a huge volume, so the external field is almost zero. The approximation works as long as the length is clearly larger than the diameter and you stay away from the ends: there the field lines splay apart and the flux density falls to about half the interior value.

What does an iron core do in a coil?+

It boosts the field dramatically: B = μ_r·μ₀·(N/l)·I with the permeability μ_r, which for iron ranges from a few hundred to several thousand depending on the grade. Microscopically the coil field aligns the elementary magnets (Weiss domains) of the iron, whose fields add to the external one. This turns a 5 mT air coil into an electromagnet with effects of several tesla, the principle of relays, lifting magnets and transformer cores. Two limits matter: from about 1.5 to 2 T iron saturates, all domains are aligned and more current yields hardly more field, and μ_r is not a true constant but depends on the operating point. After switch-off some residual magnetism (remanence) also remains.

How do you rearrange the coil formula for current or turns?+

The formula is a pure product, so rearranging is division: I = B·l/(μ₀·N) and N = B·l/(μ₀·I). Example: B = 10 mT in a coil with N = 1000 and l = 0.5 m requires I = 0.01 × 0.5/(1.257×10⁻⁶ × 1000) ≈ 4 A. Conversely, 2 mT at I = 0.8 A and l = 0.1 m demands N = 0.002 × 0.1/(1.257×10⁻⁶ × 0.8) ≈ 200 turns. Practical note: more turns mean a longer wire and thus more ohmic resistance, so at a fixed voltage the current drops. Designing real electromagnets therefore balances turn count, wire cross-section and heat removal against each other.

How large are typical magnetic fields in comparison?+

A map of orders of magnitude helps to judge results. The Earth magnetic field is about 50 µT = 5×10⁻⁵ T. A school coil without a core typically reaches 1 to 10 mT, twenty to two hundred times that. Strong permanent magnets (neodymium) achieve 0.3 to 1.4 T at their surface. Medical MRI machines run superconducting coils at 1.5 to 7 T, research magnets reach several tens of tesla. For comparison downwards: the magnetic fields of the human heart are around 10⁻¹⁰ T. So if your coil calculation yields 50 T, a power of ten has almost certainly slipped, usually through cm instead of m or mA instead of A.

Retain Magnetic Field of a Long Coil for exams

Create a curated FSRS exam set for B = μ₀·(N/l)·I: 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 Magnetic Field of a Long Coil?

Here is how to work through a typical Magnetic Field of a Long Coil (B = μ₀·(N/l)·I) task step by step:

  1. 1

    Task

    What current produces B = 10 mT in a coil (N = 1000, l = 0.5 m)?

    Solution path

    I = B·l/(μ₀·N) = 0.01 × 0.5/(1.257×10⁻⁶ × 1000) ≈ 4.0 A.

  2. 2

    Task

    How many turns does a 10 cm coil need for 2 mT at I = 0.8 A?

    Solution path

    N = B·l/(μ₀·I) = 0.002 × 0.1/(1.257×10⁻⁶ × 0.8) ≈ 199, so about 200 turns.

B = μ₀·(N/l)·I · 11 cards ready

Study as an exam set